Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek-R1
45
0/8 categoriesQwen3.5-27B
70
Winner · 7/8 categoriesDeepSeek-R1· Qwen3.5-27B
Pick Qwen3.5-27B if you want the stronger benchmark profile. DeepSeek-R1 only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
Qwen3.5-27B is clearly ahead on the aggregate, 70 to 45. 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 coding, where it averages 77.6 against 28.3. The single biggest benchmark swing on the page is LiveCodeBench, 19% to 80.7%.
DeepSeek-R1 is also the more expensive model on tokens at $0.55 input / $2.19 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen3.5-27B. That is roughly Infinityx on output cost alone. Qwen3.5-27B gives you the larger context window at 262K, compared with 128K for DeepSeek-R1.
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 | DeepSeek-R1 | Qwen3.5-27B |
|---|---|---|
| AgenticQwen3.5-27B wins | ||
| Terminal-Bench 2.0 | 42% | 41.6% |
| BrowseComp | 49% | 61% |
| OSWorld-Verified | 44% | 56.2% |
| tau2-bench | — | 79% |
| CodingQwen3.5-27B wins | ||
| HumanEval | 92% | — |
| SWE-bench Verified | 49.2% | 72.4% |
| LiveCodeBench | 19% | 80.7% |
| SWE-bench Pro | 25% | — |
| Multimodal & GroundedQwen3.5-27B wins | ||
| MMMU-Pro | 43% | 75% |
| OfficeQA Pro | 53% | — |
| ReasoningQwen3.5-27B wins | ||
| MuSR | 40% | — |
| BBH | 66% | — |
| LongBench v2 | 58% | 60.6% |
| MRCRv2 | 57% | — |
| ARC-AGI-2 | 1.3% | — |
| KnowledgeQwen3.5-27B wins | ||
| MMLU | 90.8% | — |
| GPQA | 71.5% | 85.5% |
| SuperGPQA | 41% | 65.6% |
| MMLU-Pro | 84% | 86.1% |
| HLE | 14% | — |
| FrontierScience | 44% | — |
| SimpleQA | 30.1% | — |
| Instruction FollowingQwen3.5-27B wins | ||
| IFEval | 83.3% | 95% |
| MultilingualQwen3.5-27B wins | ||
| MGSM | 61% | — |
| MMLU-ProX | 60% | 82.2% |
| Mathematics | ||
| AIME 2023 | 44% | — |
| AIME 2024 | 79.8% | — |
| AIME 2025 | 45% | — |
| HMMT Feb 2023 | 40% | — |
| HMMT Feb 2024 | 42% | — |
| HMMT Feb 2025 | 41% | — |
| BRUMO 2025 | 43% | — |
| MATH-500 | 97.3% | — |
Qwen3.5-27B is ahead overall, 70 to 45. The biggest single separator in this matchup is LiveCodeBench, where the scores are 19% and 80.7%.
Qwen3.5-27B has the edge for knowledge tasks in this comparison, averaging 80.6 versus 47. 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 28.3. 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 40. 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 44.5. Inside this category, OSWorld-Verified 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 47.5. 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 83.3. 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 60.4. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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