LATESTHume Audio Decisions API: hosted, 2 Nov →

AI for decisions.

Decision models return structured answers, such as categories or scores. They can also return a yes/no probability. Jev returns these without generating text; some alternatives generate an option letter. People use them in games and websites, and in AI agents.

Releases

Releases since Jev launched on 15 September

TypeSafe AI released Jev and named the category. Every release below links to its model page.

  1. Jev · TypeSafe AI ·
  2. Laya · Convai Innovations ·
  3. Nimble · Bespoke Labs ·
  4. Decision 1.0 · vLLM Semantic Router ·
  5. Tev1-4B · Together AI ·
  6. Span-01 · Respan ·
  7. GLiNER2.5-Decide · Fastino ·
  8. Solar Decide · Upstage ·
  9. Ollama v0.35 · Ollama ·
  10. Decisions API · OpenAI ·
  11. d1 · Liquid AI ·
  12. Mercury Decide · Inception ·
  13. Kev · Jared Palmer ·
  14. DecisionTune 1.0 · DecisionTune ·
  15. Valen · Valen Team ·
  16. Decider · Perplexity ·
  17. H2O-Lightning-4B · H2O.ai ·
  18. KnowLine · PelaAI ·
  19. Snowflake Decision · Snowflake ·
  20. Microsoft-Decision-1 · Microsoft ·
  21. Clef-omni · Cloudflare ·
  22. Arc-decide · Arc53 (DocsGPT) ·
  23. Granite Switch 4.1 · IBM ·
  24. TYGER · NineNineSix ·
  25. Clairic · Cloudflare ·
  26. GLiDE · Fastino ·
  27. Zoom Decisions API · Zoom ·
  28. Qev 0.8B · labqoat ·
  29. Mammoth 1 · Baseten (Base Labs) ·
  30. Talos · Tractian ·
  31. Decido · decido-ai ·
  32. LingDT-Decision-1.0 · Ant Digital Technologies (蚂蚁数科) ·
  33. Fovea-4B · Sivasuthan ·
  34. Parallel Decisions API · Parallel ·
  35. homi · egma-ai ·
  36. UltraBERT Curation · Daniel van Strien ·
  37. Nimbus · Crele ·
  38. Ru-Jev · besktech (beskvladimir-create) ·
  39. Hume Audio Decisions API · Hume AI ·

How it works

What happens in one call

1

You send what your app knows

A message or an agent's history can be input. You can also send a game state or a web page.

state "My card was charged twice."
2

For Choice, you list the possible answers

For a Choice question, you supply the allowed options and receive probabilities for them.

which team?
billingtechnicalsales
3

It returns a probability for each

Use the probabilities to decide which answers to act on and which to send to a person or a bigger model.

billing.94
technical.04
sales.02
→ route("billing")

The odds above illustrate a Choice question. Jev, and the models built to its API, also take Noul questions, which return a yes/no probability; the name comes from the Bernoulli distribution. A Score question returns a value along your defined scale, which can fall between the numeric levels you supply. source.

How generative LLMs compare

A generative LLM can return structured output that your code reads. It generates its answer one token at a time. Jev returns structured answers without generating text. Together reports that Tev1 keeps a text-generation head and is trained to return one option letter. Simon Willison Together

Where the name comes from

TypeSafe calls Jev a System One model, after Daniel Kahneman's split between fast, intuitive thinking and slow, deliberate reasoning. Liquid AI uses "decision models" for d1. TypeSafe Liquid

Is it new?

The interface isn't. Scoring declared labels in one pass goes back years, from zero-shot classifiers to GLiNER. What is new is a hosted model sold for it, with calibrated probabilities as its stated training goal, at this price and speed. The lineage ↓

Use cases

What people are using decision models for

The newest builds we've found. Each card links to its use case, with a source for the example.

Mobile apps

Could executing this action cause data loss, a charge, a logout, an account or permission change, a message sent to another person, or another irreversible side effect?

yes · no

Example: jev-droid · Source ↗

Search ranking

How relevant is this result to the query?

a score on a scale

Example: Simon Willison on reranking with Jev · Source ↗

Models

Every model we know of, with sources

Hosted models, open weights you can run yourself, and the community projects around them. Tap any entry for what it is, what its maker claims, and where that claim comes from.

Jev

TypeSafe AI · 15 Sep 2026 · HOSTED

The model that named the category. A hosted API: send your app's state plus typed questions (Choice, Score or Noul) and get a probability back for each answer. It generates no text, and its weights are not public.

Price
$0.042 per million input tokens; output free · Compare prices
Latency
70–500 ms end to end (TypeSafe-reported)
Version
jev-1.13 is the stable release; jev-1.14 has been the preview build since 14 Oct 2026
Context
32,000 tokens (per OpenRouter's guide)
Access
Launched in early access; also via OpenRouter
  • 40x–200x faster than frontier LLMs on comparable tasks vendor reportedsource
  • Trained with Reinforcement Learning for Calibrated Decisions (RLCD); no technical paper published as of 2 Oct 2026 vendor reportedsource
  • Leads JevBench's capability ranking of Jev-class systems (80.0) on 1 Oct 2026 independentsource
  • No public weights: TypeSafe's own Hugging Face account listed zero public models when checked on 20 Oct 2026 vendor reportedsource
  • The 14 Oct 2026 update switched on experimental image input and opt-in long context for Jev 1.13 and 1.14 (1.13 was "multimodal all along"); bounding-box answers need 1.14 vendor reportedsource
  • Tops Artificial Analysis's Decision Index at launch with 58, ahead of GLiDE and Microsoft-Decision-1 on 54 (13 Oct 2026) independentsource

d1

Liquid AI · 29 Sep 2026 · HOSTED

Liquid AI's decision model family. The hosted d1 takes the same three question types as Jev (Noul, Choice and Score). On 7 Oct Liquid released two small open siblings you can run on a device, and on 15 Oct a bigger hosted one, d1-ultra, joined OpenRouter. On 27 Oct d1.1, the successor to d1, joined OpenRouter at the same price, with -latest aliases that follow each family's newest version. On 27 Oct Liquid announced two more open models, d1-2B (text, pictures and video) and d1-Omni-2B (adds audio), built on LFM3; their Hugging Face repositories went up on 24 Oct.

Hosted
d1.1, d1 and d1-ultra, via Liquid's API and OpenRouter
Open weights
d1-3B and d1-omni-600M (7 Oct), then d1-2B and d1-Omni-2B (announced 27 Oct), under the LFM Open License v1.0
Price
OpenRouter lists d1 and d1.1 at $0.02 and d1-ultra at $0.042 per million input tokens, output free (as of 28 Oct 2026) · Compare prices
  • Liquid says d1 is the first model to beat Jev on the Jev Decision Index, a community index hosted on Hugging Face. We could not find a d1 result in the index's public data (release 0.2.1, generated 28 Sep) vendor reportedsource
  • d1-3B answers a question in 16 ms on an NVIDIA Jetson AGX Thor vendor reportedsource

Mercury Decide

Inception · 30 Sep 2026 · HOSTED

Inception's decision model, served as a System One endpoint: send state and typed questions, get a choice, a score or a yes/no answer, each with a probability.

Price
$0.02 per million input tokens at a 50% discount, output free; a free tier remains (OpenRouter, as of 22 Oct 2026) · Compare prices
Context
66K tokens (OpenRouter)
Access
OpenRouter
  • Up to 14 decisions per second vendor reportedsource
  • The most intelligent decision model on OpenRouter, on JevBench v1.4 (the board was on v1.5.4 as of 2 Oct 2026) vendor reportedsource
  • On Vals AI's LegalBench sample, 0.869 to Jev's 0.740 accuracy; on claim verification its router stayed inside a 1% error budget where Jev's went over independentsource

Solar Decide

Upstage · 28 Sep 2026 · HOSTED

Upstage's decision endpoint, available through OpenRouter. The date is when OpenRouter announced it; we have not found an announcement from Upstage itself. A faster sibling, Solar Decide Flash, joined on 8 Oct 2026 with a 524K context.

Price
OpenRouter lists $0.05 per million input tokens, output $0 (as of 2 Oct 2026); the launch post described a limited-time 50% discount · Compare prices
Access
OpenRouter

    Span-01

    Respan · 24 Sep 2026 · HOSTED

    A one-pass classifier for agent traces. For each behaviour you define, it returns the probability that it is present, absent or not observable, without generating text. Its successor, Span-02 (15 Oct 2026), reads images too, can return a number directly, and ships as Apache-2.0 open weights.

    Price
    $0.02 per million input tokens; output free · Compare prices
    Free tier
    Span-01 Lite
    Maker
    Respan, a Keywords AI product
    • Beats GPT-6 Luna and Jev 1.13 on Respan's own behaviour-detection benchmark vendor reportedsource

    Decider 1

    meraGPT · HOSTED

    A hosted decision model served only at /v1/systemone, the request format TypeSafe's SDK speaks. Its site doesn't say who runs it or when it launched.

    Price
    $0.03 per million input tokens; output $0 · Compare prices
    Endpoint
    /v1/systemone
    • 0.768 accuracy against Jev's 0.727 on typed-decision benchmarks vendor reportedsource

    Microsoft-Decision-1

    Microsoft · 9 Oct 2026 · HOSTED

    Microsoft's decision-scoring model for routing, classification, prioritisation, verification and workflow control. Post-trained from Qwen3.5-9B, available in Microsoft Foundry and on OpenRouter.

    Price
    $0.042 per million input tokens; output free · Compare prices
    Base
    Qwen3.5-9B
    Access
    Microsoft Foundry, OpenRouter
    • Ties GLiDE for second on Artificial Analysis's Decision Index at launch, on 54 (13 Oct 2026) independentsource

    Clef-omni

    Cloudflare · 9 Oct 2026 · HOSTED

    The Clef family on Workers AI answers typed questions in one pass. Clef-omni adds audio and video, so it can decide about what is seen and heard at the same time. The same update cut Clef-flash to $0.038 per million input tokens.

    Size
    30B mixture of experts, 3B active
    Price
    $0.150 per million input tokens · Compare prices
    Access
    Cloudflare Workers AI; Clef-flash weights are on Hugging Face

      Snowflake Decision

      Snowflake · 8 Oct 2026 · PREVIEW

      A decision model you call from SQL. It classifies, scores, filters and routes rows inside Snowflake through AI_COMPLETE. A lower-latency Decision Fast, with REST access, followed on 19 Oct.

      Access
      Private preview in Cortex AI Functions
      Limit
      Up to 32 options per question
      • Scored 57.63 on a subset of the Hugging Face decision benchmark (the 32-option limit rules out the full suite) vendor reportedsource

      SubQ Snap

      Subquadratic · 12 Oct 2026 · PREVIEW

      A long-context decision model: each decision can read up to 384K tokens of text and images, aimed at agent workflows.

      Price
      $0.05 per million input tokens; output free · Compare prices
      Access
      Public preview, capacity-limited, may waitlist

        Clairic

        Cloudflare · 19 Oct 2026 · PREVIEW

        A new branch of the family: a System One extraction model. Where a decision model picks from answers you list, Clairic extracts, computes, estimates or forecasts values from unstructured input and returns them as typed answers: an invoice total, a delivery date, a repair cost, a 24-hour traffic forecast. It does this quickly, without an LLM writing prose.

        Access
        Workers AI as @cf/cloudflare/clairic; listed on OpenRouter on 30 Oct 2026 at $0.10 per million input tokens, but gone from OpenRouter's model list and its page returning 404 on 31 Oct 2026
        Weights
        Cloudflare says open weights are coming soon (as of 19 Oct 2026)
        • Higher accuracy, lower latency and lower cost than traditional LLMs at extraction vendor reportedsource

        Parallel Decisions API

        Parallel · 25 Oct 2026 · HOSTED

        A decision engine that searches the live web before it answers. You send typed questions about a company, product or policy, and one call returns typed answers with probabilities (or a refusal) plus a shared list of the web pages it used, so it can answer things a model only knows from training data cannot, such as whether a company has an open job right now.

        Access
        Hosted API, with a playground; 5,000 free requests to start
        Price
        $1 per 1,000 calls (as of 25 Oct 2026) · Compare prices
        Grounding
        Searches the live web per request; citations cover the whole call, not each answer
        Latency
        VENDOR-REPORTED: 0.56 seconds median on PWS-Decisions, measured by Parallel on 29 Oct 2026 (Pacific).
        • Parallel says it costs 10x less than a standalone model vendor reportedsource
        • Parallel reports 76.22% on its own 300-question PWS-Decisions set with live web search, against Perplexity Decider v1.2 at 50.89%, OpenAI's Decisions API at 41.67% and Jev 1.14 at 33.44%, run without web access or supplied external context. Uniform random guessing is 42.42%. LLMs with native search scored 96.56% to 97.33% at about 16.5 to 28.8 times the cost. Parallel generated these questions from its own customer topics; dataset terms prohibit redistribution. vendor reportedsource
        • The benchmark blog chart labels Baseten Elephant at 32.4% overall and 32.3% public. The repository records Baseten Mammoth at 45.0% overall and 37.0% public. vendor reportedsource

        Kev

        Jared Palmer · 1 Oct 2026 · OPEN

        Kev is Jared Palmer's family of open, Jev-compatible decision models, from 0.8B to 27B parameters. A pointer head on a Qwen base scores each option, and the API matches TypeSafe's, so their SDK works against a local Kev server. Kev 1.0, released 1 Oct 2026, pins the family's checkpoints, model cards, evaluation suites and serving code; it introduces no newly trained weights.

        Sizes
        0.8B, 4B, 9B, 27B
        Licence
        Apache-2.0
        Base
        Qwen3.5 (27B: Qwen3.8)
        Release
        Kev 1.0, 1 Oct 2026
        Stars
        9,758 on GitHub (GitHub API, 2 Nov 2026)
        Price
        $0.042 per million input tokens and $0 per million output tokens for jaredpalmer/kev-4b on OpenRouter (VENDOR-REPORTED, 2 Nov 2026) · Compare prices
        Validated context
        VENDOR-REPORTED: 65,536 tokens for Kev-27B; 8,192 for 0.8B, 4B and 9B, measured on CUAD contracts
        • Kev 1.0's breadth-v1 held-out-dataset test scored a chance-corrected index of 23.3 for 0.8B, 38.0 for 4B, 41.0 for 9B and 52.3 for 27B, against Jev's 54.0, in the maker's fp32 evaluation at shipped temperatures. No Kev trained on these datasets. vendor reportedsource
        • Kev-27B's 65,536-token validated context is the longest tested state whose CUAD accuracy stays within three percentage points of its 8k-token accuracy at the 95% lower bound. The smaller models already miss that tolerance at 16k. vendor reportedsource

        DecisionTune 1.0

        DecisionTune · 4 Oct 2026 · OPEN

        A local decision model built on ModernBERT-large: give it a state, a question and options, and it returns a probability for each option in one encoder pass. It handles choice and yes/no questions without generating text, through PyTorch, MLX or ONNX. The open weights are under Apache-2.0. The date is when the weights repository went up on Hugging Face.

        Size
        395M parameters, ModernBERT-large plus a 4 KB scoring head
        Licence
        Apache-2.0
        Context
        8,192 tokens; longer sequences are refused
        • The maker reports 29.57 on Decision Index 0.2.1 (raw 46.80) from one complete run, with a second seed scoring 29.13 (raw 45.96). These are the maker's runs, not an independent reproduction. vendor reportedsource

        Tev1-4B

        Together AI · 23 Sep 2026 · OPEN

        Together's Jev-inspired experiment: a Qwen3.5-4B fine-tune that reads a state, a question and 2 to 24 lettered options. Together says plainly it is not a non-autoregressive runtime like Jev: it keeps a standard next-token head and generates its answer as one letter.

        Base
        Qwen3.5-4B
        Licence
        Code MIT; weights licence being finalised as of 2 Oct 2026
        Run it
        Together serverless, or Ollama
        • Together reports 880/1,000 (88.0%) and 300/300 (100%) on evaluation sets used during development (VENDOR-REPORTED). vendor reportedsource
        • In jev-rsi's independent baseline test, the author measured untuned Qwen3.5-4B using Tev1's decision protocol through local Ollama at 673/1,000 (67.3%) on test and 130/300 (43.3%) on policy_transfer. independentsource

        GLiNER2.5-Decide

        Fastino · 24 Sep 2026 · OPEN

        A 340M-parameter encoder decision model from the team behind GLiNER, fine-tuned from GLiNER2. It scores the allowed answers to typed questions in one pass and is small enough to run on a CPU.

        Size
        340M parameters
        Licence
        Apache 2.0
        Latency
        167 ms p50 on a Xeon CPU, 38 ms on a V100 (Fastino-reported; batch 1, 64-token input)
        • Best average across 17 datasets on Fastino's own benchmark: 60.1% in the launch post, 60.2% on the model card as of 2 Oct 2026 vendor reportedsource

        Laya

        Convai Innovations · 18 Sep 2026 · OPEN

        A non-autoregressive decision engine on ModernBERT-large (421M), with a 322M multilingual sibling. Its author, Nandakishor Mukkunnoth, says he built the same approach a year before Jev launched.

        Size
        421M (English), 322M (multilingual)
        Licence
        Apache 2.0
        Latency
        32.8 ms on a single GPU (maker-reported)
        • 6 to 8 times faster than Jev vendor reportedsource
        • Built and open-sourced a non-autoregressive decision model a year before Jev vendor reportedsource

        Nimble

        Bespoke Labs · 18 Sep 2026 · OPEN

        A 9B decision model, LoRA-tuned from Qwen3.5-9B, with its 2,676 training examples published. One of the first two models in Ollama's decision support. Bespoke Labs followed with hosted Nimble v4 (9B) and v4-max (27B) on 13 Oct 2026.

        Size
        9B
        Licence
        Apache 2.0
        Run it
        ollama pull nimble
        • Matches 90.1% of reference labels on 324 held-out examples, against 93.2% for Jev 1.13 vendor reportedsource

        Decider

        Perplexity · 6 Oct 2026 · OPEN

        Perplexity's decision model reads text and images and returns a probability for every answer. It ships as Apache-2.0 open weights and as a hosted API. V1.1 (27B) halved the price on 6 Oct; V1.2 and a 4B Decider Flash followed on 20 Oct.

        Latest
        Decider V1.2 (27B) and Decider Flash V1.2 (4B)
        Price
        $0.02 (V1.2) and $0.01 (Flash) per million input tokens on OpenRouter, output free (as of 22 Oct 2026) · Compare prices
        Licence
        Apache-2.0
        • Decider V1.1-27B was the most accurate of seven decision models (94.7%) on 799 unpublished yes/no questions about coding-agent work and destructive shell commands, and the only one to pass SILT's registered calibration rule. Jev's result was inconclusive. So were the results for OpenAI's Decisions API and Laya (11 Oct 2026) independentsource

        Drex 2.0

        Nace.AI · 6 Oct 2026 · OPEN

        Drex 2.0 returns probabilities over typed answers for text, with open weights under Nace.AI's Open RAIL licence. Drex Max (13 Oct) adds images and screenshots, hosted only. The date is Drex 2.0's release in Nace.AI's model table.

        Size
        35B, about 3B active
        Price
        $0.06 per million input tokens, hosted · Compare prices
        Context
        State up to 131,072 tokens

          H2O-Lightning-4B

          H2O.ai · 7 Oct 2026 · OPEN

          A 4B open model built on Qwen3.5-4B that runs on unmodified vLLM. Version 1.2.2 takes up to four images per request.

          Size
          4B
          Licence
          Apache-2.0
          • Its text-only v1.1 ranked #1 of 104 open-weight systems on JevBench v1.6.1's composite, 72.5 to Jev 1.13's 71.5, read 7 Oct 2026 vendor reportedsource

          Jebadiah v2.1

          frontier-infra · 10 Oct 2026 · OPEN

          Open decision models that read option-label probabilities in a single pass with thinking switched off: you get a pick and a confidence, not an essay. GGUF and MLX builds sit next to the full weights. Released 10 Oct 2026, written up on 16 Oct.

          Sizes
          9B and 27B
          Licence
          Apache-2.0

            Lumma-Fev-27B

            FrontiersMind · 14 Oct 2026 · OPEN

            A 27B open decision model that answers in English, Hindi, Tamil and Kannada. The makers say Jev still leads on some reasoning benchmarks.

            Size
            27B, full fine-tune
            Licence
            Apache-2.0
            • 90.7% vs Jev's 83.9% on the maker's held-out decision set vendor reportedsource

            Granite Switch 4.1

            IBM · 15 Oct 2026 · OPEN

            IBM's switchable models do chat, decisions and speech-to-text from one endpoint. 4.1 adds a general-purpose Decision adapter that speaks the /v1/systemone interface.

            Sizes
            8B and 30B
            Licence
            Apache-2.0

              TYGER

              NineNineSix · 17 Oct 2026 · OPEN

              A decision model for speech. It listens to the microphone and your text-to-speech at the same time and keeps deciding whether to speak, keep listening, yield or drop in an "mm-hm", scoring audio in 80 ms frames streamed in 320 ms hops. Built for full-duplex voice agents, with an open-source agent (tyger-s2s) on top.

              Base
              NVIDIA Nemotron 3.5 streaming ASR 0.6B
              Licence
              OpenMDW-1.1
              Works with
              Any LLM and TTS (maker's claim)

                Talos

                Tractian · 22 Oct 2026 · OPEN

                A decision model that stands between AI agents and the controllers that run factories. Every Modbus or OPC UA request goes through one gate, where a small model decides in a single pass to allow it, answer from cache, defer it or deny it, and every decision is logged. The date is when the repo and weights went up.

                Size
                0.8B, built on Qwen3.5-0.8B
                Licence
                Apache-2.0
                • About 5 ms per decision on a laptop CPU vendor reportedsource

                Mammoth 1

                Baseten (Base Labs) · 21 Oct 2026 · OPEN

                Two open decision models from Base Labs, Baseten's research team: mammoth-1 (27B) and elephant-1 (9B), post-trained from Qwen3.5. They read text and images and return a probability for every option of each question, with all questions sharing one prefill of the state. Baseten launched them claiming the top spot on the community Jev Decision Index. On 29 Oct a third, smaller model joined: joey-1 (2B, built on Qwen3.5-2B), pitched as the fastest and cheapest.

                Sizes
                mammoth-1 27B (Qwen3.5-27B), elephant-1 9B (Qwen3.5-9B) and joey-1 2B (Qwen3.5-2B), 16,384-token context
                Licence
                MIT
                Price
                OpenRouter lists mammoth-1 at $0.10, elephant-1 at $0.035 and joey-1 at $0.01 per million input tokens, output free (as of 30 Oct 2026) · Compare prices
                • Baseten says mammoth-1 ranked #1 of 208 models on Jev Decision Index 0.4 with a full score of 71.24, and elephant-1 is the best decision model under 15B parameters (62.65) vendor reportedsource
                • In the index's current public data (Decision Index 0.4.1, generated 22 Oct 2026), mammoth-1 is first of 228 on both public-benchmark scores (75.34 skill, 81.19 raw) but third on the full score (68.44), behind GLiDE v1.2 (71.52) and jeffan-31b (69.75) independentsource
                • Baseten says joey-1 has a P50 latency of 20 ms, 5x faster than comparable decision models vendor reportedsource

                Qev 0.8B

                labqoat · 20 Oct 2026 · OPEN

                A tiny open decision model that runs locally, built on Qwen3.5-0.8B and reading text and images. The date is when the weights went up on Hugging Face.

                Size
                0.8B, built on Qwen3.5-0.8B-Base, about 1.3 GB of RAM on a Mac in 8-bit (maker's figure)
                Licence
                Apache-2.0
                Runs on
                llama-server (GGUF), MLX on Apple silicon, or a System One API
                • First of 27 decision models of around 1B parameters on the public split of Benchmark Heaven (77.1%), and the fastest at a median 3.5 ms per request (batch size 1, one NVIDIA L40S), in the maker's own comparison vendor reportedsource

                Decido

                decido-ai · 22 Oct 2026 · OPEN

                An open decision model built to run on a laptop CPU: a LoRA fine-tune of Gemma 4 26B-A4B shipped as an 8-bit GGUF for llama.cpp, answering in the same wire format as Jev's System One API. The date is when the weights went up.

                Size
                Gemma 4 26B-A4B (about 4B active parameters), 8-bit GGUF, about 30 GB of RAM
                Licence
                Apache-2.0
                Speed
                A median 4.1 s for a 1,000-token document and 5 questions on an Apple M4 Pro CPU (maker's figure)
                Code
                The decido-ai/decido GitHub repository was gone on 31 Oct 2026; the weights and model card remain on Hugging Face
                • Scores a six-benchmark mean of 85.38 against Jev 1.13's published 83.85, or 82.04 with the benchmarks' public training splits left out; the maker notes Jev's number is its published reference run, not Jev run through the same harness vendor reportedsource

                Arc-decide

                Arc53 (DocsGPT) · 11 Oct 2026 · OPEN

                The small decision model inside DocsGPT, used for quick judgements such as whether a message is a prompt injection or whether a chunk answers the question. It runs on CPU with ONNX Runtime. The date is when v0.2 went up on Hugging Face.

                Sizes
                arc-decide-0.4b-v0.2 for CPU; arc-decide-0.6b-v0.1 for NVIDIA GPUs with vLLM
                Licence
                MIT, built on Qwen3 (Apache-2.0)

                  Valen

                  Valen Team · 5 Oct 2026 · OPEN

                  Open decision models in three sizes that read text, images and video, built on Qwen3.5. They answer through both Jev's System One format and OpenAI's Decisions format, with a free web demo and API. The date is when the first weights went up on Hugging Face; the 4B followed on 6 October.

                  Sizes
                  0.8B, 2B and 4B, built on Qwen3.5, with ONNX and GGUF builds
                  Licence
                  Apache-2.0
                  Inputs
                  Text, images and video

                    Fovea-4B

                    Sivasuthan · 24 Oct 2026 · OPEN

                    An open decision model for images: give it a photo, document, receipt, chart or screenshot plus a typed question and it returns a probability for every allowed answer, served over Jev's System One API. The date is when the weights went up.

                    Size
                    4B, built on Qwen3.5-4B
                    Licence
                    Apache-2.0
                    • Scores 80.6 on the maker's own 2,360 held-out multiple-choice questions from public benchmarks, scored with ImageJevBench's formula, ahead of H2O-Lightning-4B (79.9), Imajev-4B (79.4) and Jev-Vision 8B (75.7); the maker notes this is not the ImageJevBench leaderboard vendor reportedsource

                    homi

                    egma-ai · 27 Oct 2026 · OPEN

                    An open decision model that listens instead of reading: give it an audio clip and a typed question (how does the speaker feel? does the pitch rise?) and it returns a probability for every option, without a transcript. It is Qwen3-Omni-30B-A3B with frozen weights and a decision head in place of the text output. The date is when the weights went up on Hugging Face.

                    Base
                    Qwen3-Omni-30B-A3B-Instruct, frozen, with a decision head and 17M parameters of LoRA
                    Licence
                    Apache-2.0
                    Inputs
                    Audio clip plus Noul, Choice or Score questions
                    • 73.9 on VocalGrad against 63.0 for Gemini 3.8 Flash, and 83.1 on MMSU against Gemini's 85.7. Cost per 10,000 MMSU questions: an estimated $0.28 of batched GPU time on one H200 at $4.54/hour, against $14.80 in Gemini API charges at promotional pricing vendor reportedsource

                    Nimbus

                    Crele · 30 Oct 2026 · OPEN

                    A decision model for live audio. You ask yes/no questions in plain English (is music playing? is someone speaking?) and it answers every 40 ms of a stream with yes, no or can't tell, real time on a laptop CPU. The date is when the weights went up on Hugging Face.

                    Size
                    About 97M parameters on the audio, plus a 0.6B text encoder (Qwen3-Embedding-0.6B) that reads each question once
                    Licence
                    Apache-2.0, code and weights
                    Inputs
                    A live audio stream plus yes/no questions; answers yes, no or cant_tell per 40 ms
                    • Says yes a median 0.24 s after a sound starts over the sounds it caught on 500 real AudioSet clips (speech 0.21 s, 97% of 181 speech onsets caught); reworded questions it never trained on scored 0.892 mean AUROC against 0.911 on its own wording vendor reportedsource

                    GLiDE

                    Fastino · 19 Oct 2026 · OPEN

                    Fastino's big decision model, built on Qwen3.8-27B: give it text, JSON or images plus typed questions and it returns a probability for every option, with nothing generated. The weights are on Hugging Face under Fastino's own community licence, and Fastino also hosts it as an API. The date is when the weights repository went up; the version scored on the index is 1.2.

                    Base
                    Qwen3.8-27B; 52.2 GB of bf16 weights, needs one 80 GB GPU
                    Licence
                    Fastino community licence (not a standard open-source licence)
                    Access
                    Weights on Hugging Face, or Fastino's hosted System One API
                    • Fastino says GLiDE 1.2 is #1 of 229 models on Decision Index 0.4.1 (as of 22 Oct 2026) vendor reportedsource
                    • In the index's public data (release 0.4.1, generated 22 Oct 2026), GLiDE 1.2 is first of 228 entries on the full score (71.52), ahead of jeffan-31b (69.75) and mammoth-1 (68.44); mammoth-1 leads both public-benchmark scores. In the index's 29 Oct data, GLiDE is second of 247, behind KnowLine 31B Gen1 (71.86) independentsource

                    UltraBERT Curation

                    Daniel van Strien · 28 Oct 2026 · OPEN

                    A 380M-parameter decision model for data curation: give it a web page, prompt, maths problem or library record and typed questions (topic, format, educational value, difficulty) and it returns a probability for every option. The release centres on its recipe: the data-building and training scripts are public, with source and labeller/licence metadata where known, and 516,964 of the 535,730 training rows are released (web text omitted).

                    Size
                    380M parameters, UltraBERT-380M encoder plus a GLiClass scoring head
                    Licence
                    Apache-2.0
                    Open data
                    516,964 of 535,730 training rows released, plus 1,020,000 FineWeb pages scored with it

                      KnowLine

                      PelaAI · 7 Oct 2026 · OPEN

                      A fast-moving family of open decision models from PelaAI, trained in an agent-driven data loop, with English and Chinese in mind: from 0.8B up to 35B-A3B, with new generations posted every few days. The newest big one, KnowLine-31B-Gen1 on a Gemma 4 base, went up on 23 Oct. The date is the first family release on Hugging Face (4B-Gen1).

                      Sizes
                      4B through Gen5.5; 26B-A4B through Gen2.5; 35B-A3B through Gen1.5; 0.8B, 2B, 9B and 31B (Gemma 4 base) at Gen1; community GGUF builds
                      Licence
                      Apache-2.0
                      Languages
                      English and Chinese
                      • In the Decision Index's 29 Oct 2026 data, KnowLine 31B Gen1 is first of 247 on the full score (71.86), ahead of GLiDE 1.2 (71.52) independentsource
                      • PelaAI reports 0.4% of answers changed by an instruction planted in the state, 5.3% with injection templates unseen in training vendor reportedsource

                      Ollama v0.35

                      Ollama · 28 Sep 2026 · RUNTIME

                      Ollama added native decision models through /v1/systemone, based on TypeSafe's Jev API, so you can run one on your own machine. It shipped with Nimble and Tev1.

                      Endpoint
                      /v1/systemone
                      Question types
                      choice, noul, score
                      Launch models
                      Nimble, Tev1

                        SemIf (formerly OpenJev)

                        TheoLeeCJ · COMMUNITY

                        Reproduces Jev's interface pattern with open models: it reads typed option probabilities directly from a model, with no answer sentence and no decoding loop. Runs in your browser, or at home on a GPU, Apple Silicon or CPU. Independent of TypeSafe, and it does not reproduce Jev's model or training.

                        Availability
                        Runs in the browser
                        Models
                        Qwen3 0.6B, MiniCPM5 2B or Qwen3.5 4B, as pinned GGUF builds through wllama
                        • Checked against its own README on 2 Oct 2026 code verifiedsource

                        bnsd55/jevmlx

                        bnsd55 · COMMUNITY

                        Parallel constrained decisions for local models through mlx-lm on Apple Silicon: the schema is a set of typed fields, the context is prefilled once and broadcast across one batch row per field. MIT licensed. Large answer sets may be chunked rather than done in a single pass.

                        Availability
                        Open source
                        • Checked against its own code and README on 18 Sep 2026 code verifiedsource

                        kw2828/OpenJev

                        kw2828 · COMMUNITY

                        A browser and local playground for decision experiments on memory, uncertainty and control, using Qwen-family models and returning option probabilities. MIT licensed. Its README states it is independent of Jev, of openjev.com and of zhihz/openjev.

                        Availability
                        Open source
                        • Checked against its own code and README on 18 Sep 2026 code verifiedsource

                        akash-kamat/system-one-gemma

                        akash-kamat · COMMUNITY

                        Gemma 3 270M with a scoring head, producing a decision in a single forward pass with no text generation. Code and adapter weights are publicly readable, but the repository carries no licence, so it is not open source in the legal sense. The Gemma base weights remain under their own separate terms.

                        Availability
                        Source available; no licence found as of 18 Sep 2026
                        Model
                        270M (Gemma 3 backbone)
                        • Checked against its own code and README on 18 Sep 2026 code verifiedsource

                        leofmarciano/groot

                        leofmarciano · COMMUNITY

                        Groot 1 S, a 149M-parameter open typed-decision model that speaks the System One request format and reads states of up to 64k tokens in the maker's tests. Its forward pass also ships in the Bend language.

                        Size
                        149.3M
                        Licence
                        Apache-2.0
                        • 75.2% vs Jev's 73.7% on the full 2,000-question test split of the public LocalLLaMA/typed-decisions set, run by the maker, with a Brier score under half of Jev's vendor reportedsource

                        jwuthri-cloud/SelfJev

                        jwuthri-cloud · COMMUNITY

                        A self-hosted decision model with a Jev-compatible API: you run the server on your own GPU, and a 230 MB vision adapter on Qwen3.5-4B reads text and images. Python SDK, server and fine-tuning in one package.

                        Base
                        Qwen3.5-4B
                        Licence
                        Apache-2.0 (code)

                          prismhq/jev-router

                          prismhq · TOOLING

                          A LiteLLM-based router with an optional Jev decision backend: when a TypeSafe API key is configured it can use Jev to pick a model per request, and otherwise falls back to its own rules-based decider. MIT licensed.

                          Availability
                          Open source
                          • Checked against its own code and README on 18 Sep 2026 code verifiedsource

                          mizorewww/laya-mlx

                          mizorewww · TOOLING

                          Runs the open Laya decision models natively on Apple Silicon with MLX: no PyTorch, no cloud API, zero output tokens. Its demo plays Snake, calling Laya for every move with a safety layer that can override unsafe ones. Apache-2.0, and more starred than any project on our list except browser-use's jev-ultrafast.

                          Stars
                          6,994 on GitHub (GitHub API, 22 Oct 2026)
                          Licence
                          Apache-2.0
                          • 13.4 ms median for one short English typed decision, 7.4 ms with the multilingual checkpoint, on the maintainer's M3 Max vendor reportedsource

                          Hume Audio Decisions API

                          Hume AI · 2 Nov 2026 · HOSTED

                          Hume's hosted model answers questions about speech and sound with probabilities. Send one MP3 or WAV recording, 5 seconds to 5 minutes long, with questions in plain language. It supports yes/no, choice, score and time-span answers, and returns no transcript. Hume says the probabilities estimate how human raters would respond.

                          Access
                          Playground; request API access through Hume's waitlist
                          Inputs
                          One MP3 or WAV per request, 5 seconds to 5 minutes; the model hears 16 kHz mono audio
                          Price
                          $0.47 per million input tokens (audio, questions and context); answers not billed. Hume's billing docs, checked 03 Nov 2026 · Compare prices
                          SDKs
                          Python hume-audio-decisions and TypeScript @humeai/audio-decisions; Python SDK code MIT
                          • Hume reports 70.7% agreement with a human panel's average judgment, against 56.5% for an individual-rater baseline, in its initial evaluation. vendor reportedsource
                          • On Hume's product-page chart, its model averages 0.82 across 10 benchmarks at 0.23 seconds median latency per screen. The chart gives Gemini 3.8 Flash 78%, Qwen3.8-Omni-Flash 76% and Inkling-Small 67%; it names MMAU, ParaPair and EmphAssess among the benchmarks. vendor reportedsource

                          Zoom Decisions API

                          Zoom · 19 Oct 2026 · HOSTED

                          Zoom's hosted Decisions API and Decisions API Pro accept text, images or both. Define several questions about the same input and get structured answers in one call for condition checks, option selection and scoring.

                          Models
                          Decisions API and Decisions API Pro
                          Price
                          $0.042 per million tokens for Decisions API; $0.10 for Pro, per Zoom's product page checked 03 Nov 2026 · Compare prices
                          Endpoint
                          POST /v2/aiservices/decisions/decide
                          Access
                          Zoom AI Services portal and playground; sign-up or login required
                          • Zoom reports 83.9% overall accuracy for Decisions API Pro on a comparison it says uses JevBench and ImajevBench. Its product page says Pro leads the tested models but gives no dated per-model results table. vendor reportedsource

                          LingDT-Decision-1.0

                          Ant Digital Technologies (蚂蚁数科) · 22 Oct 2026 · HOSTED

                          Ant Digital Technologies' System One model for choosing answers to business questions, including native multi-select and decisions ordered by dependencies. IT之家 reported its 22 Oct 2026 launch in 4B and 27B versions for customer service, risk and finance. Ant's current DTMaaS listing describes the lingdt-decision-1.0 endpoint as a 4B test model.

                          Sizes
                          4B and 27B announced, per IT之家; the current DTMaaS endpoint identifies itself as 4B
                          Try it
                          Open the DTMaaS model page and its API guide. Use an API key with POST maas-api.antdigital.com/v1/systemone and model lingdt-decision-1.0; send state, questions and optional DAG dependencies
                          Limits
                          Ant lists 32 questions per request, 255 options, DAG depth 3 and a default 60 requests per minute. Its English and Chinese descriptions disagree on whether the 16K context limit applies per question or per request.
                          Price
                          Ant lists the 4B test endpoint at ¥0 per million input and output tokens during a free-trial period. Its pricing data also records an original input price of ¥0.30 per million tokens. Checked 03 Nov 2026; no trial end date was established. · Compare prices
                          Compare
                          Jev and Microsoft-Decision-1 are hosted alternatives, each listed at $0.042 per million input tokens. LingDT's free-trial price covers a 4B test endpoint; these rates do not compare accuracy
                          • Across 23 open benchmarks, Ant says LingDT-Decision-1.0 beats Microsoft-Decision-1 and Jev 1.13, with the 27B version most accurate in its size class. These are Ant's results as reported by IT之家, not an independent comparison. vendor reportedsource
                          • IT之家 reports Ant's claim of over 90% fewer tokens and up to 10x faster responses than LLMs in its customer-service, risk and finance tests. vendor reportedsource

                          Ru-Jev

                          besktech (beskvladimir-create) · 1 Nov 2026 · OPEN

                          A Russian-first typed-decision model built on mmBERT-base (307M), with a separate scoring head. It runs on CPU and supports yes/no, choice and ordinal scales. The maker says it is unaffiliated with TypeSafe AI and was not trained on Jev outputs. Hugging Face now documents v1.1; the GitHub README still reports v1 results.

                          Size
                          307M (mmBERT-base)
                          Licence
                          Code MIT; weights tagged MIT on Hugging Face
                          Run it
                          Load the backbone, head.pt and calibration.json using the Hugging Face card's Python example; the standard inference widget cannot load the separate head
                          Release date
                          First Hugging Face repository creation: 01 Nov 2026; v1.1 card checked 03 Nov 2026
                          • For v1.1, the maker reports 0.825 micro accuracy and 0.0200 ECE on 13,373 decisions, half the validation split, with temperature fitted on the other half. Its 0.0159 ECE fitted and scored on the same data is in-sample. vendor reportedsource
                          • The v1.1 card reports full-validation accuracy of 0.965 on 60-intent routing and 0.954 on 18-scenario routing, but 0.522 on reasoning questions and 0.450 on three-way NLI. Category wording also changes routing accuracy. vendor reportedsource

                          Claims are marked vendor-reported until someone independent reproduces them. Inclusion is not endorsement, and nobody pays to be listed. Something missing or wrong? if@decisionmodels.ai

                          Projects

                          Public projects built on decision models

                          Each one opened and checked against its own repo, with star counts as of the day each was added. Jev, released on 15 September 2026, is behind 77 of the 114 projects; the other 37 run on twenty-two different models, or models you load locally. Checked 03 Nov 2026, GitHub search returned 11,206 public repositories matching "jev" created from 15 to 30 September, and 27 created from 11 to 14 September.

                          Ultra-fast browser agent

                          Source: browser-use/jev-ultrafast ↗

                          Zurich to London on Google Flights in 7.1 seconds, including model calls, generated text, browser work and loading waits. Swapping to a decision model took median task time from 9.450s to 7.092s and median browser protocol calls from 1,092 to 101.

                          One natural-language goal, on the real web, at 1x speed. Maker-reported: three repeats of one task on one browser profile.

                          • 7.09 smedian task, down from 9.45 s
                          • 101browser calls, down from 1,092

                          browser-use · ★ 23,396 · MIT · Jev

                          • browser
                          • agent

                          Figures from the project's own README and docs/performance.md. The maintainers note this is three repeats of one task on one browser profile, not a general benchmark.

                          Source: GitHub → Source: Measurements →

                          fastbrowse: a browser agent where Jev picks each action

                          Source: agent-labs-dev/fastbrowse ↗

                          An LLM plans, reads and writes; Jev picks each action from the controls indexed on the page, and code stops irreversible clicks without authorization. On 46 tasks run three times each against the hosted Browser Use agent, it passed 99.2% of runs to 96.3% and cost $0.0296 a run to $0.3180, 10.7x less, but was slower: 18.9 s a task to 15.3 s (published campaign release-0.7.1).

                          agent-labs-dev · ★ 155 · MIT · Jev

                          • browser
                          • agent

                          Visual inspection workbench with Laya

                          Source: uu889/laya-opencv ↗

                          A local inspection workbench: OpenCV measures the image, hard rules decide anything numeric, and a Laya decision model cross-checks the result. Built for fruit picking, weed and pest monitoring, and steel pipe and textile checks. Version 3.0 also trains your own Laya-format decision models.

                          uu889 · ★ 58 · MIT · Laya

                          • inspection
                          • vision

                          laya-guard: review shell commands before they run

                          Source: scs0209/laya-guard ↗

                          Checks coding-agent tool calls with rules and a local multilingual Laya fine-tune, returning allow, review or block. VENDOR-REPORTED, base to fine-tune: the model alone held 32 then 39 of 43 attacks and allowed 25 then 44 of 50 everyday commands. Six end-to-end verdicts improved and two worsened, missing a preset no-regression criterion. Accuracy on 162 held-out training rows rose from 0.611 to 0.852. The author inspected the benchmark and evaluation sets between training runs, so they are less independent than a fresh test.

                          scs0209 · ★ 35 · MIT · Laya

                          • guardrails
                          • shell
                          • local

                          Source: Fine-tune and evaluation →

                          Nouli: multi-turn sessions for decision models

                          Source: nouli-ai/nouli ↗

                          Decision models forget everything after each answer; Nouli keeps a session. Type facts as you learn them and start a line with ? to ask a new question; earlier facts and answers stay in the history. A Rust CLI and library that can chain sessions into decision graphs, with Jev through its TypeSafe adapter or other providers.

                          nouli-ai · ★ 12 · MIT · Jev

                          • cli
                          • rust

                          Guardrails for coding agents' bash commands

                          Source: berget-ai/guardrails-md ↗

                          A small decision model scores every bash command against your GUARDRAILS.md in about 100 ms (maker's figure), before an opencode, pi or Claude Code agent runs it. Commands scoring above the threshold for data loss, secret leaks or rule breaks are blocked; the README warns it can still let risky ones through.

                          Berget AI · ★ 11 · MIT · berget/bev (System One-compatible)

                          • security
                          • agent

                          Jev as a second pair of eyes for Claude Code

                          Source: Civalski/claujev ↗

                          A Claude Code plugin that asks Jev short typed questions while Claude works: how big is this request, is this command destructive, was the work really done. It suggests an effort level per request, and in enforce mode a confident check can block a "done!" when the work looks incomplete.

                          Civalski · ★ 4 · MIT · Jev

                          • coding
                          • agent

                          GitHub issues filtered in plain words

                          Source: BerriAI/better-backlog ↗

                          A local web page and CLI that reads a repo's GitHub issues and lets you filter them by typing a criterion, such as people blocked from upgrading. Each criterion is asked of a decision model through your TypeSafe key or a gateway; add another to narrow the list.

                          BerriAI · ★ 2 · MIT · Jev

                          • tooling
                          • web

                          Unorphan: loose files filed into their projects

                          Source: sushrutkhandelwal/unorphan ↗

                          A local app you point at a messy folder like Downloads: it works out which of your projects each loose file belongs to, reading the README or CLAUDE.md in your code folders, and files it into a matching folder under your chosen output location. Nothing moves until you press Move, and one click puts it all back.

                          sushrutkhandelwal · ★ 2 · no licence · Jev

                          • desktop
                          • files

                          On-device email triage

                          Source: patrikcze/mail-triage ↗

                          Classifies Apple Mail messages as for you, to review or junk with a Laya model running on your own computer, and shows a report of what it would do. Mail never goes to an external AI service, and it never deletes anything.

                          patrikcze · ★ 1 · Apache-2.0 · Laya

                          • email
                          • local

                          PathJev: decisions on pathology slides

                          Source: CosmoIknosLab/PathJev ↗

                          A decision model for histopathology: given a tissue image, a question and candidate answers chosen at run time, including 'none of these', it returns a probability for each in one forward pass. Built on MedGemma-1.5-4B with LoRA adapters and a decision head, with code to rebuild every number in the authors' preprint.

                          Cristian Tommasino and Antonio M. Rinaldi · ★ 1 · Apache-2.0 (code); weights for non-commercial research only (MedGemma HAI-DEF terms) · PathJev-P (own, MedGemma-1.5-4B base)

                          • medicine
                          • vision

                          MedJev: zero-shot medical decisions

                          Source: Simonlee711/MedJev ↗

                          Runs an open System One model locally on Apple silicon against ten medical benchmarks, from USMLE-style questions to emergency-department admission and 30-day readmission, and compares it with zero-shot LLMs and supervised baselines.

                          Simonlee711 · ★ 1 · MIT (code); model weights CC BY-NC 4.0 · OpenJev Flash 9B

                          • medicine
                          • benchmark
                          • local

                          Ants that draw your picture

                          Source: scalefreegan/jev-ants ↗

                          Each ant is a Jev agent that sees the cells around it and its neighbours' headings and decides which way to move and whether to settle. A thousand small decisions turn a random scatter into a word or an uploaded picture; the maker puts a word at about fifteen seconds and seven cents.

                          scalefreegan · ★ 1 · MIT · Jev

                          • art
                          • swarm

                          X spam blocker

                          Source: bit-cook/Jev-X-Blocker ↗

                          A Chrome extension that hides spam and noise in X posts and replies by combining Jev's probabilities for rules you write in plain language with local signals, and decides locally what to hide. Everything hidden can be restored in place, and if the API fails, posts are shown.

                          bit-cook · ★ 1 · MIT · Jev

                          • browser
                          • extension

                          UI tests written in plain English

                          Source: andletenkov/decisium ↗

                          Write test steps as plain-English Gherkin and Jev picks the element, action and check for each one from options Decisium reads off the page; code, not the model, decides whether a step passed. Resolved steps are recorded so CI can replay them with no model calls.

                          andletenkov · ★ 1 · MIT · Jev

                          • testing
                          • playwright

                          RealStop: catch work handed back by a coding agent

                          Source: decision-tune/realstop ↗

                          A Claude Code Stop hook with a local fine-tune of DecisionTune 1.0 that can block a turn ending that hands work back to the user. VENDOR-REPORTED on 864 test turn ends: the moderate setting caught 67.5% (195 of 289) of hand-backs at 15.8% false interventions, against 56.7% and 14.6% for phrase rules. That test split guided development. Both members were correct in only 16% (37 of 229) of synthetic pairs; the model did not beat phrase rules there. Warm model latency was a median 27 ms on an M5 Pro. The hook cannot verify claimed task completion.

                          decision-tune · ★ 1 · Apache-2.0 · DecisionTune 1.0 (RealStop fine-tune)

                          • coding
                          • guardrails
                          • local

                          Source: Model and evaluation →

                          Sentence to typed query plan

                          Source: saembit/jevquery ↗

                          A TypeScript library where Jev picks the table, columns, filter, order and limit from what the schema offers, so it can choose a column but never write a query. Drizzle, Prisma or a SQL builder turns the plan into a real, parameterised query.

                          saembit · ★ 0 · MIT · Jev

                          • sql
                          • typescript

                          Pet shop catalogue with evidence

                          Source: jeromtom/petsona-catalogue-ai ↗

                          Tags each pet product's species, life stage and allergens from the store's own text, asking Jev or Laya one typed question per value: supported, contradicted, unknown or conflicting, with the source sentence attached. Merchants can approve, correct and lock tags, or let a validated policy accept them automatically. A hackathon entry for AIBoomi Startup Weekend Kochi, 23-24 Oct.

                          Jerom Palimattom Tom (Rexin Dynamics) · ★ 0 · MIT · Jev, Laya

                          • e-commerce
                          • shopify

                          Politicians by Jev

                          Source: n8peace/politiciansbyjev ↗

                          Ask a question and Jev, via Vercel AI Gateway, gives each of 100 well-known American politicians a yes/no probability; the faces above the threshold float up out of a physics pile.

                          n8peace · ★ 0 · MIT (code); portraits under their own licences · Jev

                          • fun
                          • web

                          Live interview checklist

                          Source: ByronDarlison/call-coach ↗

                          A macOS app that transcribes both sides of a call on your Mac with Whisper while Jev ticks off each of your questions once it has been answered, notes which question you just asked, and flags surprising answers for a follow-up. The maker puts Jev's cost at about 1 to 2 US cents for a 45-minute interview.

                          Darlison Holdings · ★ 0 · MIT · Jev

                          • meetings
                          • mcp
                          • local

                          Voice tutor grounded in a textbook

                          Source: balajibankapur/textbook-voice-tutor ↗

                          Answers a seven-year-old's spoken questions from one scanned Grade 2 textbook chapter. Jev decides whether each new question can be answered from the chapter and, with the optional verifier on, whether the drafted answer's claims are supported by the retrieved passages; common questions are matched to cached answers. On the maker's 14-question test, the decisions API cut average answer time from 4.1 to 1.7 seconds and the cost of each gate call about 25 times, against the chat wrapper.

                          balajibankapur · ★ 0 · MIT · Jev

                          • education
                          • voice

                          Talk to your Mac in one sentence

                          Source: manikv723-jpg/JevVoice ↗

                          Speak several instructions in one run-on sentence and Jev picks each action from a menu of skills, and any app from those installed on your Mac, while you are still talking. Anything outside the menu goes to Claude for a proposed shell command, which waits for your approval unless you turn on trust mode.

                          manikv723-jpg · ★ 0 · no licence · Jev

                          • voice
                          • macos

                          A village run on Jev

                          Source: Dasge97/the-village ↗

                          A pixel-art village in the browser where Jev, via OpenRouter, chooses what each adult does for the next two hours, while fixed rules handle urgent needs, night-time sleep and children. The maker built it to test whether Jev's choices stay sensible and reflect each villager's personality.

                          Dasge97 · ★ 0 · MIT (code); CC0 1.0 (graphics and sounds) · Jev

                          • simulation
                          • game

                          PII discovery in plain SQL

                          Source: senthilsweb/jev-pii ↗

                          A DuckDB function that asks Jev typed questions about each database column: is it personal data, which kind, and whether GDPR, CCPA-CPRA, HIPAA, LGPD, PIPEDA or DPDP apply. Whether to keep, mask, hash or drop it is decided by code, not the model, and a failed call sends the column to review. Also runs in the browser.

                          senthilsweb · ★ 0 · Apache-2.0 · Jev

                          • data
                          • duckdb
                          • compliance

                          Search evaluation with Jev as judge

                          Source: rickcrawford/jev-relevancy ↗

                          Grades search results with Jev on a four-level scale, checked first against Amazon's human-graded shopping queries, and stores runs, results and judgments in DuckDB with the metrics computed in SQL. The author recommends checking it against at least 300 of your own labelled pairs before trusting it on another domain.

                          rickcrawford · ★ 0 · MIT · Jev

                          • search
                          • evaluation

                          decisions, an R package and web app

                          Source: fmegahed/decisions-space ↗

                          A Hugging Face app built on the R package decisions: upload a set of images or texts, ask Jev one question about each, and get every answer with its probabilities plus the R code to repeat the run. Visitors need no key or account; runs are checked against estimated session and daily budgets.

                          fmegahed · ★ 0 · MIT · Jev

                          • r
                          • images

                          Android notification triage

                          Source: KoheiTakada/QuietFlow ↗

                          An Android app that can use Jev to sort notifications into five classes, from action required to hide, alongside local safety rules and corrections it learns from you. It starts in observation mode and never cancels notifications there; Jev is opt-in with your own key.

                          KoheiTakada · ★ 0 · MIT · Jev

                          • android
                          • notifications

                          West Philippine Sea Watch

                          Source: gianviterbo/wps-watch ↗

                          A public monitor of the West Philippine Sea with live ship positions, Sentinel-1 radar passes and Global Fishing Watch events. Jev answers four questions about each item from 13 news feeds, and two of them, whether it serves a Philippine reader and whether it carries PRC state framing, decide whether it is shown or held back. Live at wps.gdgt.link.

                          gianviterbo (Gadget Pilipinas) · ★ 0 · Proprietary (contact Gadget Pilipinas for reuse) · Jev

                          • news
                          • maps
                          • monitoring

                          Tetris Arena

                          Source: arndth/tetrisarena ↗

                          Jev, SAP-RPT-1, LLMs and a 15-line heuristic play Tetris on the same pieces and candidate moves, with live cost tracking. In the maker's 50-piece race Jev and the heuristic both cleared 19 lines and the others 18, a tie within noise; the difference was speed and cost.

                          arndth · ★ 0 · MIT · Jev

                          • game
                          • comparison

                          Decision models as tool routers, measured

                          Source: Daewooki/decision-agent-lab ↗

                          Calls Jev, OpenAI's Decisions API, Upstage Solar Decide and local Ollama models through one interface and logs version, latency, tokens and cost. On picking the right one of 12 tools for 40 Korean and English requests, the maker measured Jev 1.13 at 40 of 40 and OpenAI Decisions at 0.90, against 0.85 for a generative model choosing directly.

                          Daewooki · ★ 0 · MIT · Jev, Decisions API, Solar Decide, Nimble

                          • agent
                          • benchmark

                          Terms of service checker

                          Source: shohei81/jev-tos-checker ↗

                          A Chrome extension that splits the terms of service on the page into clauses and has Jev check each for eight kinds of one-sided term, from selling your data to forced arbitration, marking only clauses that go well beyond what major services do and showing each next to the original text. The maker says it is not legal advice.

                          shohei81 · ★ 0 · MIT · Jev

                          • browser
                          • extension
                          • legal

                          CI failure triage

                          Source: tejred213/ci-triage ↗

                          Jev reads each failed test and decides the cause: environment or timeout, flaky assertion, genuine regression or API contract mismatch. Code re-runs the build only when every failure would be fixed by re-running (three attempts in all by default), gives flaky tests a low-priority ticket as well, and files tickets for real failures with the test's owner.

                          tejred213 · ★ 0 · no licence · Jev

                          • ci
                          • devops

                          Which fixes clear most Intune noncompliance

                          Source: dallascrilley/intune-why ↗

                          Reads Intune compliance data from Microsoft Graph, groups failing devices by the setting that fails, and ranks fixes by how many devices each makes fully compliant. For custom settings its rules don't cover, Jev can optionally pick the remediation path. You can try a live demo with a sample tenant.

                          dallascrilley · ★ 0 · MIT · Jev

                          • it admin
                          • microsoft

                          Classified ads filter

                          Source: Aladincz/Jev-Content-Filter ↗

                          A browser extension that asks Jev about every ad on the Bazoš classifieds site against criteria you write in plain language, and collapses the ones that don't match. Other marketplaces and auction sites are planned.

                          Aladincz · ★ 0 · MIT · Jev

                          • browser
                          • extension
                          • shopping

                          Agent questions answered by Jev

                          Source: jflowers/opencode-auto-question ↗

                          An opencode plugin for autonomous runs: when the agent stops to ask the human a question, Jev (or an LLM, if you set one; the default tries Jev first) picks from the offered options. Below a configurable confidence (0.6 by default) it gathers context in a side session and decides again; below 0.35 the human answers after all.

                          jflowers · ★ 0 · Apache-2.0 · Jev

                          • agent
                          • coding

                          Drex inside DuckDB and other SQL systems

                          Source: nace-ai/drex-duckdb ↗

                          Nace's DuckDB extension asks typed questions about rows from SQL and returns probabilities as column values, for routing support tickets, classifying documents or scoring data you already query. Nace also publishes MIT-licensed connectors for Snowflake, PostgreSQL, ClickHouse and Apache Spark, including Databricks SQL. Text and JSON calls default to drex-v2.0; images need an image-capable model such as drex-max.

                          Nace AI · ★ 0 · MIT · Drex 2.0, Drex Max

                          • sql
                          • database

                          Source: Snowflake → Source: PostgreSQL → Source: ClickHouse → Source: Apache Spark and Databricks SQL →

                          Cameo: put yourself in any video

                          Source: kristopolous/comfyui-cameo ↗

                          A ComfyUI workflow that adds you to an existing video. Liquid's d1-2B judges every shot (framing, whether there's room for one more person, where everyone stands) and casts the best ones, then image and video models put you in beside the people already there.

                          kristopolous · ★ 0 · MIT · d1

                          • video
                          • comfyui

                          Kariyer decision bench: Turkish job ads

                          Source: kubrakrcn/kariyer-decision-bench ↗

                          Written at Kariyer.net R&D to pull seniority, department and work type out of job ads. On 600 real ads with 7 questions each (4,200 decisions), Jev 1.13.0 scored 71.5% against 64.6% for the open tau-4b and 65.7% for a prompted 4B LLM. tau-4b was better calibrated (ECE 0.093 against 0.156), and it scored 95.7% on its own training data, which is why the author warns against benchmarking a model on the set it shipped with.

                          kubrakrcn · ★ 0 · Apache-2.0 · Jev, tau-4b

                          • benchmark
                          • turkish

                          Narrative audit: earnings calls against the numbers

                          Source: adityavsb/narrative-audit-jev ↗

                          A harness that checks claims from earnings-call transcripts and footnotes against tables in DuckDB, on 64 synthetic cases. Perplexity's pplx-decider-flash-v1.2 scored 85.94% at about 1.4 s a case; a rule baseline scored 68.75% but caught only 2 of 9 Q&A deflections, which the decider caught 9 of 9. A chat model led on accuracy at 90.62%, with confidences stacked at 0.99 to 1.00. OpenRouter's chat endpoint rejected the decider with HTTP 400, so it runs through the separate /api/alpha/decisions endpoint.

                          adityavsb · ★ 0 · MIT · Decider

                          • benchmark
                          • finance

                          layagrep: train Laya to search code

                          Source: gak4u/layagrep ↗

                          jevgrep finds code by asking Jev thousands of yes/no questions. layagrep records those questions on SWE-bench tasks and fine-tunes the open Laya to answer them locally, with no API key and no code leaving the machine. A smoke run on 400 held-out questions lifted file-navigation AUC from 0.79 to 0.94 (Jev 0.97), but declaration relevance stayed at 0.58 (Jev 1.00).

                          gak4u · ★ 0 · Apache-2.0 · Laya, Jev

                          • code-search
                          • fine-tuning

                          decision-cli: local LLMs as decision models on a Mac

                          Source: cafe3310/decision-cli ↗

                          Reads the probabilities of the answer tokens from one forward pass of an ordinary local LLM, with no fine-tuning, and serves them through a CLI, batch mode and a Jev-like API on Apple Silicon. On the 231 public JevBench questions, gemma-4-26b-a4b scored 84.8% at about 170 ms a question and ling-3.0-tiny 69.7% at about 45 ms; most of the misses are on the hard set.

                          cafe3310 · ★ 0 · Apache-2.0 · Local MLX models

                          • cli
                          • mlx
                          • local

                          desktop-agent: Windows apps run in the background

                          Source: elidot007/desktop-agent ↗

                          Lets any agent operate Windows desktop apps without taking over the screen. A local 0.33B decision model, S1-Fast, reads the app's accessibility element table and does the sure steps. Uncertain steps go back to the agent or to a fine-tuned OneJev-4B, and irreversible ones stop until a person confirms them. It reports 68.4% macro step success on five public held-out test sets and 40 of 40 on its own development tasks.

                          elidot007 · ★ 0 · Apache-2.0 · S1-Fast (own 0.33B), OneJev-4B

                          • desktop
                          • agent
                          • windows

                          Decision model kernels: a faster llama.cpp on one GPU

                          Source: WyrmKeep/decision-model-kernels ↗

                          A lab notebook for speeding up one decision model, H2O Lightning 4B, served through llama.cpp on an RTX 3060 Ti. Each request takes 2.13 to 2.45 times its roofline floor. The first measured change, a llama.cpp pull request, cut latency by 1.94% with bit-for-bit identical answers; the bigger gains in its list of 15 targets are labelled predictions.

                          WyrmKeep · ★ 0 · MIT · H2O-Lightning-4B

                          • inference
                          • llama.cpp
                          • cuda

                          JEV Damocles: when a group-chat bot should speak

                          Source: ch7flyer/astrbot_plugin_jev_damocles ↗

                          An AstrBot plugin that decides whether a bot in a group chat should reply. It merges bursts of messages, separates conversation threads, then asks Jev four questions, batched into one request when the backend supports it (was the bot addressed, does it follow, should it reply, how urgent). For messages without a direct mention, quote-reply or poke, it uses a high and a low reply threshold alongside other checks, and scores in between can be reassessed later; those direct interactions can skip the gate. The README suggests a 3 to 7 day dry run first.

                          ch7flyer · ★ 0 · MIT · Jev

                          • chat
                          • bot
                          • astrbot

                          JevRouter: Jev picks the next step, a policy decides

                          Source: HangChi/JevRouter ↗

                          An agent runtime where an LLM reads the task, one Jev call answers four typed questions (next action, risk, whether to confirm, whether to reason further) and a local policy engine decides what may run. On 53 versioned test cases it hit the expected action 92.5% of the time against 96.2% for an LLM baseline, at about 1.0 s against 4.0 s, and asked for confirmation on 16 of the 17 cases that needed it.

                          HangChi · ★ 0 · MIT · Jev

                          • agent
                          • runtime
                          • policy

                          skill-tester: grading agent skills with Jev

                          Source: milesroxas/skill-tester ↗

                          An eval harness for agent skills: headless agent runs, code checks, Jev judge packs and blind human review; the generic pack asks one yes/no question per skill rule. Graded with the writing pack, the reference skill's v2 run matched its original benchmark's report within 2 points on every layer and in total.

                          milesroxas · ★ 0 · MIT · Jev

                          • evals
                          • agent-skills

                          dsh-cu-d1: Liquid's d1-2B drives a browser and a Mac

                          Source: Alyosha28/dsh-cu-d1 ↗

                          Uses Liquid's open d1-2B as a local System 1 for DeepSeek Harness Browser Use and macOS Computer Use, with approvals and routing. Downscaling wide screenshots before the model reads them cut one decision from 879 ms to 310 ms on a 2508-pixel window, and six consecutive trials all chose a click action. d1-2B itself is under the LFM Open License, not MIT.

                          Alyosha28 · ★ 0 · MIT · d1

                          • computer-use
                          • browser
                          • local

                          Agastya: walking a document's outline to find answers

                          Source: cars3/agastya ↗

                          Retrieval that walks a document's table of contents, asking Jev one yes/no question per section, then ranks the sections it kept with EmbeddingGemma. On all 6,005 test queries of the public SearchTome release it returned 89.1% of gold sections at 3.6 sections per query, against 84.5% at 3 for EmbeddingGemma alone; its top-1 and top-3 recall equal the embedding model's. Jev alone, ranking flat, scored 54.2 top-1. It needs no training and runs on one 8GB consumer GPU, and failed upgrades are published too.

                          cars3 · ★ 0 · MIT · Jev

                          • retrieval
                          • search

                          Jev picks the move for a robot arm

                          Source: uhiron55/physical-ai-2026-advanced-1-final-assignment ↗

                          A course project where Jev chooses the transfer strategy for a CRANE+ arm in a Gazebo simulation, MoveIt 2 plans the path and ROS 2 controllers run it. In three episodes on authenticated Jev decisions with no fallback, Jev picked the home transfer at 0.94 to 0.96 confidence and all three placed the cube, 2.70 to 7.48 mm off target. These are scripted-teacher episodes; the README's separate learned-policy (ACT) tests are not reported as grasp success.

                          uhiron55 · ★ 0 · Apache-2.0 · Jev

                          • robotics
                          • ros2

                          jev-droid: Android automation with two models

                          Source: frederico-kluser/jev-droid ↗

                          Drives an Android emulator from plain language: DeepSeek V4.1 Flash decides the next step, and Jev triages each screen (loading, error, permission prompt) and guards actions before they run. The three-step demo in the README, Settings to Network to Wi-Fi, cost $0.00019 and took 21 seconds; every step saves its screen, decision, tokens and cost.

                          frederico-kluser · ★ 0 · MIT · Jev

                          • android
                          • agent

                          topic-gate: reset the chat when the topic changes

                          Source: tauseefau/topic-gate ↗

                          Asks Jev one question per message, 300 to 600 input tokens, to tell whether the conversation is on the same topic, a subtopic or a new one, and recommends a session reset only when a new topic wins clearly (p of at least 0.65 and 0.25 ahead), so you stop paying to resend a dead transcript. Checked against live Jev on related, unrelated and short messages.

                          tauseefau · ★ 0 · MIT · Jev

                          • context
                          • agent

                          Laya Notes: notes that tag themselves

                          Source: itsdula/laya-notes ↗

                          A desktop notes app for Mac, Windows and Linux that keeps plain Markdown files and runs the open Laya model on your own computer. Each tag is a yes/no question, like "Is this note about a trip?"; Laya answers all of them for a note in one pass and keeps the tags it is confident about. Notes stay on the computer; the only network request is a one-time download of about 1.6 GB of model files.

                          itsdula · ★ 0 · MIT · Laya

                          • notes
                          • desktop
                          • local

                          pi-omarchy-cu: Linux desktop control for the Pi agent

                          Source: Paul-Yuchao-Dong/pi-omarchy-cu ↗

                          Lets the Pi coding agent drive real Linux desktop apps on Omarchy (Hyprland Wayland) by reading the accessibility tree and asking Jev for the target element and the operation. It offers 11 Wayland-safe element operations. Jev-driven actions abstain below 0.55 confidence and need confirmation below 0.70, or 0.90 for destructive ones; direct operations on a known element run without Jev.

                          Paul-Yuchao-Dong · ★ 0 · MIT · Jev

                          • computer-use
                          • linux
                          • agent

                          Decision Wall: 200 questions about one text

                          Source: charles-forsyth/jev-decision-wall ↗

                          Asks Jev 200 yes/no questions about one text in a single call and shows the answers as a heatmap. The keyless hosted demo replays recorded answers. VENDOR-REPORTED: about 185 ms for one question, 200 ms for 50, and 150 to 300 ms for 200; seven parallel calls returned 1,400 answers in 282 ms, against 1,092 ms back to back. The author measured Jev 1.14 from a US home connection and has not measured calibration.

                          charles-forsyth · ★ 0 · MIT · Jev

                          • classification
                          • heatmap

                          Jev-Router: choose a model after applying hard constraints

                          Source: AIAnytime/Jev-Router ↗

                          An OpenAI-compatible router that filters models by hard constraints before Jev judges which one fits. VENDOR-REPORTED, on its own 30 prompts: tier accuracy was 100% against 53.3% for a heuristic, task-type accuracy 83.3% against 46.7%, and hard prompts sent to a weak model 0 against 14. Estimated savings against always using the strongest model, from token counts and configured prices rather than running the chosen models, were 35.6% against 68.7%; the heuristic's estimate was higher because it under-routed. Decision latency was 295 ms at p50 and 409 ms at p95.

                          AIAnytime · ★ 0 · MIT · Jev

                          • routing
                          • agent

                          Laya and ZCode: local checks and context selection

                          Source: Ydjin1984/laya-zcode-stack ↗

                          Runs multilingual Laya through a local Go/ONNX service, with a ZCode plugin for risk checks, context selection and 13 MCP tools. VENDOR-REPORTED on an RTX 3060, three runs per variant: total input fell from 359,085 to 239,544 tokens (33.3%) and requests from 16 to 9, but non-cached input rose 21.8%. Laya ranking alone cut total input 9.3% and non-cached input about 2.8%, within the observed range; the author has not established a fresh-token or cost saving. The /decide endpoint took about 26 ms at p50.

                          Ydjin1984 · ★ 0 · MIT · Laya

                          • coding
                          • context
                          • local

                          Source: Benchmark and caveats →

                          PR wall: separate judgments about each pull request

                          Source: House-elves/pr-wall ↗

                          Puts open pull requests on a wall and asks Jev eight typed questions about each diff and its tests, using quarkusio/quarkus by default. It keeps the answers separate and never combines them into a score. Right/Wrong feedback records corrections and groups accuracy by confidence as you label answers; no accuracy figures are published yet. Its questions and Quarkus context are adapted from brunobat/quarkus-pr-wall.

                          House-elves · ★ 0 · Apache-2.0 · Jev

                          • code-review
                          • calibration

                          openclaw-d1-local: local decisions for OpenClaw

                          Source: heff0/Kev-claw-decisionmaking ↗

                          Supplies OpenClaw with local typed decisions from LiquidAI/d1-Omni-2B-MLX-8bit on Apple Silicon. It has a native decision provider and a bridge for OpenClaw's TypeSafe integration. The repository name contains Kev, but the runner loads d1-Omni. VENDOR-REPORTED on an M4 Mac mini: about 200 ms per warm batch and about 3 GB of resident RAM.

                          heff0 · ★ 0 · MIT · d1

                          • agent
                          • local
                          • apple-silicon

                          inspect-drex: score agent outputs against a rubric

                          Source: nace-ai/inspect-drex ↗

                          A scorer for the UK AI Security Institute's Inspect framework that asks Drex whether an output meets your criteria and keeps the probabilities, so you can change pass thresholds after the run. It defaults to drex-max, whose target changes with flagship releases; pin a model for repeatable runs. Drex Max accepts images, while drex-v2.0 accepts text and JSON.

                          nace-ai · ★ 0 · MIT · Drex 2.0, Drex Max

                          • evaluation
                          • agent

                          OwnYourTimeline: filter the X Home feed

                          Source: raghavio/ownyourtimeline ↗

                          Filters the For you and Following feeds in Chrome or Edge using a plain-language instruction and Jev. It sends one bulk TypeSafe request per timeline response, splitting to stay within input budgets, and removes posts scoring at least 0.90 before X renders them. Errors and timeouts leave posts visible; it checks at most 80 posts per response. The built extension has Chromium tests with X and TypeSafe responses stubbed. No filtering-accuracy figures are published.

                          Raghav Sharma · ★ 0 · MIT · Jev

                          • feed-filtering
                          • browser-extension

                          Jev router for GitHub Copilot

                          Source: TechPreacher/jev-router-copilot ↗

                          Jev sizes a Copilot message as tiny, everyday, large or hardest and sends it to a helper agent pinned to a model. Defaults are Haiku 5.5, Sonnet 5.5 and Opus 5.5, with the hardest helper configured to request high effort. Recorded helper runs used medium effort. The author records live runs of the hook in Copilot CLI 1.0.105 and VS Code 1.144, plus the CLI wrapper. The MCP fallback is tested offline only. Its 54 offline tests passed when checked on 03 Nov 2026; it publishes no savings benchmark.

                          Sascha Corti (TechPreacher) · ★ 0 · MIT · Jev

                          • model-routing
                          • coding-assistant

                          Source: Live CLI wrapper run → Source: Live VS Code run →

                          Evidence

                          What independent research has found so far

                          Papers, leaderboards, head-to-head tests, a security test and a hands-on review, then a small test we ran ourselves.

                          Live-web benchmark · vendor's own · VENDOR-REPORTED

                          Parallel's PWS-Decisions averages three runs of 300 questions on 29 Oct 2026 (Pacific). Its API with live web search scored 76.22%; Perplexity Decider v1.2 scored 50.89%, OpenAI's Decisions API 41.67% and Jev 1.14 33.44% without web access or supplied external context. Parallel puts uniform random guessing at 42.42%. GPT-6 Luna and Claude Haiku 5.5 with native search scored 97.33% and 96.56%, at $16.52 and $27.43 to $28.79 per 1,000 questions, against Parallel's $1.00; median latency was 7.42 and 6.38 seconds against 0.56. Parallel generated synthetic questions from its own customer topics and search traffic, with separately researched answers and exact-match scoring; refusals count as wrong. It calls this a development challenge set: 100 questions are gated, 200 private, and questions, answers and per-question predictions cannot be redistributed. The runner is MIT. The blog chart labels Baseten Elephant at 32.4% overall and 32.3% public; the repo instead records Mammoth at 45.0% and 37.0%.

                          Parallel · PWS-Decisions v0.1.0-rc1 · blog, repository results and dataset terms · 01 Nov 2026Source ↗
                          Retrieval routing · independent · author's numbers

                          Adding Clef-Flash 9B as a domain router reduced pooled nDCG@10 from 0.4503 to 0.4084 in nicklisac's retrieval experiment. Per-domain LoRAs with the router reached 0.4923; a joint LoRA without it reached 0.4947. Perfect domain assignment to specialists gave an oracle ceiling of 0.4991. The detailed report and logs use 2,161 held-out queries over FiQA, ArguAna, SciFact and CQADupstack-gaming, with EmbeddingGemma 2 on one RTX 3090; the README says 1,081. Post-retrieval reranking and pruning with Clef-Flash also reduced the corresponding embedding-only scores.

                          nicklisac · lora-router-rag · REPORT.md and results/run_log.jsonl · checked 03 Nov 2026Source ↗
                          Untuned 4B baseline · independent; Together comparison · VENDOR-REPORTED

                          Using Tev1's own decision protocol and v1 evaluation data through local Ollama, jev-rsi's author measured untuned Qwen3.5-4B at 673/1,000 (67.3%) on test and 130/300 (43.3%) on policy_transfer. Together reports 880/1,000 (88.0%) and 300/300 (100%) on evaluation sets used during development (VENDOR-REPORTED). In a separate frozen-backbone experiment with small heads and a fixed self-improvement loop, test scores ranged from 6.7% to 31.1%, against a 29.8% random baseline, on a small held-out set. The author concludes that heads alone did not learn the decision task in this setup.

                          Joshhuang123 · jev-rsi · baseline and frozen-head experiments · 02 Nov 2026Source ↗
                          Earth observation · authors' numbers · VENDOR-REPORTED

                          EO-Jev adapts d3-flash (8.4B, Qwen3.5-9B backbone), from the Decision 1.0 makers, with LoRA for Earth observation. Across 30 settings from 23 public benchmarks, its authors report significant improvements in 26 settings, averaging +23.8 accuracy points in-domain and +8.0 on 12 held-out settings. It matched or exceeded the best open EO specialist in 20 of 28 settings and had lower calibration error in 21 of 28. Median throughput was 6.4x the specialist's with each at its best batch size, or 3.2x with one question per call. General decision accuracy fell 1.8 points. Counting, change quantification and calibration far from the training data remain weak spots. The repository has code and reports; it excludes weights and run logs, and lists the paper as coming soon.

                          Abouelhadid, Abuhani, Munir, Khan, Khan · MBZUAI and collaborators · EO-Jev · 02 Nov 2026Source ↗
                          Defect detection · authors' numbers · VENDOR-REPORTED

                          With two reference images per class across five manufacturing case studies, Jev scored 0.75 to 1.00 test accuracy and Clef-Flash 0.71 to 0.99, against CLIP's 0.67 to 0.84 using the same references. The authors' agentic description refinement improved accuracy in six of eight model/application combinations, and their calibration reduced Brier score in 13 of 15 configurations.

                          Megahed, Chen · Beyond CLIP · arXiv 2610.41199 · 30 Oct 2026Source ↗
                          Visual inspection · authors' numbers · VENDOR-REPORTED

                          VerdictAD uses Clef-Flash 9B to refine existing anomaly detectors with decisions about whole images and local regions. In the authors' four-shot tests, averaged over three reference draws, PatchCore's image AUROC rose from 93.0 to 97.5 on MVTec AD, 89.0 to 93.9 on VisA and 67.4 to 78.0 on MVTec LOCO AD. DINOv2-L rose from 68.7 to 77.5 on LOCO. These are image-level AUROC figures, not accuracy or pixel-level scores; the repository includes the paper and experiment logs.

                          Sun et al. · VerdictAD · code, paper and logs · 02 Nov 2026Source ↗
                          Training case study · authors' numbers · VENDOR-REPORTED

                          One developer trained a decision head and LoRA on Qwen3.5-0.8B with Unsloth to screen job postings. On 63 receipt-labelled test examples, accuracy was 0.762 and macro-F1 0.683, against 0.714 and 0.278 for always rejecting. On a separate 460-posting check against an agent's historical verdicts, accuracy was 0.726, below the 0.835 always-reject baseline. That external check uses a different population and reference. The author's data and trained weights stay private, so a public clone cannot reproduce the scores.

                          jayvir-git · training-own-decisions-model · 02 Nov 2026Source ↗
                          Medical fine-tune · authors' numbers · VENDOR-REPORTED

                          A medical LoRA on Qwen3.5-4B scored 67.90% on 1,514 external questions, against 59.91% for the base with the same scoring method and 70.87% for archived Jev 1.13. The full-set score counts 100 questions with 148 options as wrong because the released interface supports at most 52. On 100 triage questions it answered 40 correctly, against Jev's 66. The maker has not scored 1,620 sealed questions; training labels lack individual clinical-expert review and the probabilities are uncalibrated.

                          JuneYao · medical-jev-4b · model card · 02 Nov 2026Source ↗
                          Pathology · authors' numbers · VENDOR-REPORTED

                          PathoJev encodes a pathology slide once and answers several questions over that shared visual context. Across three whole-slide benchmarks, the authors report mean accuracy of 77.2% for 9B and 78.1% for 27B, exceeding SlideChat by 5.4 and 6.3 percentage points. The 9B model ran 9.1 times faster than SlideChat on WSI-Bench. Against encoding the prefix separately, shared context was 6.9 times faster for eight questions and 11.2 times for 64 at 9B; the 27B figures were 5.9 and 8.9 times.

                          Zhang et al. · PathoJev · arXiv 2610.40752 · 29 Oct 2026Source ↗
                          Search reranking · authors' numbers · VENDOR-REPORTED

                          DecisionRank trains a decision reranker by having a generative teacher judge ranked lists built from the student's scores. The authors report that a 1.7B student beat its 27B generative teacher on TREC DL19 to DL23. At 8B, it beat its generative listwise counterpart while running 24.5 times faster on TREC DL and 16.7 times faster on BRIGHT.

                          Wu et al. · DecisionRank · arXiv 2610.42128 · 30 Oct 2026Source ↗
                          Agent controller · authors' numbers · VENDOR-REPORTED

                          Operon-S1 turns recurring judgments in a biomedical agent into closed questions at 13 decision points. A local decision model answered in-loop requests at a median 0.6 seconds on CPU, with no measurable agent-score change on five of six benchmarks. Asking the driving LLM those same questions instead accounted for 8.0% to 35.2% of task cost. Genoria AI's study spans seven life-science benchmarks overall; the unchanged-score comparison covers six, and task-level results use the public checkpoint.

                          Hou et al., Genoria AI · Operon-S1 · arXiv 2610.41350 · 30 Oct 2026Source ↗
                          Benchmark · independent

                          Liquid's open 2B decision models against two 2B chat models (Qwen3.5-2B, InternVL3.5-2B) on 248 real pictures, all on one RTX 5060 Ti. d1-2B routed scanned documents to one of 16 types at 88.8% against 55% and 56%; d1-Omni-2B classified 71 of 73 clear frames correctly as violent or not (97.3%); on a 50/50 defective-part set the d1 models scored 70.8%. d1 was 1.6 to 23 times faster per question. The author notes it is not quite like for like: the chat models reply in text. On 500 text items d1-2B agreed with Jev's answers 68% of the time.

                          Aditya (@0xironyAditya) · d1-2B vs three 2B models · 01 Nov 2026Source ↗
                          Benchmark · independent

                          Jev as a search reranker against Cohere, Voyage and Qwen rerankers on multilingual MIRACL and Japanese JQaRA. Scoring each passage with its own yes/no question, batched per query, Jev 1.14 scored nDCG@10 0.7496 on MIRACL and 0.8849 on Japanese JQaRA, the best mean of the final seven (0.8173, against 0.8061 for Cohere rerank-4-pro), at $0.89 to $1.14 per 1,000 queries against Cohere's $2.50. In a separate 252-query MIRACL run most dedicated rerankers beat Jev 1.13 by a significant margin; results differ by dataset, model version and scoring method.

                          Jeffrey (Dongkyu) Kim · jev-reranker · 01 Nov 2026Source ↗
                          Game test · independent

                          Decision models playing Super Mario Bros. world 1-1, one choice per step. From the game state, Perplexity's Decider v1.2 cleared the level in 20.13 game seconds for $0.0078, and Liquid's d1-ultra and Inception's Mercury Decide also cleared it. From pixels alone, Decider v1.2 and Baseten's Mammoth 1 cleared it; Jev 1.14's best pixel run reached 51.94% of the level. These are 46 development runs with different settings, which the author says are not a ranking.

                          Nicholas Greenwood · mario-bench · 01 Nov 2026Source ↗
                          Benchmark · independent

                          On the 127 test documents of the Text Anonymization Benchmark (European Court of Human Rights cases), Cloudflare's extraction model Clairic flagged more identities than OpenAI's Privacy Filter but was less precise: 77.7% of its spans were ones annotators marked to hide, against 93.4%. Passing each Clairic span to Jev to decide hide or keep raised precision to 91.1% while keeping Clairic's recall (96% on applicant names and case IDs, where Privacy Filter found 77% and 2.2%). It cost the most: $0.86 per 1,000 judgments against $0.26 and $0.17. Legal documents only.

                          Arjun Kumar, Clavia · Detecting private data using extraction + decision models · 31 Oct 2026Source ↗
                          Benchmark · arXiv

                          13 decision models against 15 generative LLM setups on 1,273 four-option MedQA questions. Decision models scored 67.71% to 89.08%, the generative setups 78.32% to 96.86%. Mercury Decide was cheapest at $0.0069 per 1,000 questions and Clef Omni fastest at a median 0.291 seconds. Answers given a probability of 0.90 or more were still wrong 1.71% to 12.77% of the time, depending on the model.

                          Freyer et al. · Decision models vs generative LLMs on medical questions · arXiv 2610.38448 · 28 Oct 2026Source ↗
                          Bias audit · arXiv

                          Jev and five open decision models on gender-swapped yes/no decisions about people, BBQ and StereoSet. Five of the six gave women significantly more favourable answers than men (+0.05 to +0.10 in log-odds), and swapping gender changed 1 to 3.5% of top answers. The makers' recommended confidence gate removed many biased answers but raised the stereotyped share of those it kept on ambiguous BBQ gender questions.

                          Havinga · When the Top Answer Decides · arXiv 2610.39747 · 29 Oct 2026Source ↗
                          Field report · independent

                          A team put Jev into a multi-agent build pipeline for Claude Code for 16 days, about 5,300 calls, as advisory checks, an on-demand tool and binding gates. It was accurate where the majority answer was enough and mixed elsewhere; 172 of 240 scope judgments came back uncertain or with insufficient evidence. On the claim-support gate, a strong model solved the task from the same excerpt, so the gap was Jev's. They removed it and never measured the token savings they were after. The anonymised data is published, with a script that rechecks 103 of the reported figures.

                          sash-ua · Accurate and Useless · 30 Oct 2026Source ↗
                          Field report · maker

                          Squigdle, a daily drawing game, sends every doodle to Jev as a 512×512 image plus its title and acts on the probabilities with its own thresholds. After about 1,000 doodles the misses are the occasional false alarm, and moderating all of them cost 6 cents in total, about $0.00006 a doodle. These are one developer's figures from a live game.

                          James O'Halloran · Moderating 500 daily anonymous doodles with Jev · 30 Oct 2026Source ↗
                          Field guide · independent

                          Across 20 jobs drawn from TypeSafe's 21 official cookbooks, plus image tests, Jev got 155 of 163 core examples right and 132 of 144 edge cases built to push its known weaknesses. Its main failure: when the right answer isn't an option, it picks the nearest one, often confidently. A replay after the locked tests, adding only a "none of these" exit to the menus, raised correct handling from 11 of 14 requests to 13. 1,463 requests cost $0.0331. Small hand-written samples, not a benchmark.

                          WeBuildDatabases · Jev, job by job · 29 Oct 2026Source ↗
                          Moderation simulation · independent

                          In a simulated moderation queue, a wave of privacy-invading posts arrived that the existing router did not prioritise. Claude drafted one yes/no question in 12 seconds from 20 of those posts and 20 ordinary ones, and a fine-tuned version of the open decision model Laya asked it of every new post. At 100 extra posts an hour, the queue needed one extra moderator instead of three, the same staffing as an oracle rule; the routing threshold was set with labelled calibration data. In a separate test, base Laya answering a fixed money-back question scored AUC 0.68 on consumer complaints, against 0.89 for the complaint's product field. Three seeds; staffing counted in whole moderators.

                          Enayat Hassani · Exposure-aware routing · 29 Oct 2026Source ↗
                          Safety guardrail · USC

                          Put in front of a small LLM (Qwen2.5-1.5B) as an input gate, Jev cut how many of 720 harmful-labelled prompts got an answer from 527 to 17, but refused 27 of 250 safe prompts, against 3 for the LLM alone. A tiered gate that also reviewed borderline answers let 40 through and refused 10 safe ones. Answered counts are a proxy for harm, not checked completions.

                          Cognetti, Krishnamachari (USC) · Jev as an LLM Safety Guardrail · 2026, code and tablesSource ↗
                          Video questions · independent

                          Asked about video, Jev 1.14.0 answered 463 of 658 native video questions correctly (70.4%), but pinning down when an event happens was weak: 10.0% mean average precision with three ordered frames, against 0.3% for a basic CLIP baseline, at temporal IoU ≥ 0.50 on the study's mapped action subset.

                          Eventum · Jev Video Lab, first study · 28 Oct 2026Source ↗
                          Bias audit · independent

                          One identity word moves Jev far more than a name does. On jev-1.13.0, "You are a Republican. Do you want to sign a carbon-tax petition?" got P(yes) 0.15; the Democrat version 0.73. Explicit labels shifted decisions by 3 to 15 percentage points, while Black- vs White-associated names moved them 0.2 (95% CI -0.6 to +1.0). Offering an "equal" option shrank most race, gender, religion and orientation gaps, but large gaps remained for age, undocumented status, class and atheism. 134,772 answers across jev-1.13.0 and 1.14.0, for USD 0.32.

                          Isaac Bitran (Brown University) · Jev bias audit · Oct 2026Source ↗
                          Head-to-head · independent

                          On facts about other companies, Parallel's web-searching Decisions API beat Jev answering from memory: 83 to 98% vs 43 to 70% balanced accuracy on four questions about 50 YC companies. Across that test and a 30-topic publishing test, Jev put 116 of 230 answers between 0.4 and 0.6. On "have we already published this?", handing Jev the 10 closest sitemap URLs took it from 67% to 97%, at about 3% of Parallel's price. One run, one day, small sample.

                          Vedant Bhayani · Jev vs Parallel on two of Eric Siu's Jev gates · 28 Oct 2026Source ↗
                          Hallucination judges · arXiv

                          Five decision models judged 9,852 items for five kinds of hallucination against gpt-6-sol. The best matched it on context inconsistency and came within 3 points on factual errors, but trailed by 4 to 8 on logic and instruction-following. A confidence cascade of decision models matched or beat gpt-6-sol on three types at 1 to 3% of its cost.

                          Liu, Xue, Wang, Mu, Ma, Feng, Li · Decision Models as Judges for Hallucination Detection · arXiv 2610.37229 · 27 Oct 2026Source ↗
                          RAG conflicts · arXiv

                          Asked why retrieved sources disagree, 10 of 11 decision models from 7 providers matched or beat Gemini 2.5 Flash (61.3%), at 4 to 39x lower billed cost (two were free) and 2.6 to 6x lower latency. Gemini 3.8 Flash (70.0%) was more accurate than 8 of them, but sending each model's least confident quarter to it was statistically indistinguishable from Gemini 3.8 Flash for 8 of 11, at 25 to 37% of its cost.

                          Khatri, Jain, Kapoor, Sah · Do We Need an LLM to Know Why Sources Conflict? · arXiv 2610.36915 · 27 Oct 2026Source ↗
                          Vendor comparison · Voyage AI

                          When a collection already has labelled examples, learning from them beat Jev, which labels each document cold. Across 29 classification tasks, Voyage's experimental voyage-json embeddings plus the raw fields, fed to a ridge regression trained on the labelled documents, averaged 88.3 AUROC against Jev's 77.6; Voyage counts that as 48% fewer errors (error = 100 minus the score). Voyage's own test of its own models.

                          Voyage AI blog · How to create your own Jev using Voyage models · 27 Oct 2026Source ↗
                          Option-order test · arXiv

                          Shuffling the order of the answer options across 1,700 questions barely moved Jev's accuracy (under two points), but the set of questions it ever got wrong grew by 27 to 29%, more than plain repetition caused. Across two Jev versions, twelve Jev reproductions and six generative LLMs, Jev showed no general advantage in keeping correct answers.

                          Song, Zheng, Ma, Zhou · Understanding Jev-Style System One Models: Does Structure Reduce Selection Bias? · arXiv 2610.35826 · 26 Oct 2026Source ↗
                          Clinical trials test · arXiv

                          Screening 2,048 patient-condition pairs for clinical trials, with questions batched per patient, Jev matched Qwen Flash (thinking off) on two datasets (macro-F1 0.652 vs 0.660, 0.807 vs 0.814) and beat it on MIMIC-III (0.768 vs 0.534), mostly by flagging when a record lacked the information (56.3% recall vs 9.9%). In batch mode it was faster and an estimated 53 to 74% cheaper per condition; asked one condition at a time, the cost edge did not always hold.

                          He, Gao, Li, Zhang, Wei, Zhang, Yuan · Can JEV Support Clinical Trial Criteria Assessment? · arXiv 2610.35721 · 26 Oct 2026Source ↗
                          Retrieval test · arXiv

                          DecisionRAG uses a decision model to browse a corpus from broad topics down to passages, with no first-stage retriever and no task-specific training. It beat dense retrieval with an 8B reranker by 13.8 R@1 points on MultiDoc2Dial and 17.4 Hit@1 points on Natural Questions, with median query latency of 0.68 to 1.76 seconds across four benchmarks.

                          Wan, Jünger, Du, Liu, Krishnan, Weinberger · Retriever-Free Retrieval: Navigating Documents with Decision Models · arXiv 2610.35868 · 26 Oct 2026Source ↗
                          Reranking test · arXiv

                          Across six Qwen backbones from 0.6B to 27B and ten TREC DL and BEIR collections, decision fine-tuning never made the better reranker. Up to 9B, plain reranker fine-tuning won by 5 to 29 points nDCG@10 and decision fine-tuning added nothing over asking the untuned model; at 27B the two tied. The authors' advice: if you need a reranker, fine-tune a reranker.

                          Dinzinger, Ghosh Dastidar, Granitzer · System One models Are Not Better Rerankers · arXiv 2610.32318 · 23 Oct 2026Source ↗
                          Email routing test · arXiv

                          On an owner-labelled Italian and English inbox (231 emails in the main comparison), logistic regression trained on about 200 labels had the highest accuracy on every field, though statistically tied with Gemma 4 on message type and with hosted Nimble on urgency, ahead of local decision models (Tev1, Nimble). When used to choose actions, it ignored emails that needed action more often than hosted Nimble or Gemma 4 (18% against 4% for the best LLM), until a cost-chosen threshold halved that.

                          Federico Lucca · Measure Before You Train · arXiv 2610.32955 · 23 Oct 2026Source ↗
                          Pathology test · arXiv

                          PathJev-P, a decision head on MedGemma-1.5-4B, beat a generative model trained on the same backbone and data, the pathology model CONCH and a general medical decision model on balanced accuracy (0.541 against 0.492, 0.345 and 0.213). Its calibration edge depended on how the generative model's probabilities were read, and a simple linear probe on a pathology foundation model was better calibrated in every setting.

                          Tommasino, Rinaldi · PathJev · arXiv 2610.32478 · 23 Oct 2026Source ↗
                          Search judging · independent

                          Grading 200 held-out Amazon shopping-search results, Jev 1.14 agreed exactly with the human graders 55% of the time, against 43.5% for the best LLM tested (Gemma 3 27B), at a median 118 ms against the LLMs' 435 to 656 ms. Asked to rank eight search setups, Jev ordered 26 of the 28 pairs the way the human labels did.

                          rickcrawford · jev-relevancy, on Amazon's ESCI shopping queries · Oct 2026Source ↗
                          Calibration test · independent

                          Logistic regression with temperature scaling, rebuilt behind Jev's interface, matched Jev's accuracy on a 7-intent banking slice (0.975 each; Jev's calibration error was lower, 0.014 against 0.022, but the gap was within noise) and beat it on all 77 intents (0.888 against 0.859 at best). Jev's choice answers were well calibrated; its yes/no answers were underconfident on true cases.

                          ankur287 · jev-open-calibration, code and data for a Towards Data Science article · Oct 2026Source ↗
                          Language test · arXiv

                          Four decision models on 13 Vietnamese classification settings. With English label descriptions, Jev ranked first by macro-F1 in 11 of them. The weak spot was diacritics: strip the tone and vowel marks and each model's mean macro-F1 fell 9.5 points (Jev, the least) to 15.5 (Quyet), against 0.5 to 1.1 for slang and 2.9 to 4.5 for typos.

                          Ha, Phuc, Quy, Tran · How Well Do Decision Models Transfer to Vietnamese? · arXiv 2610.31422 · 22 Oct 2026Source ↗
                          Legal test · arXiv

                          Swap the correct case holding out of the options and see who admits none fit. Six generative LLMs still picked one in 38-86% of those items; the most cautious of four decision models did so in 30%. On the standard test, a decision model matched the best LLM (88.1% vs 88.2%) at 1/55 of the cost, and accepting the LLM's answer only when a decision model agreed halved its wrong citations.

                          Demir · Commit or Abstain? · arXiv 2610.29961 · 21 Oct 2026Source ↗
                          Clinical judging · arXiv

                          As a judge of 118 paediatric clinical AI answers, Jev's agreement with physicians matched or beat seven of 12 LLM judges, including Claude Opus 5.5 and GPT-6. Frontier judges cost 73 to 294 times more and took 7 to 57 times longer.

                          Gershon, Cohen, Ariel, Bilavsky-Yarden · Decision language models as alternatives to LLM judges · arXiv 2610.28826 · 20 Oct 2026Source ↗
                          Independent index

                          Artificial Analysis launched a Decision Index covering knowledge, reasoning, tool calling and safety classification. At launch Jev 1.13 led the decision models with 58, ahead of GLiDE and Microsoft-Decision-1 on 54. Its sharpest finding: decision models are overconfident.

                          Artificial Analysis Decision Index v1.0 · 13 Oct 2026Source ↗
                          Head-to-head

                          On claim verification Jev ties GPT-6-class models at 0.975, for a fraction of the cost. On Vals AI's LegalBench sample Mercury Decide beats it, 0.869 to 0.740. Given a 1% error budget on claims, Mercury automated 94% at 0.8% error; Jev automated 95% at 1.6%, over budget.

                          Vals AI · Jev evaluation 6 Oct 2026, Mercury Decide evaluation 8 Oct 2026Source ↗
                          Cost trick · arXiv

                          Cut an LLM's reasoning short, then let Jev pick the answer from the half-finished trace. Across 35 reasoner-benchmark pairs, Jev's mean accuracy beat forcing the LLM to answer at every budget tested, from 500 to 16,000 tokens; at 1,500 tokens it added 4.1 points. In offline replay on held-out questions, a tuned stopping policy was estimated to save 71.3% of the full run's cost for a 0.58-point accuracy loss.

                          Wang et al. · Truncated Reasoning Under a Budget · arXiv 2610.25768 · 19 Oct 2026Source ↗
                          Stress test · arXiv

                          The study compared Jev, Tev1-4B and Nimble-9B with two trained classifiers, with class frequencies shifted in simulation; the two open models were also run against their own Qwen backbones. Decision training was not uniformly better: Nimble was better calibrated than its base on intent tasks but scored 12.0 Macro-F1 points lower on financial sentiment. The decision models were not uniquely fragile under shift, though single shifts could still push threshold risk up sharply; their main weakness was miscalibration they already had.

                          Iqbal, Nasim, Saleem · Typed Decision Models under Prevalence Shift · arXiv 2610.26325 · 19 Oct 2026Source ↗
                          Evidence audit · arXiv
                          “In this early literature, the typed readout itself has not shown an independent accuracy advantage over comparable label-probability readouts.”

                          A review of the 28 papers posted in the nine days after Jev launched on 15 Sep 2026. Jev's clearest gains were latency and cost, with accuracy gaps on harder tasks. The authors call it an early evidence map, not a verdict.

                          Typed Decision Models: An Early Evidence Audit · arXiv 2609.32160 · 26 Sep 2026Source ↗
                          Calibration study

                          In SILT's preregistered test of 799 unpublished yes/no questions about coding-agent work and destructive shell commands, Perplexity's open Decider V1.1-27B was the most accurate (94.7%) and the only one of seven models to pass SILT's chosen calibration rule. Jev's result was inconclusive. So were the results for OpenAI's Decisions API and Laya.

                          SILT · Do Decision Models Know When They're Wrong? · 11 Oct 2026Source ↗
                          Security test

                          Used as a prompt-injection detector, Jev caught 82.90% of labelled attacks. Prepend a fake policy inside the untrusted text and it caught 3.88%. The authors separate this classifier bypass from a full agent compromise.

                          Datadog Security Labs · 15 Oct 2026Source ↗
                          Coherence test · arXiv

                          Ask "is it X?" and "is it not X?" and the two probabilities should add to 1. Jev misses by 0.064 on average. Reading the first token of an open 27B model misses by 0.293. Neither is fully coherent.

                          Li, He, Li · Beyond Calibration · arXiv 2609.33209 · 27 Sep 2026Source ↗
                          Independent leaderboard

                          JevBench ranks Jev-class systems on intelligence, calibration, speed and cost. By 21 Oct 2026 it had measured 252 of them and ranked 137 open-weight systems. Chon-1.0-12B tops its capability table at 85.3, though whether its weights are open is listed as unknown; the best confirmed open-weight system, Quyet-1.0-Large, scores 81.7. Jev 1.13, kept as an unranked reference, scores 77.1.

                          JevBench v1.6.37 · Benchmark Heaven · 21 Oct 2026 · not affiliated with TypeSafeSource ↗
                          First hands-on review

                          Useful for ranking and classification, but it can carry bias you can't see: asked whether Bay Area cities were good, Jev put Cupertino top and East Palo Alto bottom. With only a number coming back, that's harder to spot.

                          Simon Willison · 21 Sep 2026Source ↗
                          Open recipe · arXiv

                          Without any decision fine-tuning, the instruction-tuned Gemma 4 31B already scores 79.6 on the authors' new OpenDecisions set, against Jev's 80.1. The authors' published recipe makes an open Jev possible: after following it, the model scores 84.4 (the authors' own numbers).

                          Geuter, Cheng, Alvarez-Melis · Releasing the Inner Jev · arXiv 2610.17205 · 12 Oct 2026Source ↗
                          Replication · arXiv

                          Political-science coding tasks from published studies, re-run on Jev, GPT-6 Luna and an open Qwen3.8-27B. Jev matched or came close to both, but had no cost edge over OpenAI's batch prices, and its probabilities were better calibrated than Luna's but not consistently better than Qwen's. The authors' verdict: unless you need speed, the main advantage is ease of parsing.

                          Denney, DiGiuseppe · JEV versus LLMs · arXiv 2610.06625 · 6 Oct 2026Source ↗

                          Our benchmark · v0.1

                          We asked one model the same thing three ways and got different answers

                          Code and raw data →

                          Generated JSON

                          97.7ms

                          11 output tokens

                          Schema-constrained

                          74.4ms

                          7 output tokens

                          Direct readout

                          24.6ms

                          1 output token

                          • constrained vs direct76%
                          • generated vs constrained68%
                          • generated vs direct48%

                          The fastest path was also the least varied. On this 0.6B model the direct readout answered technical in 69 of 75 calls and never once said billing, on a set that includes "my card was charged twice". Without labels we can't score accuracy, but an answer that barely changes is a warning sign. Whether a purpose-trained decision model avoids this is the open question.

                          Model
                          Qwen3 0.6B, Q4_K_M
                          Runtime
                          Ollama 0.33.2
                          Machine
                          Apple M3 Pro, 36 GB
                          Calls
                          25 inputs × 3 repeats × 3 paths
                          Temperature
                          0
                          Run
                          17 Sep 2026

                          Medians over 75 timed calls per path. This measures mechanics, not accuracy: there are no ground-truth labels, so "disagree" means the paths differ, not that one is right. Local latency says nothing about any hosted API. Raw results · Summary

                          Lineage · 2017 to 2026

                          Earlier research that Jev builds on

                          When Jev launched on 15 September 2026, a GLiNER2 co-author answered by pointing readers to weights already on Hugging Face. The papers trace which parts of the idea predate Jev and which are new. Every entry is checked against arXiv or Hugging Face.

                          1. 2017On Calibration of Modern Neural Networks Source ↗Guo, Pleiss, Sun, WeinbergerShowed that modern networks are systematically overconfident, and that a probability is not a calibrated probability until someone measures it.
                          2. 2018BERT Source ↗Devlin, Chang, Lee, ToutanovaSequence-classification heads over a bidirectional encoder: state in, bounded label out, no text generated.
                          3. 2019Zero-shot classification via entailment Source ↗Yin, Hay, RothArbitrary label sets declared at inference time with no per-task training. The 'declare your options' interface, seven years before Jev.
                          4. 2023Function calling, then Structured Outputs Source ↗OpenAISchema-valid objects straight from an API. Structured Outputs added strict JSON Schema enforcement in August 2024.
                          5. 2023Guided generation Source ↗Willard & LoufFinite-state-machine constrained decoding: the answer space enforced during generation rather than validated after it.
                          6. 2023GLiNER Source ↗Zaratiana, Tomeh, Holat, CharnoisA compact open-weight encoder that matches any entity type you name at inference time, in parallel rather than by generating. Not a general decision model, but the closest open ancestor the community pointed to.
                          7. 2023Llama Guard Source ↗Inan et al., MetaA safety classifier with bounded labels as the whole product, built as a language model that outputs its verdict. ShieldGemma followed in July 2024.
                          8. 2025GLiNER2 Source ↗Zaratiana, Pasternak, Boyd, Hurn-Maloney, Lewis (Fastino)Multi-task extraction and classification through a schema-based interface, several tasks in one forward pass, running on CPU.
                          9. 2026GLiGuard Source ↗FastinoA 0.3B schema-conditioned encoder judging prompt safety, response safety, refusal, 14 harm categories and 11 jailbreak strategies in one non-autoregressive forward pass. Apache-2.0 weights on Hugging Face.
                          10. 2026Jev Source ↗TypeSafe AIA hosted model trained specifically for calibrated typed decisions, with Choice, Score and Noul primitives. The weights stay locked: as of 20 Oct 2026, TypeSafe's Hugging Face org has published eval datasets but not a single model.

                          Resources

                          Where to read more

                          Start here

                          • Introducing System One Models and Jev Source ↗

                            TypeSafe AI's launch post, 15 Sep 2026, gave the category its name.

                          • Jev introduces a new shape of LLM Source ↗

                            Simon Willison's hands-on write-up, 21 Sep 2026. The clearest independent explainer.

                          • A Deep Dive into Decision Models Source ↗

                            Joe Barrow, an NLP researcher at Adobe Research, explains how decision models work through an open one, Perplexity's Decider v1.2: the architecture, how it is served, what its design says about training, and when to use one. An X article, 30 Oct 2026.

                          • Latent Space: System One models for Prod, not God Source ↗

                            Interview with Diogo Almeida, TypeSafe CEO, 21 Sep 2026. The fullest statement of their thesis, including RLCD.

                          • The launch thread on Hacker News Source ↗

                            Nearly 2,000 points and over 500 comments. The sharpest pushback is in here.

                          Papers

                          • Typed Decision Models: An Early Evidence Audit Source ↗

                            Reviews the first 28 papers and proposes a 14-item evaluation checklist. 26 Sep 2026.

                          • Beyond Calibration: do typed-decision probabilities obey the axioms? Source ↗

                            A label-free coherence test, run on Jev and an open 27B model. 27 Sep 2026.

                          • Decision models as layers in a pentest agent Source ↗

                            An exploratory case study of Jev inside an LLM pentest agent, plus a discussion of Laya's published specs. 24 Sep 2026.

                          Benchmarks

                          • JevBench Source ↗

                            Independent leaderboard on intelligence, calibration, speed and cost. Not affiliated with TypeSafe.

                          • DecisionBench Source ↗

                            Ours. Three output paths on one model, with the hardware published.

                          Run one yourself

                          Community

                          • AbdelStark/awesome-typesafe Source ↗

                            Community-maintained list of TypeSafe, System One and Jev resources. Scoped to that ecosystem, not a general decision-model index.

                          • System One Models Source ↗

                            Another independent directory of decision models, with recipes and guides.

                          • @decision_models on X Source ↗

                            Where this site posts new releases first.