Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Qwen3.5-27B
70
Winner · 6/8 categoriesQwen3.5-35B-A3B
68
1/8 categoriesQwen3.5-27B· Qwen3.5-35B-A3B
Pick Qwen3.5-27B if you want the stronger benchmark profile. Qwen3.5-35B-A3B only becomes the better choice if multimodal & grounded is the priority.
Qwen3.5-27B has the cleaner overall profile here, landing at 70 versus 68. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
Qwen3.5-27B's sharpest advantage is in coding, where it averages 77.6 against 72.6. The single biggest benchmark swing on the page is LiveCodeBench, 80.7% to 74.6%. Qwen3.5-35B-A3B does hit back in multimodal & grounded, so the answer changes if that is the part of the workload you care about most.
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 | Qwen3.5-27B | Qwen3.5-35B-A3B |
|---|---|---|
| AgenticQwen3.5-27B wins | ||
| Terminal-Bench 2.0 | 41.6% | 40.5% |
| BrowseComp | 61% | 61% |
| OSWorld-Verified | 56.2% | 54.5% |
| tau2-bench | 79% | 81.2% |
| CodingQwen3.5-27B wins | ||
| SWE-bench Verified | 72.4% | 69.2% |
| LiveCodeBench | 80.7% | 74.6% |
| Multimodal & GroundedQwen3.5-35B-A3B wins | ||
| MMMU-Pro | 75% | 75.1% |
| ReasoningQwen3.5-27B wins | ||
| LongBench v2 | 60.6% | 59% |
| KnowledgeQwen3.5-27B wins | ||
| MMLU-Pro | 86.1% | 85.3% |
| SuperGPQA | 65.6% | 63.4% |
| GPQA | 85.5% | 84.2% |
| Instruction FollowingQwen3.5-27B wins | ||
| IFEval | 95% | 91.9% |
| MultilingualQwen3.5-27B wins | ||
| MMLU-ProX | 82.2% | 81% |
| Mathematics | ||
| Coming soon | ||
Qwen3.5-27B is ahead overall, 70 to 68. The biggest single separator in this matchup is LiveCodeBench, where the scores are 80.7% and 74.6%.
Qwen3.5-27B has the edge for knowledge tasks in this comparison, averaging 80.6 versus 79.3. Inside this category, SuperGPQA is the benchmark that creates the most daylight between them.
Qwen3.5-27B has the edge for coding in this comparison, averaging 77.6 versus 72.6. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
Qwen3.5-27B has the edge for reasoning in this comparison, averaging 60.6 versus 59. 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 50.5. Inside this category, tau2-bench is the benchmark that creates the most daylight between them.
Qwen3.5-35B-A3B has the edge for multimodal and grounded tasks in this comparison, averaging 75.1 versus 75. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Qwen3.5-27B has the edge for instruction following in this comparison, averaging 95 versus 91.9. Inside this category, IFEval 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 81. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
Get notified when new models drop, benchmark scores change, or the leaderboard shifts. One email per week.
Free. No spam. Unsubscribe anytime. We only store derived location metadata for consent routing.