Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemma 4 26B A4B
64
2/8 categoriesGLM-5
75
Winner · 2/8 categoriesGemma 4 26B A4B· GLM-5
Pick GLM-5 if you want the stronger benchmark profile. Gemma 4 26B A4B only becomes the better choice if coding is the priority or you need the larger 256K context window.
GLM-5 is clearly ahead on the aggregate, 75 to 64. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5's sharpest advantage is in reasoning, where it averages 70.5 against 44.1. The single biggest benchmark swing on the page is MRCRv2, 44.1% to 73%. Gemma 4 26B A4B does hit back in coding, 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 GLM-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. Gemma 4 26B A4B gives you the larger context window at 256K, compared with 200K for GLM-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 | Gemma 4 26B A4B | GLM-5 |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 56.2% |
| BrowseComp | — | 62% |
| OSWorld-Verified | — | 58% |
| Claw-Eval | — | 57.7% |
| QwenClawBench | — | 54.1% |
| QwenWebBench | — | 1315 |
| TAU3-Bench | — | 65.6% |
| VITA-Bench | — | 37.0% |
| DeepPlanning | — | 14.6% |
| Toolathlon | — | 38% |
| MCP Atlas | — | 31.1% |
| MCP-Tasks | — | 60.8% |
| WideResearch | — | 69.8% |
| Tau2-Airline | — | 80.5% |
| Tau2-Telecom | — | 98.2% |
| PinchBench | — | 86.4% |
| BFCL v4 | — | 70.8% |
| CodingGemma 4 26B A4B wins | ||
| LiveCodeBench | 77.1% | 52% |
| HumanEval | — | 80% |
| SWE-bench Verified | — | 77.8% |
| SWE-bench Verified* | — | 72.8% |
| LiveCodeBench v6 | — | 85.6% |
| SWE-bench Pro | — | 55.1% |
| SWE Multilingual | — | 73.3% |
| NL2Repo | — | 35.9% |
| SWE-Rebench | — | 62.8% |
| React Native Evals | — | 74.2% |
| Multimodal & GroundedGemma 4 26B A4B wins | ||
| MMMU-Pro | 73.8% | 66% |
| OfficeQA Pro | — | 73% |
| ReasoningGLM-5 wins | ||
| BBH | 64.8% | 83% |
| MRCRv2 | 44.1% | 73% |
| MuSR | — | 82% |
| LongBench v2 | — | 60.8% |
| AI-Needle | — | 63.3% |
| KnowledgeGLM-5 wins | ||
| GPQA | 82.3% | 86% |
| MMLU-Pro | 82.6% | 85.7% |
| HLE | 17.2% | 27.2% |
| HLE w/o tools | 8.7% | — |
| MMLU | — | 91.7% |
| GPQA-D | — | 81.6% |
| SuperGPQA | — | 66.8% |
| MMLU-Pro (Arcee) | — | 85.8% |
| MMLU-Redux | — | 94.4% |
| C-Eval | — | 92.8% |
| FrontierScience | — | 74% |
| SimpleQA | — | 84% |
| Instruction Following | ||
| IFEval | — | 92.6% |
| IFBench | — | 72.3% |
| Multilingual | ||
| MGSM | — | 84% |
| MMLU-ProX | — | 83.1% |
| NOVA-63 | — | 55.1% |
| INCLUDE | — | 84.9% |
| PolyMath | — | 65.2% |
| VWT2k-lite | — | 82.1% |
| MAXIFE | — | 85.6% |
| Mathematics | ||
| AIME 2023 | — | 88% |
| AIME 2024 | — | 90% |
| AIME 2025 | — | 93.3% |
| AIME26 | — | 95.8% |
| AIME25 (Arcee) | — | 93.3% |
| HMMT Feb 2023 | — | 84% |
| HMMT Feb 2024 | — | 86% |
| HMMT Feb 2025 | — | 85% |
| HMMT Feb 2025 | — | 97.5% |
| HMMT Nov 2025 | — | 96.9% |
| HMMT Feb 2026 | — | 86.4% |
| MMAnswerBench | — | 82.5% |
| BRUMO 2025 | — | 87% |
| MATH-500 | — | 97.4% |
GLM-5 is ahead overall, 75 to 64. The biggest single separator in this matchup is MRCRv2, where the scores are 44.1% and 73%.
GLM-5 has the edge for knowledge tasks in this comparison, averaging 67.7 versus 56.1. 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 60.4. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
GLM-5 has the edge for reasoning in this comparison, averaging 70.5 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 69.2. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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