Model comparison
Mixtral 8x22B vs Qwen2.5 72B Instruct
Qwen2.5 72B Instruct is the stronger model overall, scoring 31.9 to 27.1 on the Noometry Index.
Last verified . 31 shared benchmarks.
Summary
- They share 31 benchmarks with published results for both. Mixtral 8x22B scores higher in 2 categories and Qwen2.5 72B Instruct in 7 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Qwen2.5 72B Instruct leads 27.0 to 15.1.
- The biggest single-benchmark swing is MATH Level 5: 24.2% for Mixtral 8x22B and 63.2% for Qwen2.5 72B Instruct.
- Qwen2.5 72B Instruct is cheaper at $1.40 / $5.60 per million input/output tokens, against $2 / $6 for Mixtral 8x22B.
- Qwen2.5 72B Instruct accepts more context: 131K tokens versus 64K.
Side by side
| Mixtral 8x22B | Qwen2.5 72B Instruct | |
|---|---|---|
| Provider | Mistral AI | Alibaba (Qwen) |
| Noometry Index | 27.1 | 31.9 |
| Released | 2024-04-17 | 2024-09 |
| Weights | Open | Open |
| Context window | 64K | 131K |
| Max output | 64K | 8K |
| Input $ / M tokens | $2 | $1.40 |
| Output $ / M tokens | $6 | $5.60 |
| Results tracked | 34 | 43 |
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Category by category
Coding Qwen2.5 72B Instruct leads
Mixtral 8x22B: 24.2 (#329), Qwen2.5 72B Instruct: 33.2 (#260)
| Benchmark | Mixtral 8x22B | Qwen2.5 72B Instruct |
|---|---|---|
| WeirdML | 3.2% | 16% |
| BigCodeBench Instruct | 40.6% | 45.8% |
| LMArena Coding | 1166 | 1292 |
| BigCodeBench Complete | 50.2% | 55.9% |
| HumanEval+ | 72% | — |
| MBPP+ | 64.3% | — |
Agentic & Tool Use Too close to call
Mixtral 8x22B: 23.1 (#127), Qwen2.5 72B Instruct: 22.1 (#133)
| Benchmark | Mixtral 8x22B | Qwen2.5 72B Instruct |
|---|---|---|
| TheAgentCompany | — | 5.7% |
| Cybench | 7.5% | — |
| BALROG | — | 16.2% |
| METR Time Horizons | — | 35.8% |
Reasoning Qwen2.5 72B Instruct leads
Mixtral 8x22B: 19.9 (#248), Qwen2.5 72B Instruct: 22.3 (#199)
| Benchmark | Mixtral 8x22B | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Hard Prompts | 1150 | 1271 |
| DTBench | 55.1% | 62.9% |
| Epoch Capabilities Index | 122.03 | 129 |
| ForecastBench | 56.3 | 57.5 |
| LMCA | — | 13.4% |
| BIG-Bench Hard | — | 79.8% |
| HellaSwag | — | 84.8% |
| PIQA | — | 82.6% |
| WinoGrande | — | 82.3% |
Math Mixtral 8x22B leads
Mixtral 8x22B: 22.9 (#275), Qwen2.5 72B Instruct: 19.3 (#287)
| Benchmark | Mixtral 8x22B | Qwen2.5 72B Instruct |
|---|---|---|
| Omni-MATH | 16.3% | 33% |
| LMArena Math | 1184 | 1283 |
| MATH Level 5 | 24.2% | 63.2% |
| OTIS Mock AIME 2024-2025 | — | 8.1% |
Knowledge Qwen2.5 72B Instruct leads
Mixtral 8x22B: 15.1 (#293), Qwen2.5 72B Instruct: 27.0 (#253)
| Benchmark | Mixtral 8x22B | Qwen2.5 72B Instruct |
|---|---|---|
| GPQA Diamond | 34.1% | 49.1% |
| MMLU-Pro | 46% | 63.1% |
| GPQA (HELM) | 33.4% | 42.6% |
| LMArena Expert | 1113 | 1245 |
| MMLU | 77.8% | 85.3% |
| Confabulations | — | 19.1% |
| ARC (AI2) Challenge | — | 94.5% |
| TriviaQA | — | 71.9% |
Multilingual Qwen2.5 72B Instruct leads
Mixtral 8x22B: 32.8 (#255), Qwen2.5 72B Instruct: 41.0 (#213)
| Benchmark | Mixtral 8x22B | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Non-English | 1128 | 1252 |
| LMArena Chinese | 1116 | 1272 |
| LMArena French | 1166 | 1280 |
| LMArena German | 1141 | 1234 |
| LMArena Japanese | 1037 | 1180 |
| LMArena Korean | 1057 | 1188 |
| LMArena Russian | 1158 | 1264 |
| LMArena Spanish | 1151 | 1256 |
Instruction Following Qwen2.5 72B Instruct leads
Mixtral 8x22B: 57.7 (#266), Qwen2.5 72B Instruct: 65.5 (#221)
| Benchmark | Mixtral 8x22B | Qwen2.5 72B Instruct |
|---|---|---|
| IFEval | 72.4% | 80.6% |
| LMArena Instruction Following | 1147 | 1254 |
Long Context Qwen2.5 72B Instruct leads
Mixtral 8x22B: 34.7 (#247), Qwen2.5 72B Instruct: 38.9 (#188)
| Benchmark | Mixtral 8x22B | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Longer Query | 1144 | 1282 |
Writing & Preference Qwen2.5 72B Instruct leads
Mixtral 8x22B: 36.9 (#262), Qwen2.5 72B Instruct: 46.7 (#215)
| Benchmark | Mixtral 8x22B | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Text | 1162 | 1269 |
| LMArena Creative Writing | 1141 | 1221 |
| WildBench | 71.1% | 80.2% |
| LMArena Multi-Turn | 1130 | 1272 |
Frequently asked questions
Is Mixtral 8x22B better than Qwen2.5 72B Instruct?
Qwen2.5 72B Instruct is the stronger model overall, scoring 31.9 to 27.1 on the Noometry Index.
Which is cheaper, Mixtral 8x22B or Qwen2.5 72B Instruct?
Qwen2.5 72B Instruct is cheaper. It lists at $1.40 per million input tokens and $5.60 per million output tokens; Mixtral 8x22B lists at $2 and $6.
Is Mixtral 8x22B or Qwen2.5 72B Instruct better for coding?
Qwen2.5 72B Instruct scores higher on coding benchmarks: 33.2 versus 24.2 in the Noometry coding category.
Which has the bigger context window?
Qwen2.5 72B Instruct does, with 131K tokens against 64K.
How many benchmarks do Mixtral 8x22B and Qwen2.5 72B Instruct share?
31 benchmarks have published results for both models. Mixtral 8x22B has 34 scored results on Noometry and Qwen2.5 72B Instruct has 43.