Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemma 4 26B A4B
64
Winner · 2/8 categoriesQwen2.5-1M
62
2/8 categoriesGemma 4 26B A4B· Qwen2.5-1M
Pick Gemma 4 26B A4B if you want the stronger benchmark profile. Qwen2.5-1M only becomes the better choice if reasoning is the priority or you need the larger 1M context window.
Gemma 4 26B A4B has the cleaner overall profile here, landing at 64 versus 62. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
Gemma 4 26B A4B's sharpest advantage is in coding, where it averages 77.1 against 45.1. The single biggest benchmark swing on the page is LiveCodeBench, 77.1% to 40%. Qwen2.5-1M does hit back in reasoning, so the answer changes if that is the part of the workload you care about most.
Gemma 4 26B A4B is the reasoning model in the pair, while Qwen2.5-1M 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. Qwen2.5-1M gives you the larger context window at 1M, compared with 256K for Gemma 4 26B A4B.
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 | Gemma 4 26B A4B | Qwen2.5-1M |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 65% |
| BrowseComp | — | 72% |
| OSWorld-Verified | — | 59% |
| CodingGemma 4 26B A4B wins | ||
| LiveCodeBench | 77.1% | 40% |
| HumanEval | — | 76% |
| SWE-bench Verified | — | 47% |
| SWE-bench Pro | — | 49% |
| Multimodal & GroundedGemma 4 26B A4B wins | ||
| MMMU-Pro | 73.8% | 63% |
| OfficeQA Pro | — | 75% |
| ReasoningQwen2.5-1M wins | ||
| BBH | 64.8% | 82% |
| MRCRv2 | 44.1% | 81% |
| MuSR | — | 79% |
| LongBench v2 | — | 82% |
| KnowledgeQwen2.5-1M wins | ||
| GPQA | 82.3% | 83% |
| MMLU-Pro | 82.6% | 74% |
| HLE | 17.2% | 10% |
| HLE w/o tools | 8.7% | — |
| MMLU | — | 84% |
| SuperGPQA | — | 81% |
| FrontierScience | — | 74% |
| SimpleQA | — | 81% |
| Instruction Following | ||
| IFEval | — | 84% |
| Multilingual | ||
| MGSM | — | 81% |
| MMLU-ProX | — | 80% |
| Mathematics | ||
| AIME 2023 | — | 85% |
| AIME 2024 | — | 87% |
| AIME 2025 | — | 86% |
| HMMT Feb 2023 | — | 81% |
| HMMT Feb 2024 | — | 83% |
| HMMT Feb 2025 | — | 82% |
| BRUMO 2025 | — | 84% |
| MATH-500 | — | 83% |
Gemma 4 26B A4B is ahead overall, 64 to 62. The biggest single separator in this matchup is LiveCodeBench, where the scores are 77.1% and 40%.
Qwen2.5-1M has the edge for knowledge tasks in this comparison, averaging 62.1 versus 56.1. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Gemma 4 26B A4B has the edge for coding in this comparison, averaging 77.1 versus 45.1. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
Qwen2.5-1M has the edge for reasoning in this comparison, averaging 80.9 versus 44.1. Inside this category, MRCRv2 is the benchmark that creates the most daylight between them.
Gemma 4 26B A4B has the edge for multimodal and grounded tasks in this comparison, averaging 73.8 versus 68.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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