Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-5
75
Winner · 3/8 categoriesSarvam 30B
48
1/8 categoriesGLM-5· Sarvam 30B
Pick GLM-5 if you want the stronger benchmark profile. Sarvam 30B only becomes the better choice if knowledge is the priority or you want the stronger reasoning-first profile.
GLM-5 is clearly ahead on the aggregate, 75 to 48. 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 coding, where it averages 60.4 against 34. The single biggest benchmark swing on the page is SWE-bench Verified, 77.8% 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.
Sarvam 30B 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. GLM-5 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 | Sarvam 30B |
|---|---|---|
| AgenticGLM-5 wins | ||
| Terminal-Bench 2.0 | 56.2% | — |
| BrowseComp | 62% | 35.5% |
| 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% | — |
| BFCL v4 | 70.8% | — |
| CodingGLM-5 wins | ||
| HumanEval | 80% | 92.1% |
| SWE-bench Verified | 77.8% | 34% |
| SWE-bench Verified* | 72.8% | — |
| LiveCodeBench | 52% | — |
| LiveCodeBench v6 | 85.6% | 70.0% |
| SWE-bench Pro | 55.1% | — |
| SWE Multilingual | 73.3% | — |
| NL2Repo | 35.9% | — |
| SWE-Rebench | 62.8% | — |
| React Native Evals | 74.2% | — |
| Multimodal & Grounded | ||
| MMMU-Pro | 66% | — |
| OfficeQA Pro | 73% | — |
| Reasoning | ||
| MuSR | 82% | — |
| BBH | 83% | — |
| LongBench v2 | 60.8% | — |
| MRCRv2 | 73% | — |
| AI-Needle | 63.3% | — |
| gpqaDiamond | — | 66.5% |
| KnowledgeSarvam 30B wins | ||
| MMLU | 91.7% | 85.1% |
| GPQA | 86% | — |
| GPQA-D | 81.6% | — |
| SuperGPQA | 66.8% | — |
| MMLU-Pro | 85.7% | 80% |
| MMLU-Pro (Arcee) | 85.8% | — |
| MMLU-Redux | 94.4% | — |
| C-Eval | 92.8% | — |
| HLE | 27.2% | — |
| 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% | — |
| MathematicsGLM-5 wins | ||
| AIME 2023 | 88% | — |
| AIME 2024 | 90% | — |
| AIME 2025 | 93.3% | 80% |
| AIME26 | 95.8% | — |
| AIME25 (Arcee) | 93.3% | — |
| HMMT Feb 2023 | 84% | — |
| HMMT Feb 2024 | 86% | — |
| HMMT Feb 2025 | 85% | — |
| HMMT Feb 2025 | 97.5% | 73.3% |
| HMMT Nov 2025 | 96.9% | 74.2% |
| HMMT Feb 2026 | 86.4% | — |
| MMAnswerBench | 82.5% | — |
| BRUMO 2025 | 87% | — |
| MATH-500 | 97.4% | 97% |
GLM-5 is ahead overall, 75 to 48. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 77.8% and 34%.
Sarvam 30B has the edge for knowledge tasks in this comparison, averaging 80 versus 67.7. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GLM-5 has the edge for coding in this comparison, averaging 60.4 versus 34. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
GLM-5 has the edge for math in this comparison, averaging 92.1 versus 86.5. Inside this category, HMMT Feb 2025 is the benchmark that creates the most daylight between them.
GLM-5 has the edge for agentic tasks in this comparison, averaging 58.3 versus 35.5. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
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