Model comparison
Mixtral 8x7B vs Qwen3 235B-A22B
Qwen3 235B-A22B is the stronger model overall, scoring 43.5 to 27.1 on the Noometry Index. Mixtral 8x7B costs 1.8× less per token, which makes it the better buy when Qwen3 235B-A22B's lead doesn't matter for your workload.
Last verified . 27 shared benchmarks.
Mistral AI
Alibaba (Qwen)Summary
- They share 27 benchmarks with published results for both. Mixtral 8x7B scores higher in 1 category and Qwen3 235B-A22B in 7 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Qwen3 235B-A22B leads 49.6 to 11.0.
- The biggest single-benchmark swing is Omni-MATH: 10.5% for Mixtral 8x7B and 71.8% for Qwen3 235B-A22B.
- Mixtral 8x7B is cheaper at $0.70 / $0.70 per million input/output tokens, against $0.70 / $2.80 for Qwen3 235B-A22B.
- Qwen3 235B-A22B accepts more context: 131K tokens versus 32K.
Side by side
| Mixtral 8x7B | Qwen3 235B-A22B | |
|---|---|---|
| Provider | Mistral AI | Alibaba (Qwen) |
| Noometry Index | 27.1 | 43.5 |
| Released | 2023-12-11 | 2025-04 |
| Weights | Open | Open |
| Context window | 32K | 131K |
| Max output | 32K | 16K |
| Input $ / M tokens | $0.70 | $0.70 |
| Output $ / M tokens | $0.70 | $2.80 |
| Results tracked | 38 | 49 |
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Category by category
Coding Qwen3 235B-A22B leads
Mixtral 8x7B: 32.8 (#269), Qwen3 235B-A22B: 44.3 (#75)
| Benchmark | Mixtral 8x7B | Qwen3 235B-A22B |
|---|---|---|
| LMArena Coding | 1126 | 1445 |
| Aider Polyglot | — | 59.6% |
| SciCode | — | 42.4% |
| WeirdML | — | 41% |
| HumanEval+ | 39.6% | — |
| MBPP+ | 49.7% | — |
Agentic & Tool Use Not comparable
Mixtral 8x7B: —, Qwen3 235B-A22B: 33.9 (#51)
| Benchmark | Mixtral 8x7B | Qwen3 235B-A22B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 52.1% |
| Vending-Bench 2 | — | -11.34 |
Reasoning Mixtral 8x7B leads
Mixtral 8x7B: 18.2 (#285), Qwen3 235B-A22B: 15.7 (#311)
| Benchmark | Mixtral 8x7B | Qwen3 235B-A22B |
|---|---|---|
| LMArena Hard Prompts | 1115 | 1433 |
| DTBench | 49.6% | 80.3% |
| Epoch Capabilities Index | 118.47 | 143.85 |
| ForecastBench | 56.3 | 59.7 |
| ARC-AGI-2 | — | 1.3% |
| SimpleBench | — | 31% |
| Kagi LLM Benchmark | — | 69.4% |
| ARC-AGI-1 | — | 11% |
| CritPt | — | 0% |
| Chess Puzzles | — | 12% |
| Mystery Game Puzzles | — | 9% |
| LMCA | — | 29.3% |
| Adversarial NLI | 55.2% | — |
| HellaSwag | 86.7% | — |
| PIQA | 83.6% | — |
| WinoGrande | 77.2% | — |
Math Qwen3 235B-A22B leads
Mixtral 8x7B: 18.8 (#289), Qwen3 235B-A22B: 50.4 (#57)
| Benchmark | Mixtral 8x7B | Qwen3 235B-A22B |
|---|---|---|
| Omni-MATH | 10.5% | 71.8% |
| LMArena Math | 1147 | 1432 |
| MATH Level 5 | 10% | 68.9% |
| OTIS Mock AIME 2024-2025 | — | 86.7% |
| FrontierMath (Feb 2025 set) | — | 8.5% |
| FrontierMath Tier 4 (v1) | — | 0% |
| GSM8K | 74.4% | — |
Knowledge Qwen3 235B-A22B leads
Mixtral 8x7B: 11.0 (#301), Qwen3 235B-A22B: 49.6 (#73)
| Benchmark | Mixtral 8x7B | Qwen3 235B-A22B |
|---|---|---|
| GPQA Diamond | 30.6% | 80.1% |
| MMLU-Pro | 33.5% | 84.4% |
| GPQA (HELM) | 29.6% | 72.7% |
| LMArena Expert | 1088 | 1463 |
| SimpleQA Verified | — | 40.4% |
| Confabulations | — | 15.6% |
| Vectara Hallucination Rate | — | 9.3% |
| ARC (AI2) Challenge | 87.3% | — |
| MMLU | 70.6% | — |
| OpenBookQA | 85.8% | — |
| TriviaQA | 82.2% | — |
Multilingual Qwen3 235B-A22B leads
Mixtral 8x7B: 29.6 (#266), Qwen3 235B-A22B: 52.3 (#89)
| Benchmark | Mixtral 8x7B | Qwen3 235B-A22B |
|---|---|---|
| LMArena Non-English | 1077 | 1409 |
| LMArena Chinese | 1055 | 1481 |
| LMArena French | 1166 | 1445 |
| LMArena German | 1114 | 1433 |
| LMArena Japanese | 931 | 1399 |
| LMArena Korean | 968 | 1391 |
| LMArena Russian | 1090 | 1411 |
| LMArena Spanish | 1111 | 1430 |
Instruction Following Qwen3 235B-A22B leads
Mixtral 8x7B: 51.0 (#297), Qwen3 235B-A22B: 72.6 (#136)
| Benchmark | Mixtral 8x7B | Qwen3 235B-A22B |
|---|---|---|
| IFEval | 57.5% | 83.5% |
| LMArena Instruction Following | 1109 | 1408 |
Long Context Qwen3 235B-A22B leads
Mixtral 8x7B: 33.4 (#260), Qwen3 235B-A22B: 46.1 (#26)
| Benchmark | Mixtral 8x7B | Qwen3 235B-A22B |
|---|---|---|
| LMArena Longer Query | 1103 | 1426 |
| Fiction.LiveBench | — | 75% |
Writing & Preference Qwen3 235B-A22B leads
Mixtral 8x7B: 34.2 (#270), Qwen3 235B-A22B: 59.6 (#108)
| Benchmark | Mixtral 8x7B | Qwen3 235B-A22B |
|---|---|---|
| LMArena Text | 1132 | 1419 |
| LMArena Creative Writing | 1109 | 1384 |
| WildBench | 67.3% | 86.6% |
| LMArena Multi-Turn | 1115 | 1432 |
| Short-Story Creative Writing | — | 83% |
| EQ-Bench Creative Writing | — | 1366 |
Frequently asked questions
Is Mixtral 8x7B better than Qwen3 235B-A22B?
Qwen3 235B-A22B is the stronger model overall, scoring 43.5 to 27.1 on the Noometry Index. Mixtral 8x7B costs 1.8× less per token, which makes it the better buy when Qwen3 235B-A22B's lead doesn't matter for your workload.
Which is cheaper, Mixtral 8x7B or Qwen3 235B-A22B?
Mixtral 8x7B is cheaper. It lists at $0.70 per million input tokens and $0.70 per million output tokens; Qwen3 235B-A22B lists at $0.70 and $2.80.
Is Mixtral 8x7B or Qwen3 235B-A22B better for coding?
Qwen3 235B-A22B scores higher on coding benchmarks: 44.3 versus 32.8 in the Noometry coding category.
Which has the bigger context window?
Qwen3 235B-A22B does, with 131K tokens against 32K.
How many benchmarks do Mixtral 8x7B and Qwen3 235B-A22B share?
27 benchmarks have published results for both models. Mixtral 8x7B has 38 scored results on Noometry and Qwen3 235B-A22B has 49.