Best Decision Models, Ranked
A decision model answers typed questions (choose one option, score on a rubric, yes or no) with a probability for each allowed answer instead of generating text. Models are ranked by their Full score on the Jev Decision Index, a third-party board of 37 benchmarks with private held-out sets, until scores from our own Metatext Decision Benchmark are published.
Ranked list (top 25)
| # | Model | Decision Index | ECE | p50 latency | Per 1k decisions | Params |
|---|---|---|---|---|---|---|
| 1 | Fastino GLiDE Fastino | 71.5 | 0.020 | 93 ms | $0.030 | 28B |
| 2 | Lychee-27B LycheeAIOpen weights | 67.3 | 0.108 | 97 ms | — | 26B |
| 3 | vLLM-SR d3 (27B) vLLM Semantic RouterOpen weights | 67.2 | 0.043 | 97 ms | — | 26B |
| 4 | linne-oak-1-0 MISA-AICOREOpen weights | 66.9 | 0.105 | 105 ms | — | 31B |
| 5 | Decider 31B (NVFP4) MapikaOpen weights | 66.0 | 0.024 | 72 ms | — | 33B |
| 6 | bobcat-1.3 sanghwa-naOpen weights | 64.8 | 0.102 | 64 ms | — | 31B |
| 7 | amos-1-large Scira (mukaddamzaid)Open weights | 64.8 | 0.032 | 148 ms | — | — |
| 8 | frontier-infra Jebadiah 27B v2.1 Frontier InfraOpen weights | 64.4 | 0.056 | 303 ms | — | 28B |
| 9 | Sieve-31B Sthānika AIOpen weights | 64.3 | 0.033 | 253 ms | — | 31B |
| 10 | AnyJev L0 · Gemma-4-31B morriszjmOpen weights | 63.6 | 0.240 | 98 ms | — | 31B |
| 11 | deck31b (Gemma 4 31B) krishna-gogineni-765Open weights | 63.0 | 0.204 | 219 ms | — | 33B |
| 12 | Darwin-27B-ZTC FINAL_BenchOpen weights | 63.0 | 0.045 | 104 ms | — | 26B |
| 13 | KnowLine-35B-A3B-Gen1 PelaAIOpen weights | 62.9 | 0.048 | 41 ms | — | 36B |
| 14 | Perplexity Decider v1.1 (27B) PerplexityOpen weights | 62.8 | 0.071 | 104 ms | $0.0040 | 28B |
| 15 | decisio 0.6.0 (Gemma 4 31B) aminryOpen weights | 62.7 | 0.142 | 54 ms | — | 31B |
| 16 | Decider chat · Gemma-4-31B MapikaOpen weights | 61.6 | 0.047 | 109 ms | — | 33B |
| 17 | Torchcast Decision 27B Torchcast AIOpen weights | 61.5 | 0.024 | 100 ms | — | 28B |
| 18 | Qwen Decision 27B noncausal LoRA jsaurabhOpen weights | 61.1 | 0.051 | 127 ms | — | 28B |
| 19 | Quyet-1.0-Large chinhncOpen weights | 60.8 | 0.078 | 105 ms | — | 33B |
| 20 | Jev TypeSafe AI | 60.2 | 0.080 | 196 ms | $0.0084 | — |
| 21 | Sieve 27B Sthānika AIOpen weights | 60.2 | 0.065 | 101 ms | — | 28B |
| 22 | OneJev1.5-27B OmniJevOpen weights | 59.9 | 0.024 | 224 ms | — | 27B |
| 23 | mogan-decision-31B-it Mogan AIOpen weights | 59.8 | 0.054 | 109 ms | — | 31B |
| 24 | Shisa DE-2 (repeat) Shisa.AIOpen weights | 59.8 | 0.207 | 57 ms | — | 26B |
| 25 | linne-fern 1.0 (27B) MISA-AICOREOpen weights | 59.6 | 0.079 | 108 ms | — | 28B |
Scores, calibration (ECE) and latency from the Jev Decision Index; cost per 1,000 decisions at 200 input tokens each. Updated daily.
More decision-model rankings
Frequently asked questions
What is the best decision model?
On the Jev Decision Index, Fastino GLiDE ranks first with a Full score of 71.5.
How is a decision model different from an LLM?
An LLM generates text. A decision model returns a probability distribution over the allowed answers for each typed question, so its output can be thresholded, audited and calibrated without parsing text.
Is the most accurate decision model the best choice?
Not always. Calibration (how far stated confidence is from the real hit rate), latency and cost per 1,000 decisions often matter as much; see the calibration and cost rankings.