Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Claude Haiku 4.5
63
3/8 categoriesQwen3.5-27B
71
Winner · 4/8 categoriesClaude Haiku 4.5· Qwen3.5-27B
Pick Qwen3.5-27B if you want the stronger benchmark profile. Claude Haiku 4.5 only becomes the better choice if reasoning is the priority or 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 63. 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 48.5. The single biggest benchmark swing on the page is LiveCodeBench, 36% to 80.7%. Claude Haiku 4.5 does hit back in reasoning, so the answer changes if that is the part of the workload you care about most.
Claude Haiku 4.5 is also the more expensive model on tokens at $0.80 input / $4.00 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 is the reasoning model in the pair, while Claude Haiku 4.5 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 200K for Claude Haiku 4.5.
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 | Claude Haiku 4.5 | Qwen3.5-27B |
|---|---|---|
| AgenticClaude Haiku 4.5 wins | ||
| Terminal-Bench 2.0 | 41% | 41.6% |
| BrowseComp | 62% | 61% |
| OSWorld-Verified | 57% | 56.2% |
| tau2-bench | — | 79% |
| CodingQwen3.5-27B wins | ||
| HumanEval | 60% | — |
| SWE-bench Verified | 73.3% | 72.4% |
| LiveCodeBench | 36% | 80.7% |
| SWE-bench Pro | 46% | — |
| FLTEval | 23% | — |
| Multimodal & GroundedClaude Haiku 4.5 wins | ||
| MMMU-Pro | 82% | 75% |
| OfficeQA Pro | 74% | — |
| ReasoningClaude Haiku 4.5 wins | ||
| MuSR | 63% | — |
| BBH | 81% | — |
| LongBench v2 | 72% | 60.6% |
| MRCRv2 | 70% | — |
| KnowledgeQwen3.5-27B wins | ||
| MMLU | 68% | — |
| GPQA | 67% | 85.5% |
| SuperGPQA | 65% | 65.6% |
| MMLU-Pro | 73% | 86.1% |
| HLE | 11% | — |
| FrontierScience | 64% | — |
| SimpleQA | 65% | — |
| Instruction FollowingQwen3.5-27B wins | ||
| IFEval | 86% | 95% |
| MultilingualQwen3.5-27B wins | ||
| MGSM | 82% | — |
| MMLU-ProX | 79% | 82.2% |
| Mathematics | ||
| AIME 2023 | 68% | — |
| AIME 2024 | 70% | — |
| AIME 2025 | 69% | — |
| HMMT Feb 2023 | 64% | — |
| HMMT Feb 2024 | 66% | — |
| HMMT Feb 2025 | 65% | — |
| BRUMO 2025 | 67% | — |
| MATH-500 | 81% | — |
Qwen3.5-27B is ahead overall, 71 to 63. The biggest single separator in this matchup is LiveCodeBench, where the scores are 36% and 80.7%.
Qwen3.5-27B has the edge for knowledge tasks in this comparison, averaging 80.6 versus 54.4. Inside this category, GPQA 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 48.5. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
Claude Haiku 4.5 has the edge for reasoning in this comparison, averaging 68.9 versus 60.6. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
Claude Haiku 4.5 has the edge for agentic tasks in this comparison, averaging 51.9 versus 51.6. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
Claude Haiku 4.5 has the edge for multimodal and grounded tasks in this comparison, averaging 78.4 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 86. 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 80.1. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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