Model comparison
Codestral vs Qwen2.5-Max
Qwen2.5-Max is the stronger model overall, scoring 40.7 to 30.6 on the Noometry Index.
Last verified . 0 shared benchmarks.
Mistral AI
Alibaba (Qwen)Summary
- The widest gap is in coding, where Qwen2.5-Max leads 41.8 to 27.3.
Side by side
| Codestral | Qwen2.5-Max | |
|---|---|---|
| Provider | Mistral AI | Alibaba (Qwen) |
| Noometry Index | 30.6 | 40.7 |
| Released | 2024-05-29 | 2025-01-25 |
| Weights | Proprietary | Proprietary |
| Context window | 256K | — |
| Max output | 8K | — |
| Input $ / M tokens | $0.30 | — |
| Output $ / M tokens | $0.90 | — |
| Results tracked | 7 | 27 |
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Category by category
Coding Qwen2.5-Max leads
Codestral: 27.3 (#321), Qwen2.5-Max: 41.8 (#117)
| Benchmark | Codestral | Qwen2.5-Max |
|---|---|---|
| Aider Polyglot | 11.1% | — |
| BigCodeBench Instruct | 41.8% | — |
| LiveBench Coding | — | 64.4% |
| LMArena Coding | — | 1359 |
| BigCodeBench Complete | 52.5% | — |
| ALE-Bench | 137.78 | — |
| HumanEval+ | 73.8% | — |
| MBPP+ | 61.9% | — |
Reasoning Qwen2.5-Max leads
Codestral: 19.8 (#251), Qwen2.5-Max: 25.6 (#147)
| Benchmark | Codestral | Qwen2.5-Max |
|---|---|---|
| Kagi LLM Benchmark | 32.5% | — |
| LiveBench Reasoning | — | 51.4% |
| LMArena Hard Prompts | — | 1360 |
| LiveBench Data Analysis | — | 67.9% |
| Epoch Capabilities Index | — | 132.53 |
| LiveBench | — | 62.3% |
Math Not comparable
Codestral: —, Qwen2.5-Max: 36.9 (#162)
| Benchmark | Codestral | Qwen2.5-Max |
|---|---|---|
| LiveBench Math | — | 58.4% |
| LMArena Math | — | 1369 |
Knowledge Not comparable
Codestral: —, Qwen2.5-Max: 35.3 (#186)
| Benchmark | Codestral | Qwen2.5-Max |
|---|---|---|
| Confabulations | — | 21.8% |
| LMArena Expert | — | 1337 |
Multilingual Not comparable
Codestral: —, Qwen2.5-Max: 48.1 (#146)
| Benchmark | Codestral | Qwen2.5-Max |
|---|---|---|
| LMArena Non-English | — | 1352 |
| LMArena Chinese | — | 1382 |
| LMArena French | — | 1396 |
| LMArena German | — | 1350 |
| LMArena Japanese | — | 1300 |
| LMArena Korean | — | 1304 |
| LMArena Russian | — | 1353 |
| LMArena Spanish | — | 1377 |
Instruction Following Not comparable
Codestral: —, Qwen2.5-Max: 71.3 (#152)
| Benchmark | Codestral | Qwen2.5-Max |
|---|---|---|
| LiveBench Instruction Following | — | 75.3% |
| LMArena Instruction Following | — | 1335 |
Long Context Not comparable
Codestral: —, Qwen2.5-Max: 41.4 (#142)
| Benchmark | Codestral | Qwen2.5-Max |
|---|---|---|
| LMArena Longer Query | — | 1358 |
Writing & Preference Not comparable
Codestral: —, Qwen2.5-Max: 55.4 (#146)
| Benchmark | Codestral | Qwen2.5-Max |
|---|---|---|
| LMArena Text | — | 1367 |
| LMArena Creative Writing | — | 1339 |
| Short-Story Creative Writing | — | 72.9% |
| LMArena Multi-Turn | — | 1364 |
| LiveBench Language | — | 56.3% |
Frequently asked questions
Is Codestral better than Qwen2.5-Max?
Qwen2.5-Max is the stronger model overall, scoring 40.7 to 30.6 on the Noometry Index.
Is Codestral or Qwen2.5-Max better for coding?
Qwen2.5-Max scores higher on coding benchmarks: 41.8 versus 27.3 in the Noometry coding category.
How many benchmarks do Codestral and Qwen2.5-Max share?
0 benchmarks have published results for both models. Codestral has 7 scored results on Noometry and Qwen2.5-Max has 27.