Model comparison
Mistral Medium 3.5 vs Qwen3 235B-A22B
Qwen3 235B-A22B is the stronger model overall, scoring 43.2 to 39.6 on the Noometry Index.
Last verified . 18 shared benchmarks.
Mistral AI
Alibaba (Qwen)Summary
- They share 18 benchmarks with published results for both. Mistral Medium 3.5 scores higher in 2 categories and Qwen3 235B-A22B in 6 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3 235B-A22B leads 48.5 to 36.4.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 41.4% for Mistral Medium 3.5 and 69.4% for Qwen3 235B-A22B.
- Qwen3 235B-A22B is cheaper at $0.70 / $2.80 per million input/output tokens, against $1.50 / $7.50 for Mistral Medium 3.5.
- Mistral Medium 3.5 accepts more context: 262K tokens versus 131K.
Side by side
| Mistral Medium 3.5 | Qwen3 235B-A22B | |
|---|---|---|
| Provider | Mistral AI | Alibaba (Qwen) |
| Noometry Index | 39.6 | 43.2 |
| Released | — | 2025-04 |
| Weights | Open | Open |
| Context window | 262K | 131K |
| Max output | 210K | 16K |
| Input $ / M tokens | $1.50 | $0.70 |
| Output $ / M tokens | $7.50 | $2.80 |
| Results tracked | 22 | 49 |
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Category by category
Coding Qwen3 235B-A22B leads
Mistral Medium 3.5: 35.7 (#208), Qwen3 235B-A22B: 43.8 (#72)
| Benchmark | Mistral Medium 3.5 | Qwen3 235B-A22B |
|---|---|---|
| LMArena Coding | 1461 | 1445 |
| Aider Polyglot | — | 59.6% |
| LMArena WebDev | 1263 | — |
| SciCode | — | 42.4% |
| WeirdML | — | 41% |
Agentic & Tool Use Not comparable
Mistral Medium 3.5: —, Qwen3 235B-A22B: 33.7 (#48)
| Benchmark | Mistral Medium 3.5 | Qwen3 235B-A22B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 52.1% |
| Vending-Bench 2 | — | -11.34 |
Reasoning Too close to call
Mistral Medium 3.5: 16.6 (#290), Qwen3 235B-A22B: 15.8 (#302)
| Benchmark | Mistral Medium 3.5 | Qwen3 235B-A22B |
|---|---|---|
| Kagi LLM Benchmark | 41.4% | 69.4% |
| LMArena Hard Prompts | 1436 | 1433 |
| Epoch Capabilities Index | 141.42 | 143.87 |
| ARC-AGI-2 | — | 1.3% |
| SimpleBench | — | 31% |
| NYT Connections (extended) | 12.9% | — |
| ARC-AGI-1 | — | 11% |
| CritPt | — | 0% |
| Chess Puzzles | — | 12% |
| Mystery Game Puzzles | — | 9% |
| DTBench | — | 80.3% |
| LMCA | — | 29.3% |
| ForecastBench | — | 59.7 |
Math Qwen3 235B-A22B leads
Mistral Medium 3.5: 36.4 (#113), Qwen3 235B-A22B: 48.5 (#51)
| Benchmark | Mistral Medium 3.5 | Qwen3 235B-A22B |
|---|---|---|
| LMArena Math | 1432 | 1431 |
| OTIS Mock AIME 2024-2025 | — | 86.7% |
| Omni-MATH | — | 71.8% |
| MATH Level 5 | — | 68.9% |
| FrontierMath (Feb 2025 set) | — | 8.5% |
| FrontierMath Tier 4 (v1) | — | 0% |
Knowledge Qwen3 235B-A22B leads
Mistral Medium 3.5: 39.3 (#123), Qwen3 235B-A22B: 49.5 (#67)
| Benchmark | Mistral Medium 3.5 | Qwen3 235B-A22B |
|---|---|---|
| LMArena Expert | 1431 | 1460 |
| GPQA Diamond | — | 80.1% |
| SimpleQA Verified | — | 40.4% |
| MMLU-Pro | — | 84.4% |
| Confabulations | — | 15.6% |
| Vectara Hallucination Rate | — | 9.3% |
| GPQA (HELM) | — | 72.7% |
Multimodal Not comparable
Mistral Medium 3.5: 37.6 (#64), Qwen3 235B-A22B: —
| Benchmark | Mistral Medium 3.5 | Qwen3 235B-A22B |
|---|---|---|
| LMArena Vision | 1222 | — |
Multilingual Too close to call
Mistral Medium 3.5: 52.1 (#93), Qwen3 235B-A22B: 52.5 (#84)
| Benchmark | Mistral Medium 3.5 | Qwen3 235B-A22B |
|---|---|---|
| LMArena Non-English | 1405 | 1410 |
| LMArena Chinese | 1443 | 1481 |
| LMArena French | 1448 | 1445 |
| LMArena German | 1453 | 1432 |
| LMArena Korean | 1387 | 1391 |
| LMArena Russian | 1396 | 1411 |
| LMArena Spanish | 1406 | 1429 |
| LMArena Japanese | — | 1400 |
Instruction Following Mistral Medium 3.5 leads
Mistral Medium 3.5: 74.7 (#87), Qwen3 235B-A22B: 72.8 (#132)
| Benchmark | Mistral Medium 3.5 | Qwen3 235B-A22B |
|---|---|---|
| LMArena Instruction Following | 1414 | 1408 |
| IFEval | — | 83.5% |
Long Context Qwen3 235B-A22B leads
Mistral Medium 3.5: 43.2 (#101), Qwen3 235B-A22B: 46.1 (#25)
| Benchmark | Mistral Medium 3.5 | Qwen3 235B-A22B |
|---|---|---|
| LMArena Longer Query | 1414 | 1426 |
| Fiction.LiveBench | — | 75% |
Writing & Preference Qwen3 235B-A22B leads
Mistral Medium 3.5: 58.6 (#113), Qwen3 235B-A22B: 59.8 (#103)
| Benchmark | Mistral Medium 3.5 | Qwen3 235B-A22B |
|---|---|---|
| LMArena Text | 1421 | 1419 |
| LMArena Creative Writing | 1372 | 1384 |
| LMArena Multi-Turn | 1423 | 1432 |
| Short-Story Creative Writing | — | 83% |
| EQ-Bench Creative Writing | — | 1366 |
| WildBench | — | 86.6% |
| EQ-Bench 4 | 993 | — |
Frequently asked questions
Is Mistral Medium 3.5 better than Qwen3 235B-A22B?
Qwen3 235B-A22B is the stronger model overall, scoring 43.2 to 39.6 on the Noometry Index.
Which is cheaper, Mistral Medium 3.5 or Qwen3 235B-A22B?
Qwen3 235B-A22B is cheaper. It lists at $0.70 per million input tokens and $2.80 per million output tokens; Mistral Medium 3.5 lists at $1.50 and $7.50.
Is Mistral Medium 3.5 or Qwen3 235B-A22B better for coding?
Qwen3 235B-A22B scores higher on coding benchmarks: 43.8 versus 35.7 in the Noometry coding category.
Which has the bigger context window?
Mistral Medium 3.5 does, with 262K tokens against 131K.
How many benchmarks do Mistral Medium 3.5 and Qwen3 235B-A22B share?
18 benchmarks have published results for both models. Mistral Medium 3.5 has 22 scored results on Noometry and Qwen3 235B-A22B has 49.