Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Qwen3.5-27B
70
Winner · 3/8 categoriesSarvam 30B
48
0/8 categoriesQwen3.5-27B· Sarvam 30B
Pick Qwen3.5-27B if you want the stronger benchmark profile. Sarvam 30B only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
Qwen3.5-27B is clearly ahead on the aggregate, 70 to 48. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.5-27B's sharpest advantage is in coding, where it averages 77.6 against 34. The single biggest benchmark swing on the page is SWE-bench Verified, 72.4% to 34%.
Qwen3.5-27B gives you the larger context window at 262K, 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 | Qwen3.5-27B | Sarvam 30B |
|---|---|---|
| AgenticQwen3.5-27B wins | ||
| Terminal-Bench 2.0 | 41.6% | — |
| BrowseComp | 61% | 35.5% |
| OSWorld-Verified | 56.2% | — |
| Tau2-Telecom | 79% | — |
| Claw-Eval | 20.2% | — |
| CodingQwen3.5-27B wins | ||
| SWE-bench Verified | 72.4% | 34% |
| LiveCodeBench | 80.7% | — |
| HumanEval | — | 92.1% |
| LiveCodeBench v6 | — | 70.0% |
| Multimodal & Grounded | ||
| MMMU-Pro | 75% | — |
| Reasoning | ||
| LongBench v2 | 60.6% | — |
| gpqaDiamond | — | 66.5% |
| KnowledgeQwen3.5-27B wins | ||
| MMLU-Pro | 86.1% | 80% |
| SuperGPQA | 65.6% | — |
| GPQA | 85.5% | — |
| MMLU | — | 85.1% |
| Instruction Following | ||
| IFEval | 95% | — |
| Multilingual | ||
| MMLU-ProX | 82.2% | — |
| Mathematics | ||
| MATH-500 | — | 97% |
| AIME 2025 | — | 80% |
| HMMT Feb 2025 | — | 73.3% |
| HMMT Nov 2025 | — | 74.2% |
Qwen3.5-27B is ahead overall, 70 to 48. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 72.4% and 34%.
Qwen3.5-27B has the edge for knowledge tasks in this comparison, averaging 80.6 versus 80. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Qwen3.5-27B has the edge for coding in this comparison, averaging 77.6 versus 34. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Qwen3.5-27B has the edge for agentic tasks in this comparison, averaging 51.6 versus 35.5. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
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