Model comparison
Mistral Small vs Qwen3.8 Max
Qwen3.8 Max is the stronger model overall, scoring 56.8 to 33.4 on the Noometry Index. Mistral Small costs 11× less per token, which makes it the better buy when Qwen3.8 Max's lead doesn't matter for your workload.
Last verified . 24 shared benchmarks.
Mistral AI
Alibaba (Qwen)Summary
- They share 24 benchmarks with published results for both. Mistral Small scores higher in 0 categories and Qwen3.8 Max in 10 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3.8 Max leads 73.2 to 16.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 5.8% for Mistral Small and 100% for Qwen3.8 Max.
- Mistral Small is cheaper at $0.15 / $0.60 per million input/output tokens, against $2 / $6 for Qwen3.8 Max.
- Qwen3.8 Max accepts more context: 1M tokens versus 262K.
- Mistral Small has downloadable open weights; the other is API-only.
Side by side
| Mistral Small | Qwen3.8 Max | |
|---|---|---|
| Provider | Mistral AI | Alibaba (Qwen) |
| Noometry Index | 33.4 | 56.8 |
| Released | 2024-02-26 | 2026-08-02 |
| Weights | Open | Proprietary |
| Context window | 262K | 1M |
| Max output | 256K | 131K |
| Input $ / M tokens | $0.15 | $2 |
| Output $ / M tokens | $0.60 | $6 |
| Results tracked | 39 | 39 |
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Category by category
Coding Qwen3.8 Max leads
Mistral Small: 34.0 (#247), Qwen3.8 Max: 53.5 (#29)
| Benchmark | Mistral Small | Qwen3.8 Max |
|---|---|---|
| SciCode | 26.5% | 53.2% |
| LMArena Coding | 1362 | 1502 |
| DeepSWE | — | 57.5% |
| LMArena WebDev | — | 1674 |
| FrontierSWE | — | 17.8% |
| BigCodeBench Instruct | 36.1% | — |
| LiveBench Coding | 36.2% | — |
| BigCodeBench Complete | 46.6% | — |
| ALE-Bench | 497.62 | — |
Agentic & Tool Use Qwen3.8 Max leads
Mistral Small: 28.1 (#93), Qwen3.8 Max: 45.4 (#14)
| Benchmark | Mistral Small | Qwen3.8 Max |
|---|---|---|
| APEX-Agents | — | 63.3% |
| Berkeley Function Calling Leaderboard | 37.1% | — |
| τ²-bench Banking | — | 55.1% |
| GDP.pdf | — | 23.2% |
Reasoning Qwen3.8 Max leads
Mistral Small: 19.8 (#250), Qwen3.8 Max: 54.4 (#26)
| Benchmark | Mistral Small | Qwen3.8 Max |
|---|---|---|
| CritPt | 0% | 20% |
| LMArena Hard Prompts | 1335 | 1496 |
| DTBench | 70.9% | 92% |
| LMCA | 20.6% | 46.2% |
| Kagi LLM Benchmark | 37.8% | — |
| NYT Connections (extended) | — | 88.3% |
| Chess Puzzles | — | 40% |
| LiveBench Reasoning | 44.8% | — |
| Mystery Game Puzzles | — | 38% |
| LiveBench Data Analysis | 53.7% | — |
| Epoch Capabilities Index | — | 156.41 |
| LiveBench | 44% | — |
Math Qwen3.8 Max leads
Mistral Small: 16.4 (#293), Qwen3.8 Max: 73.2 (#20)
| Benchmark | Mistral Small | Qwen3.8 Max |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 5.8% | 100% |
| LMArena Math | 1341 | 1499 |
| FrontierMath (Tiers 1-3) | — | 74.7% |
| FrontierMath Tier 4 | — | 46.3% |
| ProofBench | — | 58% |
| LiveBench Math | 39.9% | — |
| MATH Level 5 | 46.8% | — |
Knowledge Qwen3.8 Max leads
Mistral Small: 31.0 (#222), Qwen3.8 Max: 61.7 (#27)
| Benchmark | Mistral Small | Qwen3.8 Max |
|---|---|---|
| GPQA Diamond | 47.5% | 92.7% |
| LMArena Expert | 1291 | 1507 |
| SimpleQA Verified | — | 47.3% |
| Vectara Hallucination Rate | 5.1% | — |
| MMLU | 68.7% | — |
Multimodal Qwen3.8 Max leads
Mistral Small: 33.5 (#96), Qwen3.8 Max: 37.2 (#75)
| Benchmark | Mistral Small | Qwen3.8 Max |
|---|---|---|
| LMArena Vision | 1142 | 1314 |
| Furniture Assembly | — | 20% |
Multilingual Qwen3.8 Max leads
Mistral Small: 45.5 (#169), Qwen3.8 Max: 56.7 (#18)
| Benchmark | Mistral Small | Qwen3.8 Max |
|---|---|---|
| LMArena Non-English | 1315 | 1472 |
| LMArena Chinese | 1340 | 1538 |
| LMArena French | 1337 | 1503 |
| LMArena German | 1340 | 1483 |
| LMArena Japanese | 1275 | 1467 |
| LMArena Korean | 1259 | 1461 |
| LMArena Russian | 1324 | 1481 |
| LMArena Spanish | 1346 | 1492 |
Instruction Following Qwen3.8 Max leads
Mistral Small: 66.4 (#209), Qwen3.8 Max: 77.6 (#17)
| Benchmark | Mistral Small | Qwen3.8 Max |
|---|---|---|
| LMArena Instruction Following | 1310 | 1479 |
| LiveBench Instruction Following | 63.7% | — |
Long Context Qwen3.8 Max leads
Mistral Small: 40.4 (#156), Qwen3.8 Max: 45.6 (#31)
| Benchmark | Mistral Small | Qwen3.8 Max |
|---|---|---|
| LMArena Longer Query | 1327 | 1489 |
Writing & Preference Qwen3.8 Max leads
Mistral Small: 52.5 (#171), Qwen3.8 Max: 67.1 (#30)
| Benchmark | Mistral Small | Qwen3.8 Max |
|---|---|---|
| LMArena Text | 1338 | 1483 |
| LMArena Creative Writing | 1305 | 1479 |
| LMArena Multi-Turn | 1344 | 1489 |
| LiveBench Language | 30.5% | — |
Frequently asked questions
Is Mistral Small better than Qwen3.8 Max?
Qwen3.8 Max is the stronger model overall, scoring 56.8 to 33.4 on the Noometry Index. Mistral Small costs 11× less per token, which makes it the better buy when Qwen3.8 Max's lead doesn't matter for your workload.
Which is cheaper, Mistral Small or Qwen3.8 Max?
Mistral Small is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; Qwen3.8 Max lists at $2 and $6.
Is Mistral Small or Qwen3.8 Max better for coding?
Qwen3.8 Max scores higher on coding benchmarks: 53.5 versus 34.0 in the Noometry coding category.
Which has the bigger context window?
Qwen3.8 Max does, with 1M tokens against 262K.
How many benchmarks do Mistral Small and Qwen3.8 Max share?
24 benchmarks have published results for both models. Mistral Small has 39 scored results on Noometry and Qwen3.8 Max has 39.