Model comparison
Mixtral 8x7B vs Qwen3.7 Max
Qwen3.7 Max is the stronger model overall, scoring 51.5 to 27.1 on the Noometry Index. Mixtral 8x7B costs 5.4× less per token, which makes it the better buy when Qwen3.7 Max's lead doesn't matter for your workload.
Last verified . 15 shared benchmarks.
Mistral AI
Alibaba (Qwen)Summary
- They share 15 benchmarks with published results for both. Mixtral 8x7B scores higher in 0 categories and Qwen3.7 Max in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Qwen3.7 Max leads 61.6 to 11.0.
- The biggest single-benchmark swing is GPQA Diamond: 30.6% for Mixtral 8x7B and 90.9% for Qwen3.7 Max.
- Mixtral 8x7B is cheaper at $0.70 / $0.70 per million input/output tokens, against $2.50 / $7.50 for Qwen3.7 Max.
- Qwen3.7 Max accepts more context: 1M tokens versus 32K.
- Mixtral 8x7B has downloadable open weights; the other is API-only.
Side by side
| Mixtral 8x7B | Qwen3.7 Max | |
|---|---|---|
| Provider | Mistral AI | Alibaba (Qwen) |
| Noometry Index | 27.1 | 51.5 |
| Released | 2023-12-11 | 2026-05-19 |
| Weights | Open | Proprietary |
| Context window | 32K | 1M |
| Max output | 32K | 131K |
| Input $ / M tokens | $0.70 | $2.50 |
| Output $ / M tokens | $0.70 | $7.50 |
| Results tracked | 38 | 33 |
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Category by category
Coding Qwen3.7 Max leads
Mixtral 8x7B: 32.8 (#269), Qwen3.7 Max: 50.4 (#45)
| Benchmark | Mixtral 8x7B | Qwen3.7 Max |
|---|---|---|
| LMArena Coding | 1126 | 1498 |
| SWE-bench Verified | — | 77.3% |
| LMArena WebDev | — | 1515 |
| SciCode | — | 48.8% |
| ALE-Bench | — | 1,189 |
| HumanEval+ | 39.6% | — |
| MBPP+ | 49.7% | — |
Agentic & Tool Use Not comparable
Mixtral 8x7B: —, Qwen3.7 Max: 22.1 (#135)
| Benchmark | Mixtral 8x7B | Qwen3.7 Max |
|---|---|---|
| GBAEval | — | 0.4% |
Reasoning Qwen3.7 Max leads
Mixtral 8x7B: 18.2 (#285), Qwen3.7 Max: 49.2 (#38)
| Benchmark | Mixtral 8x7B | Qwen3.7 Max |
|---|---|---|
| LMArena Hard Prompts | 1115 | 1483 |
| DTBench | 49.6% | 92.3% |
| Epoch Capabilities Index | 118.47 | 153.68 |
| SimpleBench | — | 70.4% |
| NYT Connections (extended) | — | 85.1% |
| CritPt | — | 13.4% |
| Chess Puzzles | — | 19% |
| EBR-Bench | — | 9.5% |
| Mystery Game Puzzles | — | 32% |
| LMCA | — | 44% |
| Adversarial NLI | 55.2% | — |
| ForecastBench | 56.3 | — |
| HellaSwag | 86.7% | — |
| PIQA | 83.6% | — |
| WinoGrande | 77.2% | — |
Math Qwen3.7 Max leads
Mixtral 8x7B: 18.8 (#289), Qwen3.7 Max: 62.4 (#32)
| Benchmark | Mixtral 8x7B | Qwen3.7 Max |
|---|---|---|
| LMArena Math | 1147 | 1490 |
| FrontierMath (Tiers 1-3) | — | 64.6% |
| FrontierMath Tier 4 | — | 34.1% |
| OTIS Mock AIME 2024-2025 | — | 95.6% |
| ProofBench | — | 26% |
| Omni-MATH | 10.5% | — |
| MATH Level 5 | 10% | — |
| GSM8K | 74.4% | — |
Knowledge Qwen3.7 Max leads
Mixtral 8x7B: 11.0 (#301), Qwen3.7 Max: 61.6 (#28)
| Benchmark | Mixtral 8x7B | Qwen3.7 Max |
|---|---|---|
| GPQA Diamond | 30.6% | 90.9% |
| LMArena Expert | 1088 | 1488 |
| SimpleQA Verified | — | 55.8% |
| MMLU-Pro | 33.5% | — |
| GPQA (HELM) | 29.6% | — |
| ARC (AI2) Challenge | 87.3% | — |
| MMLU | 70.6% | — |
| OpenBookQA | 85.8% | — |
| TriviaQA | 82.2% | — |
Multilingual Qwen3.7 Max leads
Mixtral 8x7B: 29.6 (#266), Qwen3.7 Max: 56.9 (#15)
| Benchmark | Mixtral 8x7B | Qwen3.7 Max |
|---|---|---|
| LMArena Non-English | 1077 | 1474 |
| LMArena Chinese | 1055 | 1530 |
| LMArena Russian | 1090 | 1484 |
| LMArena French | 1166 | — |
| LMArena German | 1114 | — |
| LMArena Japanese | 931 | — |
| LMArena Korean | 968 | — |
| LMArena Spanish | 1111 | — |
Instruction Following Qwen3.7 Max leads
Mixtral 8x7B: 51.0 (#297), Qwen3.7 Max: 76.7 (#38)
| Benchmark | Mixtral 8x7B | Qwen3.7 Max |
|---|---|---|
| LMArena Instruction Following | 1109 | 1460 |
| IFEval | 57.5% | — |
Long Context Qwen3.7 Max leads
Mixtral 8x7B: 33.4 (#260), Qwen3.7 Max: 45.4 (#40)
| Benchmark | Mixtral 8x7B | Qwen3.7 Max |
|---|---|---|
| LMArena Longer Query | 1103 | 1482 |
Writing & Preference Qwen3.7 Max leads
Mixtral 8x7B: 34.2 (#270), Qwen3.7 Max: 65.0 (#54)
| Benchmark | Mixtral 8x7B | Qwen3.7 Max |
|---|---|---|
| LMArena Text | 1132 | 1476 |
| LMArena Creative Writing | 1109 | 1449 |
| LMArena Multi-Turn | 1115 | 1481 |
| WildBench | 67.3% | — |
| EQ-Bench 4 | — | 1110 |
Frequently asked questions
Is Mixtral 8x7B better than Qwen3.7 Max?
Qwen3.7 Max is the stronger model overall, scoring 51.5 to 27.1 on the Noometry Index. Mixtral 8x7B costs 5.4× less per token, which makes it the better buy when Qwen3.7 Max's lead doesn't matter for your workload.
Which is cheaper, Mixtral 8x7B or Qwen3.7 Max?
Mixtral 8x7B is cheaper. It lists at $0.70 per million input tokens and $0.70 per million output tokens; Qwen3.7 Max lists at $2.50 and $7.50.
Is Mixtral 8x7B or Qwen3.7 Max better for coding?
Qwen3.7 Max scores higher on coding benchmarks: 50.4 versus 32.8 in the Noometry coding category.
Which has the bigger context window?
Qwen3.7 Max does, with 1M tokens against 32K.
How many benchmarks do Mixtral 8x7B and Qwen3.7 Max share?
15 benchmarks have published results for both models. Mixtral 8x7B has 38 scored results on Noometry and Qwen3.7 Max has 33.