Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemma 4 26B A4B
64
Winner · 4/8 categoriesGPT-5.4 nano
58
0/8 categoriesGemma 4 26B A4B· GPT-5.4 nano
Pick Gemma 4 26B A4B if you want the stronger benchmark profile. GPT-5.4 nano only becomes the better choice if you need the larger 400K context window.
Gemma 4 26B A4B is clearly ahead on the aggregate, 64 to 58. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Gemma 4 26B A4B's sharpest advantage is in coding, where it averages 77.1 against 52.4. The single biggest benchmark swing on the page is HLE, 17.2% to 37.7%.
GPT-5.4 nano is also the more expensive model on tokens at $0.20 input / $1.25 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Gemma 4 26B A4B. That is roughly Infinityx on output cost alone. GPT-5.4 nano gives you the larger context window at 400K, 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 | GPT-5.4 nano |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 46.3% |
| OSWorld-Verified | — | 39% |
| MCP Atlas | — | 56.1% |
| Toolathlon | — | 35.5% |
| Tau2-Telecom | — | 92.5% |
| CodingGemma 4 26B A4B wins | ||
| LiveCodeBench | 77.1% | — |
| SWE-bench Pro | — | 52.4% |
| Multimodal & GroundedGemma 4 26B A4B wins | ||
| MMMU-Pro | 73.8% | 66.1% |
| MMMU-Pro w/ Python | — | 69.5% |
| ReasoningGemma 4 26B A4B wins | ||
| BBH | 64.8% | — |
| MRCRv2 | 44.1% | 38.7% |
| MRCR v2 64K-128K | — | 44.2% |
| MRCR v2 128K-256K | — | 33.1% |
| Graphwalks BFS 128K | — | 73.4% |
| Graphwalks Parents 128K | — | 50.8% |
| KnowledgeGemma 4 26B A4B wins | ||
| GPQA | 82.3% | 82.8% |
| MMLU-Pro | 82.6% | — |
| HLE | 17.2% | 37.7% |
| HLE w/o tools | 8.7% | 24.3% |
| Instruction Following | ||
| Coming soon | ||
| Multilingual | ||
| Coming soon | ||
| Mathematics | ||
| Coming soon | ||
Gemma 4 26B A4B is ahead overall, 64 to 58. The biggest single separator in this matchup is HLE, where the scores are 17.2% and 37.7%.
Gemma 4 26B A4B has the edge for knowledge tasks in this comparison, averaging 56.1 versus 53.2. Inside this category, HLE 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 52.4. GPT-5.4 nano stays close enough that the answer can still flip depending on your workload.
Gemma 4 26B A4B has the edge for reasoning in this comparison, averaging 44.1 versus 38.7. 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 66.1. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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