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Codestral (25.01)
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Codestral (25.01) is a closed-weights language model from Mistral, released January 2025. 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 codestral-2501 ('Codestral' version 25.01 in Mistral's legacy-models table: deprecated 11/6/2025, retired 11/30/2025). The Aider run (2025-01-13, label 'Codestral 25.01') called codestral-latest on Codestral 25.01's launch day, when the alias pointed to 25.01. No open weights for 25.01 (distinct from the open Codestral-22B-v0.1).
$ how it compares
The LLMs ranked either side of Codestral (25.01), out of 1132 with a score, then more from Mistral.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 528 |  Qwen2.5 14B Instruct | 52±1 | local | ||
| 529 |  Gemini Experimental 1121 | 52±2 | comparison only | ||
| 530 | Codestral (25.01) | 52±3 | comparison only | ||
| 531 |  Jan v1 4B | 52±7 | local | ||
| 532 |  Cogito v1 Preview Llama 8B | 51±5 | local | ||
| more from Mistral | |||||
| 88 | Mistral Large 4 (public preview) | 71±2 | api $2.06/M | ||
| 204 | Mistral Medium 3.5 (128B) | 65±1 | api $3.00/M +local | ||
| 212 | Mistral Medium 3.1 (2508) | 64±2 | comparison only | ||
$ what it's good at
coding▁▁▁▃▅▇█▇▅▄▂▁ #313 of 549 * · 96% of its score · 1 test
Beats Qwen3-VL 32B Instruct, Solar Open 100B (102B-A12B), GLM-4.7-Flash here, though they rank higher overall. Trails DeepSeek LLM 67B Chat, QwQ-32B-Preview, InternLM2.5 20B Chat here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| Aider polyglot (225 exercises, two attempts) | 11.1% ✎ | ▂·▃▃▅▂▃█▆▄▂▂ beats 4/49 | 96% |
$ where to run it
No verified API or download recorded yet · add one
$ how the score was worked out · Aider polyglot benchmark runs 96%
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
| source | results | share of the score |
|---|---|---|
 Aider polyglot benchmark runs | 1 | 96% |
| prior · pull toward the middle | 4% | |
| weighted average | = 51.5 |
$ 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