Model comparison
Mistral Small 3.1 vs Qwen3.7 Max
Qwen3.7 Max is the stronger model overall, scoring 51.5 to 31.7 on the Noometry Index. Mistral Small 3.1 costs 9.3× less per token, which makes it the better buy when Qwen3.7 Max's lead doesn't matter for your workload.
Last verified . 16 shared benchmarks.
Mistral AI
Alibaba (Qwen)Summary
- They share 16 benchmarks with published results for both. Mistral Small 3.1 scores higher in 0 categories and Qwen3.7 Max in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3.7 Max leads 62.4 to 14.7.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 3.9% for Mistral Small 3.1 and 95.6% for Qwen3.7 Max.
- Mistral Small 3.1 is cheaper at $0.35 / $0.56 per million input/output tokens, against $2.50 / $7.50 for Qwen3.7 Max.
- Qwen3.7 Max accepts more context: 1M tokens versus 128K.
- Mistral Small 3.1 has downloadable open weights; the other is API-only.
Side by side
| Mistral Small 3.1 | Qwen3.7 Max | |
|---|---|---|
| Provider | Mistral AI | Alibaba (Qwen) |
| Noometry Index | 31.7 | 51.5 |
| Released | 2025-03-17 | 2026-05-19 |
| Weights | Open | Proprietary |
| Context window | 128K | 1M |
| Max output | 102K | 131K |
| Input $ / M tokens | $0.35 | $2.50 |
| Output $ / M tokens | $0.56 | $7.50 |
| Results tracked | 28 | 33 |
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Category by category
Coding Qwen3.7 Max leads
Mistral Small 3.1: 38.3 (#179), Qwen3.7 Max: 50.4 (#45)
| Benchmark | Mistral Small 3.1 | Qwen3.7 Max |
|---|---|---|
| LMArena Coding | 1309 | 1498 |
| SWE-bench Verified | — | 77.3% |
| LMArena WebDev | — | 1515 |
| SciCode | — | 48.8% |
| ALE-Bench | — | 1,189 |
Agentic & Tool Use Not comparable
Mistral Small 3.1: —, Qwen3.7 Max: 22.1 (#135)
| Benchmark | Mistral Small 3.1 | Qwen3.7 Max |
|---|---|---|
| GBAEval | — | 0.4% |
Reasoning Qwen3.7 Max leads
Mistral Small 3.1: 19.7 (#254), Qwen3.7 Max: 49.2 (#38)
| Benchmark | Mistral Small 3.1 | Qwen3.7 Max |
|---|---|---|
| Chess Puzzles | 1% | 19% |
| LMArena Hard Prompts | 1278 | 1483 |
| Epoch Capabilities Index | 127.48 | 153.68 |
| SimpleBench | — | 70.4% |
| NYT Connections (extended) | — | 85.1% |
| CritPt | — | 13.4% |
| EBR-Bench | — | 9.5% |
| Mystery Game Puzzles | — | 32% |
| DTBench | — | 92.3% |
| LMCA | — | 44% |
Math Qwen3.7 Max leads
Mistral Small 3.1: 14.7 (#301), Qwen3.7 Max: 62.4 (#32)
| Benchmark | Mistral Small 3.1 | Qwen3.7 Max |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 3.9% | 95.6% |
| LMArena Math | 1262 | 1490 |
| FrontierMath (Tiers 1-3) | — | 64.6% |
| FrontierMath Tier 4 | — | 34.1% |
| ProofBench | — | 26% |
| Omni-MATH | 24.8% | — |
Knowledge Qwen3.7 Max leads
Mistral Small 3.1: 22.6 (#271), Qwen3.7 Max: 61.6 (#28)
| Benchmark | Mistral Small 3.1 | Qwen3.7 Max |
|---|---|---|
| GPQA Diamond | 41.9% | 90.9% |
| LMArena Expert | 1257 | 1488 |
| SimpleQA Verified | — | 55.8% |
| MMLU-Pro | 61% | — |
| GPQA (HELM) | 39.2% | — |
Multimodal Not comparable
Mistral Small 3.1: 33.2 (#99), Qwen3.7 Max: —
| Benchmark | Mistral Small 3.1 | Qwen3.7 Max |
|---|---|---|
| LMArena Vision | 1136 | — |
Multilingual Qwen3.7 Max leads
Mistral Small 3.1: 41.2 (#209), Qwen3.7 Max: 56.9 (#15)
| Benchmark | Mistral Small 3.1 | Qwen3.7 Max |
|---|---|---|
| LMArena Non-English | 1255 | 1474 |
| LMArena Chinese | 1253 | 1530 |
| LMArena Russian | 1263 | 1484 |
| LMArena French | 1273 | — |
| LMArena German | 1266 | — |
| LMArena Japanese | 1208 | — |
| LMArena Korean | 1206 | — |
| LMArena Spanish | 1283 | — |
Instruction Following Qwen3.7 Max leads
Mistral Small 3.1: 63.6 (#230), Qwen3.7 Max: 76.7 (#38)
| Benchmark | Mistral Small 3.1 | Qwen3.7 Max |
|---|---|---|
| LMArena Instruction Following | 1264 | 1460 |
| IFEval | 75% | — |
Long Context Qwen3.7 Max leads
Mistral Small 3.1: 39.5 (#178), Qwen3.7 Max: 45.4 (#40)
| Benchmark | Mistral Small 3.1 | Qwen3.7 Max |
|---|---|---|
| LMArena Longer Query | 1299 | 1482 |
Writing & Preference Qwen3.7 Max leads
Mistral Small 3.1: 37.0 (#259), Qwen3.7 Max: 65.0 (#54)
| Benchmark | Mistral Small 3.1 | Qwen3.7 Max |
|---|---|---|
| LMArena Text | 1277 | 1476 |
| LMArena Creative Writing | 1253 | 1449 |
| LMArena Multi-Turn | 1270 | 1481 |
| EQ-Bench Creative Writing | 761 | — |
| WildBench | 78.8% | — |
| EQ-Bench 4 | — | 1110 |
Frequently asked questions
Is Mistral Small 3.1 better than Qwen3.7 Max?
Qwen3.7 Max is the stronger model overall, scoring 51.5 to 31.7 on the Noometry Index. Mistral Small 3.1 costs 9.3× less per token, which makes it the better buy when Qwen3.7 Max's lead doesn't matter for your workload.
Which is cheaper, Mistral Small 3.1 or Qwen3.7 Max?
Mistral Small 3.1 is cheaper. It lists at $0.35 per million input tokens and $0.56 per million output tokens; Qwen3.7 Max lists at $2.50 and $7.50.
Is Mistral Small 3.1 or Qwen3.7 Max better for coding?
Qwen3.7 Max scores higher on coding benchmarks: 50.4 versus 38.3 in the Noometry coding category.
Which has the bigger context window?
Qwen3.7 Max does, with 1M tokens against 128K.
How many benchmarks do Mistral Small 3.1 and Qwen3.7 Max share?
16 benchmarks have published results for both models. Mistral Small 3.1 has 28 scored results on Noometry and Qwen3.7 Max has 33.