Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-4.7
74
Winner · 3/8 categoriesSarvam 30B
48
1/8 categoriesGLM-4.7· Sarvam 30B
Pick GLM-4.7 if you want the stronger benchmark profile. Sarvam 30B only becomes the better choice if knowledge is the priority.
GLM-4.7 is clearly ahead on the aggregate, 74 to 48. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-4.7's sharpest advantage is in coding, where it averages 69.3 against 34. The single biggest benchmark swing on the page is SWE-bench Verified, 73.8% to 34%. Sarvam 30B does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
GLM-4.7 gives you the larger context window at 200K, compared with 64K for Sarvam 30B.
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 | GLM-4.7 | Sarvam 30B |
|---|---|---|
| AgenticGLM-4.7 wins | ||
| OSWorld-Verified | 61% | — |
| VITA-Bench | 15.5% | — |
| BrowseComp | — | 35.5% |
| CodingGLM-4.7 wins | ||
| HumanEval | 94.2% | 92.1% |
| SWE-bench Verified | 73.8% | 34% |
| LiveCodeBench | 84.9% | — |
| SWE-bench Pro | 51% | — |
| LiveCodeBench v6 | — | 70.0% |
| Multimodal & Grounded | ||
| MMMU-Pro | 66% | — |
| OfficeQA Pro | 76% | — |
| Reasoning | ||
| MuSR | 80% | — |
| BBH | 84% | — |
| LongBench v2 | 79% | — |
| MRCRv2 | 78% | — |
| gpqaDiamond | — | 66.5% |
| KnowledgeSarvam 30B wins | ||
| MMLU | 86% | 85.1% |
| GPQA | 85.7% | — |
| SuperGPQA | 82% | — |
| MMLU-Pro | 84.3% | 80% |
| FrontierScience | 72% | — |
| SimpleQA | 46% | — |
| Instruction Following | ||
| Coming soon | ||
| Multilingual | ||
| MGSM | 94% | — |
| MMLU-ProX | 78% | — |
| MathematicsGLM-4.7 wins | ||
| AIME 2023 | 86% | — |
| AIME 2024 | 88% | — |
| AIME 2025 | 95.7% | 80% |
| HMMT Feb 2023 | 82% | — |
| HMMT Feb 2025 | 97.1% | — |
| BRUMO 2025 | 85% | — |
| MATH-500 | — | 97% |
| HMMT Feb 2025 | — | 73.3% |
| HMMT Nov 2025 | — | 74.2% |
GLM-4.7 is ahead overall, 74 to 48. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 73.8% and 34%.
Sarvam 30B has the edge for knowledge tasks in this comparison, averaging 80 versus 74.8. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
GLM-4.7 has the edge for coding in this comparison, averaging 69.3 versus 34. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
GLM-4.7 has the edge for math in this comparison, averaging 90.7 versus 86.5. Inside this category, AIME 2025 is the benchmark that creates the most daylight between them.
GLM-4.7 has the edge for agentic tasks in this comparison, averaging 61 versus 35.5. Sarvam 30B stays close enough that the answer can still flip depending on your workload.
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