Model comparison
Mixtral 8x7B vs Qwen2.5 7B Instruct
Qwen2.5 7B Instruct is the stronger model overall, scoring 28.6 to 26.8 on the Noometry Index.
Last verified . 9 shared benchmarks.
Mistral AI
Alibaba (Qwen)Summary
- They share 9 benchmarks with published results for both. Mixtral 8x7B scores higher in 2 categories and Qwen2.5 7B Instruct in 4 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where Qwen2.5 7B Instruct leads 48.8 to 34.4.
- The biggest single-benchmark swing is MMLU-Pro: 33.5% for Mixtral 8x7B and 53.9% for Qwen2.5 7B Instruct.
- Qwen2.5 7B Instruct is cheaper at $0.17 / $0.70 per million input/output tokens, against $0.70 / $0.70 for Mixtral 8x7B.
- Qwen2.5 7B Instruct accepts more context: 131K tokens versus 32K.
Side by side
| Mixtral 8x7B | Qwen2.5 7B Instruct | |
|---|---|---|
| Provider | Mistral AI | Alibaba (Qwen) |
| Noometry Index | 26.8 | 28.6 |
| Released | 2023-12-11 | 2024-09 |
| Weights | Open | Open |
| Context window | 32K | 131K |
| Max output | 32K | 8K |
| Input $ / M tokens | $0.70 | $0.17 |
| Output $ / M tokens | $0.70 | $0.70 |
| Results tracked | 38 | 15 |
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Category by category
Coding Qwen2.5 7B Instruct leads
Mixtral 8x7B: 32.2 (#265), Qwen2.5 7B Instruct: 35.9 (#204)
| Benchmark | Mixtral 8x7B | Qwen2.5 7B Instruct |
|---|---|---|
| BigCodeBench Instruct | — | 37.6% |
| LMArena Coding | 1127 | — |
| BigCodeBench Complete | — | 46.1% |
| HumanEval+ | 39.6% | — |
| MBPP+ | 49.7% | — |
Agentic & Tool Use Not comparable
Mixtral 8x7B: —, Qwen2.5 7B Instruct: 23.5 (#117)
| Benchmark | Mixtral 8x7B | Qwen2.5 7B Instruct |
|---|---|---|
| BALROG | — | 7.8% |
Reasoning Mixtral 8x7B leads
Mixtral 8x7B: 17.4 (#283), Qwen2.5 7B Instruct: 14.3 (#317)
| Benchmark | Mixtral 8x7B | Qwen2.5 7B Instruct |
|---|---|---|
| DTBench | 49.6% | 47.7% |
| Epoch Capabilities Index | 118.38 | 118.44 |
| Chess Puzzles | — | 0% |
| LMArena Hard Prompts | 1115 | — |
| LMCA | — | 6.4% |
| Adversarial NLI | 55.2% | — |
| ForecastBench | 56.3 | — |
| HellaSwag | 86.7% | — |
| PIQA | 83.6% | — |
| WinoGrande | 77.2% | — |
Math Mixtral 8x7B leads
Mixtral 8x7B: 17.2 (#284), Qwen2.5 7B Instruct: 11.8 (#300)
| Benchmark | Mixtral 8x7B | Qwen2.5 7B Instruct |
|---|---|---|
| Omni-MATH | 10.5% | 29.4% |
| OTIS Mock AIME 2024-2025 | — | 2.5% |
| LMArena Math | 1147 | — |
| MATH Level 5 | 10% | — |
| GSM8K | 74.4% | — |
Knowledge Qwen2.5 7B Instruct leads
Mixtral 8x7B: 11.0 (#296), Qwen2.5 7B Instruct: 17.0 (#281)
| Benchmark | Mixtral 8x7B | Qwen2.5 7B Instruct |
|---|---|---|
| GPQA Diamond | 30.6% | 35.5% |
| MMLU-Pro | 33.5% | 53.9% |
| GPQA (HELM) | 29.6% | 34.1% |
| MMLU | 70.6% | 72.9% |
| LMArena Expert | 1088 | — |
| ARC (AI2) Challenge | 87.3% | — |
| OpenBookQA | 85.8% | — |
| TriviaQA | 82.2% | — |
Multilingual Not comparable
Mixtral 8x7B: 29.8 (#261), Qwen2.5 7B Instruct: —
| Benchmark | Mixtral 8x7B | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Non-English | 1077 | — |
| LMArena Chinese | 1055 | — |
| LMArena French | 1165 | — |
| LMArena German | 1114 | — |
| LMArena Japanese | 931 | — |
| LMArena Korean | 968 | — |
| LMArena Russian | 1090 | — |
| LMArena Spanish | 1110 | — |
Instruction Following Qwen2.5 7B Instruct leads
Mixtral 8x7B: 51.2 (#292), Qwen2.5 7B Instruct: 63.4 (#226)
| Benchmark | Mixtral 8x7B | Qwen2.5 7B Instruct |
|---|---|---|
| IFEval | 57.5% | 74.1% |
| LMArena Instruction Following | 1110 | — |
Long Context Not comparable
Mixtral 8x7B: 33.4 (#255), Qwen2.5 7B Instruct: —
| Benchmark | Mixtral 8x7B | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Longer Query | 1103 | — |
Writing & Preference Qwen2.5 7B Instruct leads
Mixtral 8x7B: 34.4 (#265), Qwen2.5 7B Instruct: 48.8 (#194)
| Benchmark | Mixtral 8x7B | Qwen2.5 7B Instruct |
|---|---|---|
| WildBench | 67.3% | 73.1% |
| LMArena Text | 1132 | — |
| LMArena Creative Writing | 1109 | — |
| LMArena Multi-Turn | 1115 | — |
Frequently asked questions
Is Mixtral 8x7B better than Qwen2.5 7B Instruct?
Qwen2.5 7B Instruct is the stronger model overall, scoring 28.6 to 26.8 on the Noometry Index.
Which is cheaper, Mixtral 8x7B or Qwen2.5 7B Instruct?
Qwen2.5 7B Instruct is cheaper. It lists at $0.17 per million input tokens and $0.70 per million output tokens; Mixtral 8x7B lists at $0.70 and $0.70.
Is Mixtral 8x7B or Qwen2.5 7B Instruct better for coding?
Qwen2.5 7B Instruct scores higher on coding benchmarks: 35.9 versus 32.2 in the Noometry coding category.
Which has the bigger context window?
Qwen2.5 7B Instruct does, with 131K tokens against 32K.
How many benchmarks do Mixtral 8x7B and Qwen2.5 7B Instruct share?
9 benchmarks have published results for both models. Mixtral 8x7B has 38 scored results on Noometry and Qwen2.5 7B Instruct has 15.