Model comparison
Mistral Small vs Qwen3.6 35B-A3B
Qwen3.6 35B-A3B is the stronger model overall, scoring 37.2 to 33.1 on the Noometry Index. Mistral Small costs 2.1× less per token, which makes it the better buy when Qwen3.6 35B-A3B's lead doesn't matter for your workload.
Last verified . 6 shared benchmarks.
Mistral AI
Alibaba (Qwen)Summary
- They share 6 benchmarks with published results for both. Mistral Small scores higher in 1 category and Qwen3.6 35B-A3B in 4 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3.6 35B-A3B leads 37.9 to 15.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 5.8% for Mistral Small and 86.7% for Qwen3.6 35B-A3B.
- Mistral Small is cheaper at $0.15 / $0.60 per million input/output tokens, against $0.25 / $1.49 for Qwen3.6 35B-A3B.
- Qwen3.6 35B-A3B accepts more context: 262K tokens versus 256K.
Side by side
| Mistral Small | Qwen3.6 35B-A3B | |
|---|---|---|
| Provider | Mistral AI | Alibaba (Qwen) |
| Noometry Index | 33.1 | 37.2 |
| Released | 2024-02-26 | 2026-04-01 |
| Weights | Open | Open |
| Context window | 256K | 262K |
| Max output | 256K | 66K |
| Input $ / M tokens | $0.15 | $0.25 |
| Output $ / M tokens | $0.60 | $1.49 |
| Results tracked | 39 | 14 |
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Category by category
Coding Qwen3.6 35B-A3B leads
Mistral Small: 33.5 (#242), Qwen3.6 35B-A3B: 36.7 (#190)
| Benchmark | Mistral Small | Qwen3.6 35B-A3B |
|---|---|---|
| SciCode | 26.5% | 35.8% |
| WeirdML | — | 34.5% |
| BigCodeBench Instruct | 36.1% | — |
| LiveBench Coding | 36.2% | — |
| LMArena Coding | 1363 | — |
| BigCodeBench Complete | 46.6% | — |
| ALE-Bench | 497.62 | — |
Agentic & Tool Use Mistral Small leads
Mistral Small: 27.9 (#87), Qwen3.6 35B-A3B: 21.9 (#126)
| Benchmark | Mistral Small | Qwen3.6 35B-A3B |
|---|---|---|
| Terminal-Bench | — | 23% |
| Berkeley Function Calling Leaderboard | 37.1% | — |
Reasoning Qwen3.6 35B-A3B leads
Mistral Small: 19.3 (#238), Qwen3.6 35B-A3B: 27.2 (#112)
| Benchmark | Mistral Small | Qwen3.6 35B-A3B |
|---|---|---|
| CritPt | 0% | 0.3% |
| DTBench | 70.9% | 73.9% |
| LMCA | 20.6% | 29.7% |
| Kagi LLM Benchmark | 37.8% | — |
| NYT Connections (extended) | — | 41.6% |
| Chess Puzzles | — | 26% |
| LiveBench Reasoning | 44.8% | — |
| LMArena Hard Prompts | 1336 | — |
| Mystery Game Puzzles | — | 22% |
| LiveBench Data Analysis | 53.7% | — |
| Surface Evolver Bench | — | 44.4% |
| Epoch Capabilities Index | — | 143.92 |
| LiveBench | 44% | — |
Math Qwen3.6 35B-A3B leads
Mistral Small: 15.4 (#287), Qwen3.6 35B-A3B: 37.9 (#90)
| Benchmark | Mistral Small | Qwen3.6 35B-A3B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 5.8% | 86.7% |
| FrontierMath (Tiers 1-3) | — | 20.4% |
| LiveBench Math | 39.9% | — |
| LMArena Math | 1341 | — |
| MATH Level 5 | 46.8% | — |
Knowledge Qwen3.6 35B-A3B leads
Mistral Small: 30.9 (#214), Qwen3.6 35B-A3B: 51.4 (#63)
| Benchmark | Mistral Small | Qwen3.6 35B-A3B |
|---|---|---|
| GPQA Diamond | 47.5% | 84.8% |
| Vectara Hallucination Rate | 5.1% | — |
| LMArena Expert | 1293 | — |
| MMLU | 68.7% | — |
Multimodal Not comparable
Mistral Small: 32.9 (#91), Qwen3.6 35B-A3B: —
| Benchmark | Mistral Small | Qwen3.6 35B-A3B |
|---|---|---|
| LMArena Vision | 1142 | — |
Multilingual Not comparable
Mistral Small: 45.7 (#164), Qwen3.6 35B-A3B: —
| Benchmark | Mistral Small | Qwen3.6 35B-A3B |
|---|---|---|
| LMArena Non-English | 1315 | — |
| LMArena Chinese | 1342 | — |
| LMArena French | 1337 | — |
| LMArena German | 1341 | — |
| LMArena Japanese | 1275 | — |
| LMArena Korean | 1260 | — |
| LMArena Russian | 1324 | — |
| LMArena Spanish | 1346 | — |
Instruction Following Not comparable
Mistral Small: 66.6 (#204), Qwen3.6 35B-A3B: —
| Benchmark | Mistral Small | Qwen3.6 35B-A3B |
|---|---|---|
| LiveBench Instruction Following | 63.7% | — |
| LMArena Instruction Following | 1310 | — |
Long Context Not comparable
Mistral Small: 40.4 (#151), Qwen3.6 35B-A3B: —
| Benchmark | Mistral Small | Qwen3.6 35B-A3B |
|---|---|---|
| LMArena Longer Query | 1327 | — |
Writing & Preference Not comparable
Mistral Small: 52.6 (#165), Qwen3.6 35B-A3B: —
| Benchmark | Mistral Small | Qwen3.6 35B-A3B |
|---|---|---|
| LMArena Text | 1338 | — |
| LMArena Creative Writing | 1304 | — |
| LMArena Multi-Turn | 1344 | — |
| LiveBench Language | 30.5% | — |
Frequently asked questions
Is Mistral Small better than Qwen3.6 35B-A3B?
Qwen3.6 35B-A3B is the stronger model overall, scoring 37.2 to 33.1 on the Noometry Index. Mistral Small costs 2.1× less per token, which makes it the better buy when Qwen3.6 35B-A3B's lead doesn't matter for your workload.
Which is cheaper, Mistral Small or Qwen3.6 35B-A3B?
Mistral Small is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; Qwen3.6 35B-A3B lists at $0.25 and $1.49.
Is Mistral Small or Qwen3.6 35B-A3B better for coding?
Qwen3.6 35B-A3B scores higher on coding benchmarks: 36.7 versus 33.5 in the Noometry coding category.
Which has the bigger context window?
Qwen3.6 35B-A3B does, with 262K tokens against 256K.
How many benchmarks do Mistral Small and Qwen3.6 35B-A3B share?
6 benchmarks have published results for both models. Mistral Small has 39 scored results on Noometry and Qwen3.6 35B-A3B has 14.