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Mistral Small (2402)

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Mistral Small (2402) is a closed-weights language model from Mistral, released February 2024. Licence: proprietary.

Catalogue identity notes

Comparison-only: retired or no longer listed API model, kept so its independent runs can be scored. Mistral API model mistral-small-2402 ('Mistral Small 1.0', version 24.02 in Mistral's legacy-models table: deprecated 11/30/2024, retired 6/16/2025). No open weights. Distinct from the open Mistral Small 2409/2501/2503/2506 checkpoints.

44.8±1.3comparison only: no current run option verifiedfrom 4 benchmark results across 3 sources

$ how it compares

The LLMs ranked either side of Mistral Small (2402), out of 828 with a score, then more from Mistral.

#modelscorewhereRAMreleased
533Phi-3-medium-4k-instruct Microsoft45±1local~9.8GB2024-05
534Qwen2-Math 1.5B Instruct Alibaba45±2local~2.4GB2024-08
535Mistral Small (2402) Mistral45±1comparison only2024-02
536Llama 3.1 8B Instruct Meta45±0api $0.22/M +local~6.2GB2024-07
537Command R Plus (04-2024) Cohere45±1local~43GB2024-04
more from Mistral
79Mistral Large 4 (public preview) Mistral71±2api $2.06/M2026-10
174Mistral Medium 3.5 (128B) Mistral65±1api $3.00/M +local~42GB2026-04
180Mistral Medium 3.1 (2508) Mistral64±2comparison only2025-08

see all LLMs · find the ones that fit your machine

$ what it's good at

agent tasks▁▁▂▆█▆▅▄▄▃▂▁ #306 of 340 * · 3.5% of its score · 1 test

Beats Magistral Small 1.0 (2506), Llama 4 Scout (109B-A17B), Bielik 11B v2.3 Instruct here, though they rank higher overall. Trails Granite 4.0 H 350M, Qwen3 0.6B, Qwen2.5 1.5B Instruct here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
BFCL v346.3% ✎▁▁▁▁▁▃▅█▅▄▁▁ beats 26/114medium · avg 52%▇ 3.5%blunt · at its level
other tests3 not in a task · 96% of its score
testresultvs the others in its rundifficultycountswhy
LiveBench 2024-07-2632.2% ✎▁▁▁▂▂▃▂█▆▃▅▃ beats 33/61medium · avg 41%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 59%at its level
BigCodeBench (Complete)39.0% ✎▂▁▁▄▂▅▇█▆▂▂▂ beats 25/58medium · avg 50%▇▇▇▇▇▇▇ 19%at its level
BigCodeBench (Instruct)31.8% ✎▃▂·▆▆██▆▅▃▃▃ beats 8/33medium · avg 41%▇▇▇▇▇▇▇ 18%at its level

$ where to run it

No verified API or download recorded yet · add one

$ how the score was worked out · LiveBench model judgments (HF… 59%, BigCodeBench paper (Zhuo et a… 37%, Berkeley Function Calling Lea… 3%

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
LiveBench model judgments (HF livebench/model_judgment, 2024-10-22 revision) with the LiveBench question datasets1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 59%Apache-2.0 (stated in the LiveBench datasheet)
BigCodeBench paper (Zhuo et al., ICLR 2025), arXiv 2406.15877v42▇▇▇▇▇▇▇▇▇▇▇ 37%CC BY 4.0
Berkeley Function Calling Leaderboard result archive1▇ 3%Apache-2.0
prior · pull toward the middle▇ 1%
weighted average= 44.8

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