Model comparison
Mistral Small 3.1 vs Qwen3.5-Flash
Qwen3.5-Flash is the stronger model overall, scoring 42.3 to 31.4 on the Noometry Index.
Last verified . 21 shared benchmarks.
Mistral AI
Alibaba (Qwen)Summary
- They share 21 benchmarks with published results for both. Mistral Small 3.1 scores higher in 1 category and Qwen3.5-Flash in 7 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3.5-Flash leads 36.3 to 13.7.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 3.9% for Mistral Small 3.1 and 84.4% for Qwen3.5-Flash.
- Qwen3.5-Flash is cheaper at $0.10 / $0.40 per million input/output tokens, against $0.35 / $0.56 for Mistral Small 3.1.
- Qwen3.5-Flash 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 | Qwen3.5-Flash | |
|---|---|---|
| Provider | Mistral AI | Alibaba (Qwen) |
| Noometry Index | 31.4 | 42.3 |
| Released | 2025-03-17 | 2026-02-23 |
| Weights | Open | Proprietary |
| Context window | 128K | 1M |
| Max output | 102K | 66K |
| Input $ / M tokens | $0.35 | $0.10 |
| Output $ / M tokens | $0.56 | $0.40 |
| Results tracked | 28 | 32 |
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Category by category
Coding Mistral Small 3.1 leads
Mistral Small 3.1: 37.7 (#175), Qwen3.5-Flash: 33.9 (#234)
| Benchmark | Mistral Small 3.1 | Qwen3.5-Flash |
|---|---|---|
| LMArena Coding | 1309 | 1412 |
| LMArena WebDev | — | 1243 |
| ALE-Bench | — | 221.8 |
Agentic & Tool Use Not comparable
Mistral Small 3.1: —, Qwen3.5-Flash: —
| Benchmark | Mistral Small 3.1 | Qwen3.5-Flash |
|---|---|---|
| Vending-Bench 2 | — | 462.69 |
Reasoning Qwen3.5-Flash leads
Mistral Small 3.1: 18.9 (#249), Qwen3.5-Flash: 32.9 (#63)
| Benchmark | Mistral Small 3.1 | Qwen3.5-Flash |
|---|---|---|
| Chess Puzzles | 1% | 21% |
| LMArena Hard Prompts | 1278 | 1404 |
| Epoch Capabilities Index | 127.48 | 143.98 |
| Mystery Game Puzzles | — | 20% |
| DTBench | — | 82.9% |
| LMCA | — | 29.1% |
Math Qwen3.5-Flash leads
Mistral Small 3.1: 13.7 (#295), Qwen3.5-Flash: 36.3 (#117)
| Benchmark | Mistral Small 3.1 | Qwen3.5-Flash |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 3.9% | 84.4% |
| LMArena Math | 1261 | 1407 |
| FrontierMath (Tiers 1-3) | — | 18.2% |
| Omni-MATH | 24.8% | — |
| FrontierMath (Feb 2025 set) | — | 6.2% |
| FrontierMath Tier 4 (v1) | — | 0% |
Knowledge Qwen3.5-Flash leads
Mistral Small 3.1: 22.6 (#266), Qwen3.5-Flash: 43.2 (#84)
| Benchmark | Mistral Small 3.1 | Qwen3.5-Flash |
|---|---|---|
| GPQA Diamond | 41.9% | 82.3% |
| LMArena Expert | 1258 | 1408 |
| SimpleQA Verified | — | 20.3% |
| MMLU-Pro | 61% | — |
| Vectara Hallucination Rate | — | 10.5% |
| GPQA (HELM) | 39.2% | — |
Multimodal Not comparable
Mistral Small 3.1: 32.6 (#93), Qwen3.5-Flash: —
| Benchmark | Mistral Small 3.1 | Qwen3.5-Flash |
|---|---|---|
| LMArena Vision | 1136 | — |
Multilingual Qwen3.5-Flash leads
Mistral Small 3.1: 41.4 (#204), Qwen3.5-Flash: 50.7 (#116)
| Benchmark | Mistral Small 3.1 | Qwen3.5-Flash |
|---|---|---|
| LMArena Non-English | 1255 | 1385 |
| LMArena Chinese | 1254 | 1446 |
| LMArena French | 1270 | 1412 |
| LMArena German | 1266 | 1389 |
| LMArena Japanese | 1210 | 1369 |
| LMArena Korean | 1206 | 1344 |
| LMArena Russian | 1263 | 1379 |
| LMArena Spanish | 1283 | 1399 |
Instruction Following Qwen3.5-Flash leads
Mistral Small 3.1: 63.8 (#225), Qwen3.5-Flash: 72.8 (#133)
| Benchmark | Mistral Small 3.1 | Qwen3.5-Flash |
|---|---|---|
| LMArena Instruction Following | 1264 | 1374 |
| IFEval | 75% | — |
Long Context Qwen3.5-Flash leads
Mistral Small 3.1: 39.5 (#172), Qwen3.5-Flash: 42.4 (#119)
| Benchmark | Mistral Small 3.1 | Qwen3.5-Flash |
|---|---|---|
| LMArena Longer Query | 1300 | 1392 |
Writing & Preference Qwen3.5-Flash leads
Mistral Small 3.1: 37.2 (#254), Qwen3.5-Flash: 58.2 (#117)
| Benchmark | Mistral Small 3.1 | Qwen3.5-Flash |
|---|---|---|
| LMArena Text | 1278 | 1397 |
| LMArena Creative Writing | 1253 | 1343 |
| LMArena Multi-Turn | 1270 | 1393 |
| EQ-Bench Creative Writing | 761 | — |
| WildBench | 78.8% | — |
Frequently asked questions
Is Mistral Small 3.1 better than Qwen3.5-Flash?
Qwen3.5-Flash is the stronger model overall, scoring 42.3 to 31.4 on the Noometry Index.
Which is cheaper, Mistral Small 3.1 or Qwen3.5-Flash?
Qwen3.5-Flash is cheaper. It lists at $0.10 per million input tokens and $0.40 per million output tokens; Mistral Small 3.1 lists at $0.35 and $0.56.
Is Mistral Small 3.1 or Qwen3.5-Flash better for coding?
Mistral Small 3.1 scores higher on coding benchmarks: 37.7 versus 33.9 in the Noometry coding category.
Which has the bigger context window?
Qwen3.5-Flash does, with 1M tokens against 128K.
How many benchmarks do Mistral Small 3.1 and Qwen3.5-Flash share?
21 benchmarks have published results for both models. Mistral Small 3.1 has 28 scored results on Noometry and Qwen3.5-Flash has 32.