Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Granite-4.0-1B
~40
0/8 categoriesQwen3 235B 2507 (Reasoning)
55
Winner · 3/8 categoriesGranite-4.0-1B· Qwen3 235B 2507 (Reasoning)
Pick Qwen3 235B 2507 (Reasoning) if you want the stronger benchmark profile. Granite-4.0-1B only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Qwen3 235B 2507 (Reasoning) is clearly ahead on the aggregate, 55 to 40. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3 235B 2507 (Reasoning)'s sharpest advantage is in multilingual, where it averages 74.4 against 27.5. The single biggest benchmark swing on the page is MMLU-Pro, 32.9% to 84.4%.
Qwen3 235B 2507 (Reasoning) is the reasoning model in the pair, while Granite-4.0-1B 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.
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 | Granite-4.0-1B | Qwen3 235B 2507 (Reasoning) |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 47% |
| BrowseComp | — | 48% |
| OSWorld-Verified | — | 43% |
| Coding | ||
| HumanEval | 73% | 32% |
| SWE-bench Verified | — | 16% |
| LiveCodeBench | — | 74.1% |
| SWE-bench Pro | — | 29% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 38% |
| OfficeQA Pro | — | 47% |
| Reasoning | ||
| BBH | 59.7% | 63% |
| MuSR | — | 36% |
| LongBench v2 | — | 58% |
| MRCRv2 | — | 58% |
| KnowledgeQwen3 235B 2507 (Reasoning) wins | ||
| MMLU | 59.7% | 40% |
| GPQA | 29.7% | 81.1% |
| MMLU-Pro | 32.9% | 84.4% |
| SuperGPQA | — | 64.9% |
| HLE | — | 6% |
| FrontierScience | — | 42% |
| SimpleQA | — | 38% |
| Instruction FollowingQwen3 235B 2507 (Reasoning) wins | ||
| IFEval | 78.5% | 87.8% |
| MultilingualQwen3 235B 2507 (Reasoning) wins | ||
| MGSM | 27.5% | 62% |
| MMLU-ProX | — | 81% |
| Mathematics | ||
| AIME 2023 | — | 40% |
| AIME 2024 | — | 42% |
| AIME 2025 | — | 92.3% |
| HMMT Feb 2023 | — | 36% |
| HMMT Feb 2024 | — | 38% |
| HMMT Feb 2025 | — | 37% |
| BRUMO 2025 | — | 39% |
| MATH-500 | — | 60% |
Qwen3 235B 2507 (Reasoning) is ahead overall, 55 to 40. The biggest single separator in this matchup is MMLU-Pro, where the scores are 32.9% and 84.4%.
Qwen3 235B 2507 (Reasoning) has the edge for knowledge tasks in this comparison, averaging 50 versus 31.7. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Qwen3 235B 2507 (Reasoning) has the edge for instruction following in this comparison, averaging 87.8 versus 78.5. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Qwen3 235B 2507 (Reasoning) has the edge for multilingual tasks in this comparison, averaging 74.4 versus 27.5. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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