Model comparison
Devstral Small 2505 vs Qwen3.8 Max
Qwen3.8 Max is the stronger model overall, scoring 56.8 to 34.3 on the Noometry Index. Devstral Small 2505 costs 20× less per token, which makes it the better buy when Qwen3.8 Max's lead doesn't matter for your workload.
Last verified . 2 shared benchmarks.
Mistral AI
Alibaba (Qwen)Summary
- They share 2 benchmarks with published results for both. Devstral Small 2505 scores higher in 0 categories and Qwen3.8 Max in 2 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Qwen3.8 Max leads 54.4 to 19.7.
- The biggest single-benchmark swing is SciCode: 28.8% for Devstral Small 2505 and 53.2% for Qwen3.8 Max.
- Devstral Small 2505 is cheaper at $0.10 / $0.30 per million input/output tokens, against $2 / $6 for Qwen3.8 Max.
- Qwen3.8 Max accepts more context: 1M tokens versus 128K.
- Devstral Small 2505 has downloadable open weights; the other is API-only.
Side by side
| Devstral Small 2505 | Qwen3.8 Max | |
|---|---|---|
| Provider | Mistral AI | Alibaba (Qwen) |
| Noometry Index | 34.3 | 56.8 |
| Released | 2025-05-07 | 2026-08-02 |
| Weights | Open | Proprietary |
| Context window | 128K | 1M |
| Max output | 128K | 131K |
| Input $ / M tokens | $0.10 | $2 |
| Output $ / M tokens | $0.30 | $6 |
| Results tracked | 4 | 39 |
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Category by category
Coding Qwen3.8 Max leads
Devstral Small 2505: 38.9 (#166), Qwen3.8 Max: 53.5 (#29)
| Benchmark | Devstral Small 2505 | Qwen3.8 Max |
|---|---|---|
| SciCode | 28.8% | 53.2% |
| DeepSWE | — | 57.5% |
| SWE-bench Verified (bash only) | 56.4% | — |
| LMArena WebDev | — | 1674 |
| FrontierSWE | — | 17.8% |
| LMArena Coding | — | 1502 |
Agentic & Tool Use Not comparable
Devstral Small 2505: —, Qwen3.8 Max: 45.4 (#14)
| Benchmark | Devstral Small 2505 | Qwen3.8 Max |
|---|---|---|
| APEX-Agents | — | 63.3% |
| τ²-bench Banking | — | 55.1% |
| GDP.pdf | — | 23.2% |
Reasoning Qwen3.8 Max leads
Devstral Small 2505: 19.7 (#252), Qwen3.8 Max: 54.4 (#26)
| Benchmark | Devstral Small 2505 | Qwen3.8 Max |
|---|---|---|
| CritPt | 0% | 20% |
| Kagi LLM Benchmark | 37.7% | — |
| NYT Connections (extended) | — | 88.3% |
| Chess Puzzles | — | 40% |
| LMArena Hard Prompts | — | 1496 |
| Mystery Game Puzzles | — | 38% |
| DTBench | — | 92% |
| LMCA | — | 46.2% |
| Epoch Capabilities Index | — | 156.41 |
Math Not comparable
Devstral Small 2505: —, Qwen3.8 Max: 73.2 (#20)
| Benchmark | Devstral Small 2505 | Qwen3.8 Max |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 74.7% |
| FrontierMath Tier 4 | — | 46.3% |
| OTIS Mock AIME 2024-2025 | — | 100% |
| ProofBench | — | 58% |
| LMArena Math | — | 1499 |
Knowledge Not comparable
Devstral Small 2505: —, Qwen3.8 Max: 61.7 (#27)
| Benchmark | Devstral Small 2505 | Qwen3.8 Max |
|---|---|---|
| GPQA Diamond | — | 92.7% |
| SimpleQA Verified | — | 47.3% |
| LMArena Expert | — | 1507 |
Multimodal Not comparable
Devstral Small 2505: —, Qwen3.8 Max: 37.2 (#75)
| Benchmark | Devstral Small 2505 | Qwen3.8 Max |
|---|---|---|
| LMArena Vision | — | 1314 |
| Furniture Assembly | — | 20% |
Multilingual Not comparable
Devstral Small 2505: —, Qwen3.8 Max: 56.7 (#18)
| Benchmark | Devstral Small 2505 | Qwen3.8 Max |
|---|---|---|
| LMArena Non-English | — | 1472 |
| LMArena Chinese | — | 1538 |
| LMArena French | — | 1503 |
| LMArena German | — | 1483 |
| LMArena Japanese | — | 1467 |
| LMArena Korean | — | 1461 |
| LMArena Russian | — | 1481 |
| LMArena Spanish | — | 1492 |
Instruction Following Not comparable
Devstral Small 2505: —, Qwen3.8 Max: 77.6 (#17)
| Benchmark | Devstral Small 2505 | Qwen3.8 Max |
|---|---|---|
| LMArena Instruction Following | — | 1479 |
Long Context Not comparable
Devstral Small 2505: —, Qwen3.8 Max: 45.6 (#31)
| Benchmark | Devstral Small 2505 | Qwen3.8 Max |
|---|---|---|
| LMArena Longer Query | — | 1489 |
Writing & Preference Not comparable
Devstral Small 2505: —, Qwen3.8 Max: 67.1 (#30)
| Benchmark | Devstral Small 2505 | Qwen3.8 Max |
|---|---|---|
| LMArena Text | — | 1483 |
| LMArena Creative Writing | — | 1479 |
| LMArena Multi-Turn | — | 1489 |
Frequently asked questions
Is Devstral Small 2505 better than Qwen3.8 Max?
Qwen3.8 Max is the stronger model overall, scoring 56.8 to 34.3 on the Noometry Index. Devstral Small 2505 costs 20× less per token, which makes it the better buy when Qwen3.8 Max's lead doesn't matter for your workload.
Which is cheaper, Devstral Small 2505 or Qwen3.8 Max?
Devstral Small 2505 is cheaper. It lists at $0.10 per million input tokens and $0.30 per million output tokens; Qwen3.8 Max lists at $2 and $6.
Is Devstral Small 2505 or Qwen3.8 Max better for coding?
Qwen3.8 Max scores higher on coding benchmarks: 53.5 versus 38.9 in the Noometry coding category.
Which has the bigger context window?
Qwen3.8 Max does, with 1M tokens against 128K.
How many benchmarks do Devstral Small 2505 and Qwen3.8 Max share?
2 benchmarks have published results for both models. Devstral Small 2505 has 4 scored results on Noometry and Qwen3.8 Max has 39.