mistral-small-2409: benchmark scores, price and ranking
mistral-small-2409 from Mistral AI ranks #532 of 743 LLMs on the LLMs Tiger aggregate benchmark score (46.3), based on 1 benchmark results captured up to 2026-10-13.
- Organisation
- Mistral AI
- Type
- Open-weight / local model
- Open weights
- Yes
- Released
- 2024-09-17 (source: epoch)
- Aggregate rank
- #532 of 743 (score 46.3)
Across the 1 benchmark where at least ten models are scored, mistral-small-2409 is in the top 10% on 0 and in the top half on 0.
mistral-small-2409 benchmark results
| Benchmark | Score | Rank | Source | Captured |
|---|---|---|---|---|
| DTBench | 49.5% | #133 of 146 | Epoch AI Benchmarking Hub | 2026-10-13 |
About mistral-small-2409
mistral-small-2409 is an open-weight model that can run on your own hardware from Mistral AI. It was released on 2024-09-17, according to Epoch AI. No API price is listed for it yet. We hold 1 benchmark result for it across 1 area, and none is in the top tenth of its board.
Where mistral-small-2409 stands
On our aggregate score, mistral-small-2409 is #532 of 743 ranked models, in the lower half. Its score is 46.3; the scale is centred on 50, so it sits 3.7 points below the typical model. That beats 28% of the ranked field. Among the 44 ranked Mistral AI models it is #19. The next one up is mistral-small-3.2-2506 at 46.4. The next one down is open-mixtral-8x22b at 46.0.
mistral-small-2409, benchmark by benchmark
DTBench is a weak spot. mistral-small-2409 scores 49.5%, #133 of 146, behind 90% of the models measured. The leader, Claude Opus 5.5 (max), reaches 98.9%. It covers dynamic tool-use benchmark. The board median is 68.8%, so mistral-small-2409 is 19.3 points below it. Source: Epoch AI Benchmarking Hub, captured 2026-10-13.
Release history
The Mistral AI model released before it was mistral-large-2407 on 2024-07-24, which scores 43.3. The next one was ministral-3b-2410 on 2024-10-16, at 41.3.
Reading these numbers
The 1 score on this page comes from 1 source: Epoch AI Benchmarking Hub (1). No official leaderboard supplied any of them, so treat them as third-party measurements. With 1 results the aggregate rank can move a lot when one new score arrives.
Models ranked nearby
- #529 qwen1.5-110b-chat (46.3)
- #530 Qwen2.5-3B (46.3)
- #531 GPT-5.4 mini (xhigh) (46.3)
- #533 MAmmoTH2-8B-Plus (46.2)
- #534 Yi-34B (46.2)
- #535 Qwen3.6 35B-A3B (46.2)
More from Mistral AI
- Mistral Large 2 (2407) (#231, 49.9)
- codestral-2405 (#352, 48.4)
- pixtral-large-2411 (#360, 48.3)
- Codestral-22B-v0.1-Q4_K_M (#392, 48.0)
- codestral:22b-v0.1-q8_0 (#393, 48.0)
- Mistral Large 4 (high) (#411, 47.7)
Compare it on the live leaderboard → · How the aggregate score works