Model comparison
Codestral vs Qwen3.5 397B-A17B
Qwen3.5 397B-A17B is the stronger model overall, scoring 46.0 to 30.6 on the Noometry Index. Codestral costs 3.0× less per token, which makes it the better buy when Qwen3.5 397B-A17B's lead doesn't matter for your workload.
Last verified . 1 shared benchmarks.
Mistral AI
Alibaba (Qwen)Summary
- They share 1 benchmark with published results for both. Codestral scores higher in 0 categories and Qwen3.5 397B-A17B in 2 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Qwen3.5 397B-A17B leads 34.5 to 19.8.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 32.5% for Codestral and 73.7% for Qwen3.5 397B-A17B.
- Codestral is cheaper at $0.30 / $0.90 per million input/output tokens, against $0.60 / $3.60 for Qwen3.5 397B-A17B.
- Qwen3.5 397B-A17B accepts more context: 262K tokens versus 256K.
- Qwen3.5 397B-A17B has downloadable open weights; the other is API-only.
Side by side
| Codestral | Qwen3.5 397B-A17B | |
|---|---|---|
| Provider | Mistral AI | Alibaba (Qwen) |
| Noometry Index | 30.6 | 46.0 |
| Released | 2024-05-29 | 2026-02-01 |
| Weights | Proprietary | Open |
| Context window | 256K | 262K |
| Max output | 8K | 66K |
| Input $ / M tokens | $0.30 | $0.60 |
| Output $ / M tokens | $0.90 | $3.60 |
| Results tracked | 7 | 36 |
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Category by category
Coding Qwen3.5 397B-A17B leads
Codestral: 27.3 (#321), Qwen3.5 397B-A17B: 42.0 (#114)
| Benchmark | Codestral | Qwen3.5 397B-A17B |
|---|---|---|
| Aider Polyglot | 11.1% | — |
| LMArena WebDev | — | 1400 |
| BigCodeBench Instruct | 41.8% | — |
| LMArena Coding | — | 1465 |
| BigCodeBench Complete | 52.5% | — |
| ALE-Bench | 137.78 | — |
| HumanEval+ | 73.8% | — |
| MBPP+ | 61.9% | — |
Agentic & Tool Use Not comparable
Codestral: —, Qwen3.5 397B-A17B: 33.3 (#53)
| Benchmark | Codestral | Qwen3.5 397B-A17B |
|---|---|---|
| APEX-Agents | — | 24.9% |
| τ²-bench Airline | — | 81.5% |
| τ²-bench Banking | — | 9.8% |
| τ²-bench Retail | — | 84.4% |
| τ²-bench Telecom | — | 97.8% |
Reasoning Qwen3.5 397B-A17B leads
Codestral: 19.8 (#251), Qwen3.5 397B-A17B: 34.5 (#70)
| Benchmark | Codestral | Qwen3.5 397B-A17B |
|---|---|---|
| Kagi LLM Benchmark | 32.5% | 73.7% |
| NYT Connections (extended) | — | 58.9% |
| Chess Puzzles | — | 13% |
| Thematic Generalization | — | 65.1% |
| LMArena Hard Prompts | — | 1448 |
| Mystery Game Puzzles | — | 18% |
| DTBench | — | 87.5% |
| LMCA | — | 37.9% |
| Epoch Capabilities Index | — | 146.65 |
Math Not comparable
Codestral: —, Qwen3.5 397B-A17B: 46.1 (#73)
| Benchmark | Codestral | Qwen3.5 397B-A17B |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 31.2% |
| OTIS Mock AIME 2024-2025 | — | 88.9% |
| LMArena Math | — | 1454 |
Knowledge Not comparable
Codestral: —, Qwen3.5 397B-A17B: 53.3 (#58)
| Benchmark | Codestral | Qwen3.5 397B-A17B |
|---|---|---|
| GPQA Diamond | — | 86.4% |
| LMArena Expert | — | 1462 |
Multimodal Not comparable
Codestral: —, Qwen3.5 397B-A17B: 40.7 (#44)
| Benchmark | Codestral | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Vision | — | 1263 |
Multilingual Not comparable
Codestral: —, Qwen3.5 397B-A17B: 53.7 (#59)
| Benchmark | Codestral | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Non-English | — | 1430 |
| LMArena Chinese | — | 1500 |
| LMArena French | — | 1461 |
| LMArena German | — | 1447 |
| LMArena Japanese | — | 1426 |
| LMArena Korean | — | 1384 |
| LMArena Russian | — | 1429 |
| LMArena Spanish | — | 1441 |
Instruction Following Not comparable
Codestral: —, Qwen3.5 397B-A17B: 75.0 (#77)
| Benchmark | Codestral | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Instruction Following | — | 1424 |
Long Context Not comparable
Codestral: —, Qwen3.5 397B-A17B: 44.1 (#74)
| Benchmark | Codestral | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Longer Query | — | 1442 |
Writing & Preference Not comparable
Codestral: —, Qwen3.5 397B-A17B: 62.3 (#79)
| Benchmark | Codestral | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Text | — | 1438 |
| LMArena Creative Writing | — | 1401 |
| EQ-Bench Creative Writing | — | 1478 |
| LMArena Multi-Turn | — | 1446 |
Frequently asked questions
Is Codestral better than Qwen3.5 397B-A17B?
Qwen3.5 397B-A17B is the stronger model overall, scoring 46.0 to 30.6 on the Noometry Index. Codestral costs 3.0× less per token, which makes it the better buy when Qwen3.5 397B-A17B's lead doesn't matter for your workload.
Which is cheaper, Codestral or Qwen3.5 397B-A17B?
Codestral is cheaper. It lists at $0.30 per million input tokens and $0.90 per million output tokens; Qwen3.5 397B-A17B lists at $0.60 and $3.60.
Is Codestral or Qwen3.5 397B-A17B better for coding?
Qwen3.5 397B-A17B scores higher on coding benchmarks: 42.0 versus 27.3 in the Noometry coding category.
Which has the bigger context window?
Qwen3.5 397B-A17B does, with 262K tokens against 256K.
How many benchmarks do Codestral and Qwen3.5 397B-A17B share?
1 benchmark has published results for both models. Codestral has 7 scored results on Noometry and Qwen3.5 397B-A17B has 36.