Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-5 (Reasoning)
82
Winner · 3/8 categoriesSarvam 30B
48
1/8 categoriesGLM-5 (Reasoning)· Sarvam 30B
Pick GLM-5 (Reasoning) if you want the stronger benchmark profile. Sarvam 30B only becomes the better choice if knowledge is the priority.
GLM-5 (Reasoning) is clearly ahead on the aggregate, 82 to 48. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5 (Reasoning)'s sharpest advantage is in agentic, where it averages 78.3 against 35.5. The single biggest benchmark swing on the page is BrowseComp, 80% to 35.5%. Sarvam 30B does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
GLM-5 (Reasoning) 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-5 (Reasoning) | Sarvam 30B |
|---|---|---|
| AgenticGLM-5 (Reasoning) wins | ||
| Terminal-Bench 2.0 | 81% | — |
| BrowseComp | 80% | 35.5% |
| OSWorld-Verified | 74% | — |
| CodingGLM-5 (Reasoning) wins | ||
| HumanEval | 88% | 92.1% |
| SWE-bench Verified | 62% | 34% |
| LiveCodeBench v6 | — | 70.0% |
| Multimodal & Grounded | ||
| MMMU-Pro | 74% | — |
| OfficeQA Pro | 84% | — |
| Reasoning | ||
| MuSR | 90% | — |
| BBH | 91% | — |
| gpqaDiamond | — | 66.5% |
| KnowledgeSarvam 30B wins | ||
| MMLU | 96% | 85.1% |
| GPQA | 94% | — |
| SuperGPQA | 92% | — |
| MMLU-Pro | 81% | 80% |
| HLE | 29% | — |
| FrontierScience | 83% | — |
| SimpleQA | 92% | — |
| Instruction Following | ||
| Coming soon | ||
| Multilingual | ||
| MGSM | 89% | — |
| MathematicsGLM-5 (Reasoning) wins | ||
| AIME 2023 | 98% | — |
| AIME 2024 | 99% | — |
| AIME 2025 | 98% | 80% |
| HMMT Feb 2023 | 94% | — |
| HMMT Feb 2024 | 96% | — |
| HMMT Feb 2025 | 95% | — |
| BRUMO 2025 | 96% | — |
| MATH-500 | 92% | 97% |
| HMMT Feb 2025 | — | 73.3% |
| HMMT Nov 2025 | — | 74.2% |
GLM-5 (Reasoning) is ahead overall, 82 to 48. The biggest single separator in this matchup is BrowseComp, where the scores are 80% and 35.5%.
Sarvam 30B has the edge for knowledge tasks in this comparison, averaging 80 versus 73.7. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GLM-5 (Reasoning) has the edge for coding in this comparison, averaging 62 versus 34. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
GLM-5 (Reasoning) has the edge for math in this comparison, averaging 95.8 versus 86.5. Inside this category, AIME 2025 is the benchmark that creates the most daylight between them.
GLM-5 (Reasoning) has the edge for agentic tasks in this comparison, averaging 78.3 versus 35.5. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
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