Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Mixtral 8x22B Instruct v0.1
36
0/8 categoriesQwen3.5-27B
71
Winner · 6/8 categoriesMixtral 8x22B Instruct v0.1· Qwen3.5-27B
Pick Qwen3.5-27B if you want the stronger benchmark profile. Mixtral 8x22B Instruct v0.1 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Qwen3.5-27B is clearly ahead on the aggregate, 71 to 36. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.5-27B's sharpest advantage is in multilingual, where it averages 82.2 against 42. The single biggest benchmark swing on the page is MMLU-ProX, 42% to 82.2%.
Qwen3.5-27B is the reasoning model in the pair, while Mixtral 8x22B Instruct v0.1 is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. Qwen3.5-27B gives you the larger context window at 262K, compared with 64K for Mixtral 8x22B Instruct v0.1.
BenchLM keeps the benchmark table and the operator tradeoffs on the same page so a better score does not hide a materially slower, pricier, or smaller-context model.
Runtime metrics show N/A when BenchLM does not have a sourced snapshot for that exact model. The scoring rules and freshness policy are documented on the methodology page.
| Benchmark | Mixtral 8x22B Instruct v0.1 | Qwen3.5-27B |
|---|---|---|
| AgenticQwen3.5-27B wins | ||
| Terminal-Bench 2.0 | 35% | 41.6% |
| BrowseComp | 32% | 61% |
| OSWorld-Verified | 28% | 56.2% |
| tau2-bench | — | 79% |
| CodingQwen3.5-27B wins | ||
| HumanEval | 54.8% | — |
| SWE-bench Pro | 40% | — |
| SWE-bench Verified | — | 72.4% |
| LiveCodeBench | — | 80.7% |
| Multimodal & GroundedQwen3.5-27B wins | ||
| MMMU-Pro | 35% | 75% |
| OfficeQA Pro | 36% | — |
| ReasoningQwen3.5-27B wins | ||
| LongBench v2 | 39% | 60.6% |
| MRCRv2 | 38% | — |
| KnowledgeQwen3.5-27B wins | ||
| MMLU | 77.8% | — |
| FrontierScience | 53% | — |
| MMLU-Pro | — | 86.1% |
| SuperGPQA | — | 65.6% |
| GPQA | — | 85.5% |
| Instruction Following | ||
| IFEval | — | 95% |
| MultilingualQwen3.5-27B wins | ||
| MMLU-ProX | 42% | 82.2% |
| Mathematics | ||
| Coming soon | ||
Qwen3.5-27B is ahead overall, 71 to 36. The biggest single separator in this matchup is MMLU-ProX, where the scores are 42% and 82.2%.
Qwen3.5-27B has the edge for knowledge tasks in this comparison, averaging 80.6 versus 53. Mixtral 8x22B Instruct v0.1 stays close enough that the answer can still flip depending on your workload.
Qwen3.5-27B has the edge for coding in this comparison, averaging 77.6 versus 40. Mixtral 8x22B Instruct v0.1 stays close enough that the answer can still flip depending on your workload.
Qwen3.5-27B has the edge for reasoning in this comparison, averaging 60.6 versus 38.5. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
Qwen3.5-27B has the edge for agentic tasks in this comparison, averaging 51.6 versus 31.8. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
Qwen3.5-27B has the edge for multimodal and grounded tasks in this comparison, averaging 75 versus 35.5. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Qwen3.5-27B has the edge for multilingual tasks in this comparison, averaging 82.2 versus 42. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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