Model comparison
Mistral Small vs Qwen3.6 27B
Qwen3.6 27B is the stronger model overall, scoring 42.2 to 33.4 on the Noometry Index. Mistral Small costs 5.1× less per token, which makes it the better buy when Qwen3.6 27B'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 27B in 4 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3.6 27B leads 48.5 to 16.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 5.8% for Mistral Small and 91.1% for Qwen3.6 27B.
- Mistral Small is cheaper at $0.15 / $0.60 per million input/output tokens, against $0.60 / $3.60 for Qwen3.6 27B.
Side by side
| Mistral Small | Qwen3.6 27B | |
|---|---|---|
| Provider | Mistral AI | Alibaba (Qwen) |
| Noometry Index | 33.4 | 42.2 |
| Released | 2024-02-26 | 2026-04-22 |
| Weights | Open | Open |
| Context window | 262K | 262K |
| Max output | 256K | 66K |
| Input $ / M tokens | $0.15 | $0.60 |
| Output $ / M tokens | $0.60 | $3.60 |
| Results tracked | 39 | 11 |
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Category by category
Coding Qwen3.6 27B leads
Mistral Small: 34.0 (#247), Qwen3.6 27B: 39.1 (#163)
| Benchmark | Mistral Small | Qwen3.6 27B |
|---|---|---|
| SciCode | 26.5% | 37.3% |
| BigCodeBench Instruct | 36.1% | — |
| LiveBench Coding | 36.2% | — |
| LMArena Coding | 1362 | — |
| BigCodeBench Complete | 46.6% | — |
| ALE-Bench | 497.62 | — |
Agentic & Tool Use Not comparable
Mistral Small: 28.1 (#93), Qwen3.6 27B: —
| Benchmark | Mistral Small | Qwen3.6 27B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 37.1% | — |
Reasoning Qwen3.6 27B leads
Mistral Small: 19.8 (#250), Qwen3.6 27B: 25.0 (#153)
| Benchmark | Mistral Small | Qwen3.6 27B |
|---|---|---|
| CritPt | 0% | 0.9% |
| DTBench | 70.9% | 78.1% |
| LMCA | 20.6% | 34.5% |
| Kagi LLM Benchmark | 37.8% | — |
| Chess Puzzles | — | 22% |
| LiveBench Reasoning | 44.8% | — |
| LMArena Hard Prompts | 1335 | — |
| Mystery Game Puzzles | — | 7% |
| LiveBench Data Analysis | 53.7% | — |
| Epoch Capabilities Index | — | 146.5 |
| LiveBench | 44% | — |
Math Qwen3.6 27B leads
Mistral Small: 16.4 (#293), Qwen3.6 27B: 48.5 (#62)
| Benchmark | Mistral Small | Qwen3.6 27B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 5.8% | 91.1% |
| FrontierMath (Tiers 1-3) | — | 35.1% |
| LiveBench Math | 39.9% | — |
| LMArena Math | 1341 | — |
| MATH Level 5 | 46.8% | — |
Knowledge Qwen3.6 27B leads
Mistral Small: 31.0 (#222), Qwen3.6 27B: 52.4 (#63)
| Benchmark | Mistral Small | Qwen3.6 27B |
|---|---|---|
| GPQA Diamond | 47.5% | 85.9% |
| Vectara Hallucination Rate | 5.1% | — |
| LMArena Expert | 1291 | — |
| MMLU | 68.7% | — |
Multimodal Not comparable
Mistral Small: 33.5 (#96), Qwen3.6 27B: —
| Benchmark | Mistral Small | Qwen3.6 27B |
|---|---|---|
| LMArena Vision | 1142 | — |
Multilingual Not comparable
Mistral Small: 45.5 (#169), Qwen3.6 27B: —
| Benchmark | Mistral Small | Qwen3.6 27B |
|---|---|---|
| LMArena Non-English | 1315 | — |
| LMArena Chinese | 1340 | — |
| LMArena French | 1337 | — |
| LMArena German | 1340 | — |
| LMArena Japanese | 1275 | — |
| LMArena Korean | 1259 | — |
| LMArena Russian | 1324 | — |
| LMArena Spanish | 1346 | — |
Instruction Following Not comparable
Mistral Small: 66.4 (#209), Qwen3.6 27B: —
| Benchmark | Mistral Small | Qwen3.6 27B |
|---|---|---|
| LiveBench Instruction Following | 63.7% | — |
| LMArena Instruction Following | 1310 | — |
Long Context Not comparable
Mistral Small: 40.4 (#156), Qwen3.6 27B: —
| Benchmark | Mistral Small | Qwen3.6 27B |
|---|---|---|
| LMArena Longer Query | 1327 | — |
Writing & Preference Mistral Small leads
Mistral Small: 52.5 (#171), Qwen3.6 27B: 50.3 (#181)
| Benchmark | Mistral Small | Qwen3.6 27B |
|---|---|---|
| LMArena Text | 1338 | — |
| LMArena Creative Writing | 1305 | — |
| EQ-Bench 4 | — | 1026 |
| LMArena Multi-Turn | 1344 | — |
| LiveBench Language | 30.5% | — |
Frequently asked questions
Is Mistral Small better than Qwen3.6 27B?
Qwen3.6 27B is the stronger model overall, scoring 42.2 to 33.4 on the Noometry Index. Mistral Small costs 5.1× less per token, which makes it the better buy when Qwen3.6 27B's lead doesn't matter for your workload.
Which is cheaper, Mistral Small or Qwen3.6 27B?
Mistral Small is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; Qwen3.6 27B lists at $0.60 and $3.60.
Is Mistral Small or Qwen3.6 27B better for coding?
Qwen3.6 27B scores higher on coding benchmarks: 39.1 versus 34.0 in the Noometry coding category.
Which has the bigger context window?
Both accept 262K tokens.
How many benchmarks do Mistral Small and Qwen3.6 27B share?
6 benchmarks have published results for both models. Mistral Small has 39 scored results on Noometry and Qwen3.6 27B has 11.