OPEN
Smaug-Decisions
Smaug-Decisions is Abacus.AI's open 4B model for audio, images and text. Its family includes a 0.7B audio tier and a LiteRT build for phones.
By Abacus.AI · Maker blog announcement: 2 Nov 2026
Abacus.AI's 4B decision model answers a typed question about audio, an image or text with a probability per option in one pass. It is a Gemma 4 E4B-it fine-tune. The family includes a 744M audio model and a LiteRT build for phones. Each 4B request takes one audio clip up to 30 seconds or one image, with up to 20 options.
Facts
- Size
- 4B effective, 8.6B stored; 744M audio tier
- Base
- google/gemma-4-E4B-it; google/embeddinggemma-2 for the audio tier
- Licence
- Apache-2.0
- Inputs
- Audio, images and text; 2 to 20 options
- Formats
- Transformers; LiteRT-LM; Core ML int8 for the audio tier
Claims and sources
- On Abacus.AI's 6,671-item Earshot benchmark, the 4B scores 60.1 audio skill with ECE 0.069. Gemini 4 Argon scores 59.4 with ECE 0.123, and Gemini 3.8 Flash 50.5 with ECE 0.204. The 0.7B audio tier scores 53.0 with ECE 0.086. vendor reportedsource
- Using the 0.7B audio tier first and escalating below 0.9 matches the 4B's 60.1 audio skill at 130 ms mean latency instead of 210 ms in Abacus.AI's run. vendor reportedsource
- Abacus.AI reports 57.9 on public Decision Index images and 56.6 on text. These are its own runs, not leaderboard submissions. vendor reportedsource
- The 3.45 GB LiteRT build scores 59.4 audio skill in a PyTorch replica of its quantized weights. Abacus.AI measures about 0.6 seconds per text decision on an iPhone 14 Pro Max. vendor reportedsource
Links
Source: Weights and model card → Source: 0.7B audio tier → Source: LiteRT build → Source: Maker blog announcement → Source: X announcement: 3 Nov 2026 →
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