# hs-decide-1-flash for Hindsight memory and its price > hs-decide-1-flash is Vectorize's open 0.8B model for agent memory. It supports eight languages and costs $0.02 per million hosted input tokens. Updated 2026-11-04 (Australia/Adelaide) By Vectorize (Hindsight) · Released: 2026-11-03 · OPEN Vectorize's Hindsight team fine-tuned this 0.8B model from Qwen3.5-0.8B for agent memory. It returns probabilities for choice, score and yes/no questions in one pass. Hindsight first tried these judgments with [Jev](), then trained its own model to check whether facts are new and evidence is enough. It uses the same System One request shape. ## Facts - Size: 0.8B - Base: Qwen/Qwen3.5-0.8B - Licence: Apache-2.0 - Inputs: Text and structured state - Languages: English, Spanish, French, German, Portuguese, Japanese, Korean and Chinese - Context: 65,536 tokens per request (trained) - Price: $0.02 per million input tokens on Hindsight Inference; input only · [Compare prices]() - Access: Open weights; vLLM with the repository's serving plugin; hosted POST /v1/systemone ## Claims and sources - **VENDOR-REPORTED**: In Hindsight's own 1.2.0 evaluation, quality rises for six of seven LLMs. glm-5.3-flash goes from 73.8 to 87.1 and deepseek-v4.1-flash from 81.9 to 85.1; gemini-3.1-flash-lite goes from 87.1 to 86.6. That model is also the judge, so its rows are self-graded. Costs fall for five LLMs. [source]() - **VENDOR-REPORTED**: glm-5.3-flash with the decision model scores 87.1 at $7.72 per thousand cases, against 89.0 at $147 for gemini-3.8-flash alone. Costs use list prices and an equal mix of four memory operations. [source]() - **VENDOR-REPORTED**: Median decision-model latency is 167 ms, with 350 ms at p90. The replaced LLM calls had median latencies of 1 to 7 seconds, depending on the model. [source]() - **VENDOR-REPORTED**: Reflect costs 4.6x less with gemini-3.8-flash, while quality drops from 94.1 to 91.2. With gemini-3.1-flash-lite, reflect quality drops from 94.1 to 85.3. [source]() - **VENDOR-REPORTED**: Against three other 0.8B models, its mean across eight benchmarks is 75.6, ahead of 74.5, 73.4 and 66.6. On the public Decision Index it trails StartLux-Decision 0.8B Instant, 36.1 to 53.8. [source]() ## Links - [Source: Launch and evaluation]() - [Source: Weights and model card]() - [Source: Hindsight code]() - [Source: System evaluation]() ## Related projects ### Hindsight agent memory [Hindsight agent memory]() [Source: vectorize-io/hindsight]() Hindsight uses [hs-decide-1-flash]() to prune and deduplicate recall, check whether evidence answers a request, consolidate new facts and decide whether a memory could change a knowledge page. It first tried this with [Jev](). In the team's own Hindsight 1.2.0 evaluation, glm-5.3-flash plus the decision model scores 87.1 at $7.72 per thousand cases, against 89.0 at $147 for gemini-3.8-flash alone. Reflect quality falls with some LLMs. gemini-3.1-flash-lite judges all runs and also appears in the comparison. - Creator: Vectorize - Stars: 58,477 - Licence: MIT - Model: [hs-decide-1-flash]() - Tags: memory, agents Authors' published runs · VENDOR-REPORTED; not independently reproduced - [Source: Launch and evaluation]() - [Source: MIT licence]()