Codestral 25.01: benchmark scores, price and ranking
Codestral 25.01 from Mistral AI ranks #452 of 743 LLMs on the LLMs Tiger aggregate benchmark score (47.2), based on 1 benchmark results captured up to 2026-10-13.
- Organisation
- Mistral AI
- Type
- Frontier model
- Open weights
- No
- Released
- 2025-01-13 (source: curated)
- Aggregate rank
- #452 of 743 (score 47.2)
Across the 1 benchmark where at least ten models are scored, Codestral 25.01 is in the top 10% on 0 and in the top half on 0.
Codestral 25.01 benchmark results
| Benchmark | Score | Rank | Source | Captured |
|---|---|---|---|---|
| Aider Polyglot | 11.1% | #45 of 49 | Aider polyglot leaderboard | 2026-10-13 |
About Codestral 25.01
Codestral 25.01 is a frontier model from Mistral AI. It was released on 2025-01-13, according to the developer's announcement. 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 Codestral 25.01 stands
On our aggregate score, Codestral 25.01 is #452 of 743 ranked models, in the lower half. Its score is 47.2; the scale is centred on 50, so it sits 2.8 points below the typical model. That beats 39% of the ranked field. Among the 44 ranked Mistral AI models it is #10. The next one up is Magistral Small at 47.4. The next one down is Magistral Medium 1.2 at 47.1.
Codestral 25.01, benchmark by benchmark
Aider Polyglot is a weak spot. Codestral 25.01 scores 11.1%, #45 of 49, behind 90% of the models measured. The leader, gemini-2.5-pro-preview-06-05, reaches 83.1%. It covers edits across 6 languages (225 exercises). The board median is 44.9%, so Codestral 25.01 is 33.8 points below it. Source: Aider polyglot leaderboard, captured 2026-10-13.
Release history
The Mistral AI model released before it was pixtral-large-2411 on 2024-11-18, which scores 48.3. The next one was mistral-small-2501 on 2025-01-25, at 44.8.
Reading these numbers
The 1 score on this page comes from 1 source: Aider polyglot leaderboard (1). 1 of them come from an official leaderboard and 0 from datasets or third-party runs. With 1 results the aggregate rank can move a lot when one new score arrives.
Models ranked nearby
- #449 gpt-5.6-terra-xhigh (codex-harness) (47.3)
- #450 Llama-3.2-3B-Instruct (47.2)
- #451 ollama/yi-coder:9b-chat-fp16 (47.2)
- #453 gpt-5.6-luna-xhigh (codex-harness) (47.2)
- #454 seed-2.1-pro-preview (47.2)
- #455 MAmmoTH2-8x7B-Plus (47.2)
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