Model comparison
Codestral vs Qwen3.7 Flash
Qwen3.7 Flash is the stronger model overall, scoring 39.9 to 30.6 on the Noometry Index.
Last verified . 0 shared benchmarks.
Mistral AI
Alibaba (Qwen)Summary
- The widest gap is in reasoning, where Qwen3.7 Flash leads 28.2 to 19.8.
- Qwen3.7 Flash is cheaper at $0.03 / $0.13 per million input/output tokens, against $0.30 / $0.90 for Codestral.
- Qwen3.7 Flash accepts more context: 1M tokens versus 256K.
Side by side
| Codestral | Qwen3.7 Flash | |
|---|---|---|
| Provider | Mistral AI | Alibaba (Qwen) |
| Noometry Index | 30.6 | 39.9 |
| Released | 2024-05-29 | 2026-07-15 |
| Weights | Proprietary | Proprietary |
| Context window | 256K | 1M |
| Max output | 8K | 131K |
| Input $ / M tokens | $0.30 | $0.03 |
| Output $ / M tokens | $0.90 | $0.13 |
| Results tracked | 7 | 7 |
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Category by category
Coding Not comparable
Codestral: 27.3 (#321), Qwen3.7 Flash: —
| Benchmark | Codestral | Qwen3.7 Flash |
|---|---|---|
| Aider Polyglot | 11.1% | — |
| BigCodeBench Instruct | 41.8% | — |
| BigCodeBench Complete | 52.5% | — |
| ALE-Bench | 137.78 | — |
| HumanEval+ | 73.8% | — |
| MBPP+ | 61.9% | — |
Reasoning Qwen3.7 Flash leads
Codestral: 19.8 (#251), Qwen3.7 Flash: 28.2 (#108)
| Benchmark | Codestral | Qwen3.7 Flash |
|---|---|---|
| Kagi LLM Benchmark | 32.5% | — |
| NYT Connections (extended) | — | 43.8% |
| Chess Puzzles | — | 23% |
| Mystery Game Puzzles | — | 15% |
| Epoch Capabilities Index | — | 144.64 |
Math Not comparable
Codestral: —, Qwen3.7 Flash: 38.3 (#140)
| Benchmark | Codestral | Qwen3.7 Flash |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 19.3% |
| OTIS Mock AIME 2024-2025 | — | 86.7% |
Knowledge Not comparable
Codestral: —, Qwen3.7 Flash: 48.9 (#75)
| Benchmark | Codestral | Qwen3.7 Flash |
|---|---|---|
| GPQA Diamond | — | 82.3% |
Frequently asked questions
Is Codestral better than Qwen3.7 Flash?
Qwen3.7 Flash is the stronger model overall, scoring 39.9 to 30.6 on the Noometry Index.
Which is cheaper, Codestral or Qwen3.7 Flash?
Qwen3.7 Flash is cheaper. It lists at $0.03 per million input tokens and $0.13 per million output tokens; Codestral lists at $0.30 and $0.90.
Which has the bigger context window?
Qwen3.7 Flash does, with 1M tokens against 256K.
How many benchmarks do Codestral and Qwen3.7 Flash share?
0 benchmarks have published results for both models. Codestral has 7 scored results on Noometry and Qwen3.7 Flash has 7.