Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemini 3 Pro Deep Think
80
Winner · 4/8 categoriesQwen3.5-35B-A3B
67
3/8 categoriesGemini 3 Pro Deep Think· Qwen3.5-35B-A3B
Pick Gemini 3 Pro Deep Think if you want the stronger benchmark profile. Qwen3.5-35B-A3B only becomes the better choice if coding is the priority.
Gemini 3 Pro Deep Think is clearly ahead on the aggregate, 80 to 67. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Gemini 3 Pro Deep Think's sharpest advantage is in agentic, where it averages 78.1 against 50.5. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 77% to 40.5%. Qwen3.5-35B-A3B does hit back in coding, so the answer changes if that is the part of the workload you care about most.
Gemini 3 Pro Deep Think gives you the larger context window at 2M, compared with 262K for Qwen3.5-35B-A3B.
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 | Gemini 3 Pro Deep Think | Qwen3.5-35B-A3B |
|---|---|---|
| AgenticGemini 3 Pro Deep Think wins | ||
| Terminal-Bench 2.0 | 77% | 40.5% |
| BrowseComp | 87% | 61% |
| OSWorld-Verified | 73% | 54.5% |
| tau2-bench | — | 81.2% |
| CodingQwen3.5-35B-A3B wins | ||
| HumanEval | 91% | — |
| SWE-bench Verified | 58% | 69.2% |
| LiveCodeBench | 58% | 74.6% |
| SWE-bench Pro | 63% | — |
| Multimodal & GroundedGemini 3 Pro Deep Think wins | ||
| MMMU-Pro | 95% | 75.1% |
| OfficeQA Pro | 95% | — |
| ReasoningGemini 3 Pro Deep Think wins | ||
| MuSR | 93% | — |
| BBH | 95% | — |
| LongBench v2 | 94% | 59% |
| MRCRv2 | 96% | — |
| ARC-AGI-2 | 45.1% | — |
| KnowledgeQwen3.5-35B-A3B wins | ||
| MMLU | 99% | — |
| GPQA | 97% | 84.2% |
| SuperGPQA | 95% | 63.4% |
| MMLU-Pro | 81% | 85.3% |
| HLE | 32% | — |
| FrontierScience | 88% | — |
| SimpleQA | 95% | — |
| Instruction FollowingQwen3.5-35B-A3B wins | ||
| IFEval | 89% | 91.9% |
| MultilingualGemini 3 Pro Deep Think wins | ||
| MGSM | 92% | — |
| MMLU-ProX | 85% | 81% |
| Mathematics | ||
| AIME 2023 | 99% | — |
| AIME 2024 | 99% | — |
| AIME 2025 | 98% | — |
| HMMT Feb 2023 | 95% | — |
| HMMT Feb 2024 | 97% | — |
| HMMT Feb 2025 | 96% | — |
| BRUMO 2025 | 96% | — |
| MATH-500 | 92% | — |
Gemini 3 Pro Deep Think is ahead overall, 80 to 67. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 77% and 40.5%.
Qwen3.5-35B-A3B has the edge for knowledge tasks in this comparison, averaging 79.3 versus 76.4. Inside this category, SuperGPQA is the benchmark that creates the most daylight between them.
Qwen3.5-35B-A3B has the edge for coding in this comparison, averaging 72.6 versus 59.9. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
Gemini 3 Pro Deep Think has the edge for reasoning in this comparison, averaging 82.1 versus 59. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
Gemini 3 Pro Deep Think has the edge for agentic tasks in this comparison, averaging 78.1 versus 50.5. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Gemini 3 Pro Deep Think has the edge for multimodal and grounded tasks in this comparison, averaging 95 versus 75.1. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Qwen3.5-35B-A3B has the edge for instruction following in this comparison, averaging 91.9 versus 89. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Gemini 3 Pro Deep Think has the edge for multilingual tasks in this comparison, averaging 87.4 versus 81. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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