Model comparison
Mistral Large 4 vs Qwen3 235B-A22B
Mistral Large 4 and Qwen3 235B-A22B score almost the same on the Noometry Index (43.1 vs 43.5), so choose on price, context window or the category you care about most.
Last verified . 13 shared benchmarks.
Mistral AI
Alibaba (Qwen)Summary
- They share 13 benchmarks with published results for both. Mistral Large 4 scores higher in 5 categories and Qwen3 235B-A22B in 3 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Qwen3 235B-A22B leads 49.6 to 36.6.
- The biggest single-benchmark swing is SimpleQA Verified: 20% for Mistral Large 4 and 40.4% for Qwen3 235B-A22B.
- Mistral Large 4 is cheaper at $0.68 / $2.09 per million input/output tokens, against $0.70 / $2.80 for Qwen3 235B-A22B.
- Mistral Large 4 accepts more context: 1.05M tokens versus 131K.
- Qwen3 235B-A22B has downloadable open weights; the other is API-only.
Side by side
| Mistral Large 4 | Qwen3 235B-A22B | |
|---|---|---|
| Provider | Mistral AI | Alibaba (Qwen) |
| Noometry Index | 43.1 | 43.5 |
| Released | 2026-10-06 | 2025-04 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 131K |
| Max output | 262K | 16K |
| Input $ / M tokens | $0.68 | $0.70 |
| Output $ / M tokens | $2.09 | $2.80 |
| Results tracked | 15 | 49 |
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Category by category
Coding Mistral Large 4 leads
Mistral Large 4: 48.6 (#57), Qwen3 235B-A22B: 44.3 (#75)
| Benchmark | Mistral Large 4 | Qwen3 235B-A22B |
|---|---|---|
| LMArena Coding | 1475 | 1445 |
| Aider Polyglot | — | 59.6% |
| LMArena WebDev | 1541 | — |
| SciCode | — | 42.4% |
| WeirdML | — | 41% |
Agentic & Tool Use Not comparable
Mistral Large 4: —, Qwen3 235B-A22B: 33.9 (#51)
| Benchmark | Mistral Large 4 | Qwen3 235B-A22B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 52.1% |
| Vending-Bench 2 | — | -11.34 |
Reasoning Mistral Large 4 leads
Mistral Large 4: 22.5 (#192), Qwen3 235B-A22B: 15.7 (#311)
| Benchmark | Mistral Large 4 | Qwen3 235B-A22B |
|---|---|---|
| LMArena Hard Prompts | 1444 | 1433 |
| ARC-AGI-2 | — | 1.3% |
| SimpleBench | — | 31% |
| Kagi LLM Benchmark | — | 69.4% |
| NYT Connections (extended) | 27.4% | — |
| ARC-AGI-1 | — | 11% |
| CritPt | — | 0% |
| Chess Puzzles | — | 12% |
| Mystery Game Puzzles | — | 9% |
| DTBench | — | 80.3% |
| LMCA | — | 29.3% |
| Epoch Capabilities Index | — | 143.85 |
| ForecastBench | — | 59.7 |
Math Qwen3 235B-A22B leads
Mistral Large 4: 40.4 (#91), Qwen3 235B-A22B: 50.4 (#57)
| Benchmark | Mistral Large 4 | Qwen3 235B-A22B |
|---|---|---|
| LMArena Math | 1488 | 1432 |
| 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 Large 4: 36.6 (#166), Qwen3 235B-A22B: 49.6 (#73)
| Benchmark | Mistral Large 4 | Qwen3 235B-A22B |
|---|---|---|
| SimpleQA Verified | 20% | 40.4% |
| LMArena Expert | 1447 | 1463 |
| GPQA Diamond | — | 80.1% |
| MMLU-Pro | — | 84.4% |
| Confabulations | — | 15.6% |
| Vectara Hallucination Rate | — | 9.3% |
| GPQA (HELM) | — | 72.7% |
Multilingual Too close to call
Mistral Large 4: 52.6 (#82), Qwen3 235B-A22B: 52.3 (#89)
| Benchmark | Mistral Large 4 | Qwen3 235B-A22B |
|---|---|---|
| LMArena Non-English | 1415 | 1409 |
| LMArena Chinese | 1491 | 1481 |
| LMArena Russian | 1414 | 1411 |
| LMArena French | — | 1445 |
| LMArena German | — | 1433 |
| LMArena Japanese | — | 1399 |
| LMArena Korean | — | 1391 |
| LMArena Spanish | — | 1430 |
Instruction Following Mistral Large 4 leads
Mistral Large 4: 75.0 (#76), Qwen3 235B-A22B: 72.6 (#136)
| Benchmark | Mistral Large 4 | Qwen3 235B-A22B |
|---|---|---|
| LMArena Instruction Following | 1424 | 1408 |
| IFEval | — | 83.5% |
Long Context Qwen3 235B-A22B leads
Mistral Large 4: 43.6 (#89), Qwen3 235B-A22B: 46.1 (#26)
| Benchmark | Mistral Large 4 | Qwen3 235B-A22B |
|---|---|---|
| LMArena Longer Query | 1429 | 1426 |
| Fiction.LiveBench | — | 75% |
Writing & Preference Too close to call
Mistral Large 4: 60.4 (#97), Qwen3 235B-A22B: 59.6 (#108)
| Benchmark | Mistral Large 4 | Qwen3 235B-A22B |
|---|---|---|
| LMArena Text | 1427 | 1419 |
| LMArena Creative Writing | 1361 | 1384 |
| LMArena Multi-Turn | 1424 | 1432 |
| Short-Story Creative Writing | — | 83% |
| EQ-Bench Creative Writing | — | 1366 |
| WildBench | — | 86.6% |
Frequently asked questions
Is Mistral Large 4 better than Qwen3 235B-A22B?
Mistral Large 4 and Qwen3 235B-A22B score almost the same on the Noometry Index (43.1 vs 43.5), so choose on price, context window or the category you care about most.
Which is cheaper, Mistral Large 4 or Qwen3 235B-A22B?
Mistral Large 4 is cheaper. It lists at $0.68 per million input tokens and $2.09 per million output tokens; Qwen3 235B-A22B lists at $0.70 and $2.80.
Is Mistral Large 4 or Qwen3 235B-A22B better for coding?
Mistral Large 4 scores higher on coding benchmarks: 48.6 versus 44.3 in the Noometry coding category.
Which has the bigger context window?
Mistral Large 4 does, with 1.05M tokens against 131K.
How many benchmarks do Mistral Large 4 and Qwen3 235B-A22B share?
13 benchmarks have published results for both models. Mistral Large 4 has 15 scored results on Noometry and Qwen3 235B-A22B has 49.