whatmodel.sh / Mistral Large (2402)
Mistral Large (2402)
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Mistral Large (2402) is a closed-weights language model from Mistral, released February 2024. Licence: proprietary.
Catalogue identity notes
Comparison model from the Epoch AI internal-evaluation CSVs (Epoch release date 2024-02-26). API model mistral-large-2402 ('Mistral Large 1.0', version 24.02 in Mistral's legacy-models table; retired 6/16/2025). No open weights.
$ how it compares
The LLMs ranked either side of Mistral Large (2402), out of 828 with a score, then more from Mistral.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 488 |  Granite 4.0 H Micro 3B | 47±1 | local | ||
| 489 |  GPT-3.5 Turbo (0125) | 46±1 | api $0.75/M | ||
| 490 | Mistral Large (2402) | 46±1 | comparison only | ||
| 491 |  Jamba 1.5 Mini | 46±1 | local | ||
| 492 |  MedGemma 4B IT | 46±3 | local | ||
| more from Mistral | |||||
| 79 | Mistral Large 4 (public preview) | 71±2 | api $2.06/M | ||
| 174 | Mistral Medium 3.5 (128B) | 65±1 | api $3.00/M +local | ||
| 180 | Mistral Medium 3.1 (2508) | 64±2 | comparison only | ||
$ what it's good at
coding▁▁▁▃▅▇█▇▅▄▃▁ #370 of 524 · 46% of its score · 3 tests
Beats GLM-4.7-Flash, Qwen3-VL 8B Instruct, Qwen3-VL 4B Instruct here, though they rank higher overall. Trails DeepSeek LLM 67B Chat, WizardLM 70B V1.0, Phi-4-mini 3.8B here, though they rank lower overall.
chat & writing▁▁▁▃▅▄▆█▇▇▇▃ #332 of 463 · 24% of its score · 4 tests
Beats Ministral 3 14B Reasoning 2512, Gemma 4 E4B, Ministral 3 8B here, though they rank higher overall. Trails Flan-T5 XXL, LFM2 700M, EXAONE 3.5 2.4B Instruct here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| AlpacaEval 2 length-controlled | 32.6% ✎ | ▁▂▂▃▆▆█▆▄▄▁▁ beats 85/122 | 11% |
| Arena-Hard (v0.1, GPT-4-1106 judge) | 37.7% ✎ | ▂▁▁▅█▅▄▃▃▅▃▁ beats 32/60 | 7.6% |
| Polyrating rating (bias-aware re-fit of Chatbot Arena votes, mid-2024) | 1163.0 ✎ | ▂▁▂▁▃▇█▆▇▄▂▂ beats 72/95 | 3.0% |
| LMArena (text) | 1242.5 ✎ | ▁▁▁▂▃▃▃▆▆▇█▃ beats 91/379 | 2.9% |
math▁▂▃▅▇▅▅█▄▁▁▁ #354 of 462 * · 5.0% of its score · 2 tests
Beats GPT-4 (0613), Athene-70B, Llama 3 70B Instruct here, though they rank higher overall. Trails Gemma 7B (base), DeepSeek R1 Distill Qwen 1.5B, CodeGemma 7B (v1.0) here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| MATH level 5 | 24.5% ✎ | ▂▂▅▆▄█▆▃▅▂▃▄ beats 22/92 | 4.6% |
| OTIS mock AIME 2024–2025 | 1.9% ✎ | ▃▃▄▃▃▃▇▇█▃▂▅ beats 14/193 | 0.5% |
other tests5 not in a task · 24% of its score
| test | result | vs the others in its run | counts |
|---|---|---|---|
| LiveBench 2024-07-26 | 39.2% ✎ | ▁▁▁▂▂▃▂█▆▃▅▃ beats 43/61 | 12% |
| BigCodeBench (Instruct) | 29.6% ✎ | ▃▂·▆▆██▆▅▃▃▃ beats 6/33 | 3.6% |
| BigCodeBench (Complete) | 37.9% ✎ | ▂▁▁▄▂▅▇█▆▂▂▂ beats 21/58 | 3.5% |
| WildBench v2 WB-Score (task macro) | 38.8937 WB-Score ✎ | ▁·▁▁·▃█▄▇▆▅▆ beats 27/52 | 3.3% |
| GPQA Diamond | 38.8% ✎ | ▁▁▁▁▂▄▆▄▅▇█▅ beats 36/208 | 2.5% |
$ where to run it
No verified API or download recorded yet · add one
$ how the score was worked out · EvalPlus / EvalPerf published… 46%, LiveBench model judgments (HF… 12%, AlpacaEval 1.0 and 2.0 leader… 11% and 6 more
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
$ improve this page
Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.
data updated · sources and licences · json