Model comparison
Mixtral 8x7B vs Qwen3-Coder 480B-A35B Instruct
Qwen3-Coder 480B-A35B Instruct is the stronger model overall, scoring 38.1 to 27.1 on the Noometry Index. Mixtral 8x7B costs 4.3× less per token, which makes it the better buy when Qwen3-Coder 480B-A35B Instruct's lead doesn't matter for your workload.
Last verified . 17 shared benchmarks.
Mistral AI
Alibaba (Qwen)Summary
- They share 17 benchmarks with published results for both. Mixtral 8x7B scores higher in 0 categories and Qwen3-Coder 480B-A35B Instruct in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Qwen3-Coder 480B-A35B Instruct leads 37.0 to 11.0.
- Mixtral 8x7B is cheaper at $0.70 / $0.70 per million input/output tokens, against $1.50 / $7.50 for Qwen3-Coder 480B-A35B Instruct.
- Qwen3-Coder 480B-A35B Instruct accepts more context: 262K tokens versus 32K.
Side by side
| Mixtral 8x7B | Qwen3-Coder 480B-A35B Instruct | |
|---|---|---|
| Provider | Mistral AI | Alibaba (Qwen) |
| Noometry Index | 27.1 | 38.1 |
| Released | 2023-12-11 | 2025-04 |
| Weights | Open | Open |
| Context window | 32K | 262K |
| Max output | 32K | 66K |
| Input $ / M tokens | $0.70 | $1.50 |
| Output $ / M tokens | $0.70 | $7.50 |
| Results tracked | 38 | 25 |
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Category by category
Coding Qwen3-Coder 480B-A35B Instruct leads
Mixtral 8x7B: 32.8 (#269), Qwen3-Coder 480B-A35B Instruct: 35.5 (#223)
| Benchmark | Mixtral 8x7B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Coding | 1126 | 1412 |
| SWE-bench Verified (bash only) | — | 55.4% |
| LMArena WebDev | — | 1275 |
| GSO | — | 4.9% |
| WeirdML | — | 41.2% |
| ALE-Bench | — | 461.45 |
| AlgoTune | — | 1.44 |
| HumanEval+ | 39.6% | — |
| MBPP+ | 49.7% | — |
Agentic & Tool Use Not comparable
Mixtral 8x7B: —, Qwen3-Coder 480B-A35B Instruct: 23.9 (#123)
| Benchmark | Mixtral 8x7B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| Terminal-Bench | — | 27.2% |
Reasoning Qwen3-Coder 480B-A35B Instruct leads
Mixtral 8x7B: 18.2 (#285), Qwen3-Coder 480B-A35B Instruct: 25.5 (#149)
| Benchmark | Mixtral 8x7B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Hard Prompts | 1115 | 1372 |
| Kagi LLM Benchmark | — | 49.5% |
| DTBench | 49.6% | — |
| Adversarial NLI | 55.2% | — |
| Epoch Capabilities Index | 118.47 | — |
| ForecastBench | 56.3 | — |
| HellaSwag | 86.7% | — |
| PIQA | 83.6% | — |
| WinoGrande | 77.2% | — |
Math Qwen3-Coder 480B-A35B Instruct leads
Mixtral 8x7B: 18.8 (#289), Qwen3-Coder 480B-A35B Instruct: 37.6 (#150)
| Benchmark | Mixtral 8x7B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Math | 1147 | 1365 |
| Omni-MATH | 10.5% | — |
| MATH Level 5 | 10% | — |
| GSM8K | 74.4% | — |
Knowledge Qwen3-Coder 480B-A35B Instruct leads
Mixtral 8x7B: 11.0 (#301), Qwen3-Coder 480B-A35B Instruct: 37.0 (#162)
| Benchmark | Mixtral 8x7B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Expert | 1088 | 1338 |
| GPQA Diamond | 30.6% | — |
| MMLU-Pro | 33.5% | — |
| GPQA (HELM) | 29.6% | — |
| ARC (AI2) Challenge | 87.3% | — |
| MMLU | 70.6% | — |
| OpenBookQA | 85.8% | — |
| TriviaQA | 82.2% | — |
Multilingual Qwen3-Coder 480B-A35B Instruct leads
Mixtral 8x7B: 29.6 (#266), Qwen3-Coder 480B-A35B Instruct: 47.7 (#148)
| Benchmark | Mixtral 8x7B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Non-English | 1077 | 1346 |
| LMArena Chinese | 1055 | 1357 |
| LMArena French | 1166 | 1398 |
| LMArena German | 1114 | 1325 |
| LMArena Japanese | 931 | 1310 |
| LMArena Korean | 968 | 1305 |
| LMArena Russian | 1090 | 1366 |
| LMArena Spanish | 1111 | 1360 |
Instruction Following Qwen3-Coder 480B-A35B Instruct leads
Mixtral 8x7B: 51.0 (#297), Qwen3-Coder 480B-A35B Instruct: 71.6 (#147)
| Benchmark | Mixtral 8x7B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Instruction Following | 1109 | 1355 |
| IFEval | 57.5% | — |
Long Context Qwen3-Coder 480B-A35B Instruct leads
Mixtral 8x7B: 33.4 (#260), Qwen3-Coder 480B-A35B Instruct: 42.0 (#131)
| Benchmark | Mixtral 8x7B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Longer Query | 1103 | 1378 |
Writing & Preference Qwen3-Coder 480B-A35B Instruct leads
Mixtral 8x7B: 34.2 (#270), Qwen3-Coder 480B-A35B Instruct: 55.3 (#147)
| Benchmark | Mixtral 8x7B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Text | 1132 | 1357 |
| LMArena Creative Writing | 1109 | 1333 |
| LMArena Multi-Turn | 1115 | 1365 |
| WildBench | 67.3% | — |
Frequently asked questions
Is Mixtral 8x7B better than Qwen3-Coder 480B-A35B Instruct?
Qwen3-Coder 480B-A35B Instruct is the stronger model overall, scoring 38.1 to 27.1 on the Noometry Index. Mixtral 8x7B costs 4.3× less per token, which makes it the better buy when Qwen3-Coder 480B-A35B Instruct's lead doesn't matter for your workload.
Which is cheaper, Mixtral 8x7B or Qwen3-Coder 480B-A35B Instruct?
Mixtral 8x7B is cheaper. It lists at $0.70 per million input tokens and $0.70 per million output tokens; Qwen3-Coder 480B-A35B Instruct lists at $1.50 and $7.50.
Is Mixtral 8x7B or Qwen3-Coder 480B-A35B Instruct better for coding?
Qwen3-Coder 480B-A35B Instruct scores higher on coding benchmarks: 35.5 versus 32.8 in the Noometry coding category.
Which has the bigger context window?
Qwen3-Coder 480B-A35B Instruct does, with 262K tokens against 32K.
How many benchmarks do Mixtral 8x7B and Qwen3-Coder 480B-A35B Instruct share?
17 benchmarks have published results for both models. Mixtral 8x7B has 38 scored results on Noometry and Qwen3-Coder 480B-A35B Instruct has 25.