Model comparison
Qwen3.6-27B vs Sarvam 30B
Head-to-head evidence from 11 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Qwen3.6-27B #93 (Estimated); Sarvam 30B #169 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Qwen3.6-27B and Sarvam 30B share 11 comparable benchmark results. 0 of 8 categories are comparable. 43 results are unique to Qwen3.6-27B; 0 to Sarvam 30B.
Updated July 21, 2026- Shared results
- 11
- Qwen3.6-27B only
- 43
- Sarvam 30B only
- 0
- Comparable categories
- 0 / 8
Benchmark data for Qwen3.6-27B and Sarvam 30B is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 11 shared benchmark results across 5 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
Qwen3.6-27B has the larger context window at 262K, compared with 64K for Sarvam 30B.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
| Category | Qwen3.6-27B | Δ | Sarvam 30B |
|---|---|---|---|
| Agentic | Qwen3.6-27B59.3 | MarginNo overlap | Sarvam 30BNot measured |
| Coding | Qwen3.6-27B77.5 | MarginNo overlap | Sarvam 30BNot measured |
| Knowledge | Qwen3.6-27B53.3 | MarginNo overlap | Sarvam 30BNot measured |
| Math | Qwen3.6-27B89.2 | MarginNo overlap | Sarvam 30BNot measured |
| Multimodal | Qwen3.6-27B76.7 | MarginNo overlap | Sarvam 30BNot measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Qwen3.6-27B | Sarvam 30B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Qwen3.6-27B$0 input / $0 output | Sarvam 30B$0 input / $0 output | Listed prices are equal. |
| Generation speedtokens per second | Qwen3.6-27BNot available | Sarvam 30BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Qwen3.6-27BNot available | Sarvam 30BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Qwen3.6-27B262K | Sarvam 30B64K | Qwen3.6-27B lists the larger context window. |
Benchmark Deep Dive
Agentic10 benchmarks
| Benchmark | Qwen3.6-27B | Sarvam 30B | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 59.3% | — | Not comparable |
| Claw-EvalSource | 72.4% | — | Not comparable |
| QwenClawBenchSource | 53.4% | — | Not comparable |
| QwenWebBenchSource | 1487 | — | Not comparable |
| AndroidWorldSource | 70.3% | — | Not comparable |
| AA Agentic IndexSource | 27.0% | — | Not comparable |
| τ²-bench resultsSource | 94.2% | 34.5% | Qwen3.6-27B leads |
| GDPval-AASource | 32.0% | — | Not comparable |
| GDPval-AASource | 1140 | — | Not comparable |
| Gert LabsSource | 54.84% | — | Not comparable |
Coding8 benchmarks
| Benchmark | Qwen3.6-27B | Sarvam 30B | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 77.2% | — | Not comparable |
| SWE MultilingualSource | 71.3% | — | Not comparable |
| SWE-bench ProSource | 53.5% | — | Not comparable |
| Terminal-Bench 2.0Source | 59.3% | — | Not comparable |
| LiveCodeBenchSource | 83.9% | — | Not comparable |
| NL2RepoSource | 36.2% | — | Not comparable |
| AA Coding IndexSource | 53.7% | — | Not comparable |
| AA-SciCodeSource | 39.8% | 19.2% | Qwen3.6-27B leads |
Reasoning2 benchmarks
Knowledge12 benchmarks
| Benchmark | Qwen3.6-27B | Sarvam 30B | Result |
|---|---|---|---|
| MMLU-ProSource | 86.2% | — | Not comparable |
| MMLU-ReduxSource | 93.5% | — | Not comparable |
| SuperGPQASource | 66% | — | Not comparable |
| C-EvalSource | 91.4% | — | Not comparable |
| GPQASource | 87.8% | — | Not comparable |
| HLESource | 24% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 37.0% | 6.6% | Qwen3.6-27B leads |
| AA-GPQA DiamondSource | 84.2% | 63.3% | Qwen3.6-27B leads |
| AA-HLESource | 21.6% | 7.0% | Qwen3.6-27B leads |
| AA-Omniscience IndexSource | -19.8% | -72.0% | Qwen3.6-27B leads |
| AA-Omniscience AccuracySource | 19.2% | 12.7% | Qwen3.6-27B leads |
| AA-Omniscience Hallucination RateSource | 48.3% | 97.0% | Qwen3.6-27B leads |
Math5 benchmarks
Multimodal16 benchmarks
| Benchmark | Qwen3.6-27B | Sarvam 30B | Result |
|---|---|---|---|
| MMMUSource | 82.9% | — | Not comparable |
| MMMU-ProSource | 75.8% | — | Not comparable |
| RealWorldQASource | 84.1% | — | Not comparable |
| DynaMathSource | 85.6% | — | Not comparable |
| MStarSource | 81.4% | — | Not comparable |
| SimpleVQASource | 56.1% | — | Not comparable |
| CharXivSource | 78.4% | — | Not comparable |
| CC-OCRSource | 81.2% | — | Not comparable |
| CountBenchSource | 97.8% | — | Not comparable |
| RefCOCO (avg)Source | 92.5% | — | Not comparable |
| ERQASource | 62.5% | — | Not comparable |
| Video-MME (with subtitle)Source | 87.7% | — | Not comparable |
| VideoMMMUSource | 84.4% | — | Not comparable |
| MLVU (M-Avg)Source | 86.6% | — | Not comparable |
| V*Source | 94.7% | — | Not comparable |
| AA-MMMU-ProSource | 74.6% | — | Not comparable |
Inst. Following1 benchmarks
| Benchmark | Qwen3.6-27B | Sarvam 30B | Result |
|---|---|---|---|
| AA-IFBenchSource | 67.6% | 26.5% | Qwen3.6-27B leads |
Frequently Asked Questions (3)
Can I compare Qwen3.6-27B and Sarvam 30B on BenchLM yet?
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
Why does this comparison show “coming soon”?
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
What data is available for Qwen3.6-27B and Sarvam 30B today?
Qwen3.6-27B: $0.00 input / $0.00 output per 1M tokens Sarvam 30B: $0.00 input / $0.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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