Model comparison
Qwen3.6-35B-A3B vs Sarvam 105B
Head-to-head evidence from 12 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Verified leaderboard positions: Qwen3.6-35B-A3B #31; Sarvam 105B unranked
BenchAlign evidence: Qwen3.6-35B-A3B estimated; Sarvam 105B estimated. Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Qwen3.6-35B-A3B and Sarvam 105B share 12 comparable benchmark results. 0 of 8 categories are comparable. 46 results are unique to Qwen3.6-35B-A3B; 0 to Sarvam 105B.
Updated July 15, 2026- Shared results
- 12
- Qwen3.6-35B-A3B only
- 46
- Sarvam 105B only
- 0
- Comparable categories
- 0 / 8
Benchmark data for Qwen3.6-35B-A3B and Sarvam 105B is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 12 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.
Qwen3.6-35B-A3B has the larger context window at 262K, compared with 128K for Sarvam 105B.
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 | Qwen3.6-35B-A3B | Δ | Sarvam 105B |
|---|---|---|---|
| Agentic | Qwen3.6-35B-A3B51.5 | MarginNo overlap | Sarvam 105BNot measured |
| Coding | Qwen3.6-35B-A3B73.8 | MarginNo overlap | Sarvam 105BNot measured |
| Knowledge | Qwen3.6-35B-A3B51.8 | MarginNo overlap | Sarvam 105BNot measured |
| Math | Qwen3.6-35B-A3B88.2 | MarginNo overlap | Sarvam 105BNot measured |
| Multimodal | Qwen3.6-35B-A3B76.3 | MarginNo overlap | Sarvam 105BNot measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Qwen3.6-35B-A3B | Sarvam 105B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Qwen3.6-35B-A3BNot available | Sarvam 105B$0 input / $0 output | A complete price comparison is not available. |
| Generation speedtokens per second | Qwen3.6-35B-A3BNot available | Sarvam 105BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Qwen3.6-35B-A3BNot available | Sarvam 105BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Qwen3.6-35B-A3B262K | Sarvam 105B128K | Qwen3.6-35B-A3B lists the larger context window. |
Benchmark Deep Dive
Agentic15 benchmarks
| Benchmark | Qwen3.6-35B-A3B | Sarvam 105B | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 51.5% | — | Not comparable |
| Claw-EvalSource | 68.7% | — | Not comparable |
| QwenClawBenchSource | 52.6% | — | Not comparable |
| QwenWebBenchSource | 1397 | — | Not comparable |
| τ³-bench resultsSource | 67.2% | — | Not comparable |
| VITA-BenchSource | 35.6% | — | Not comparable |
| DeepPlanningSource | 25.9% | — | Not comparable |
| ToolathlonSource | 26.9% | — | Not comparable |
| MCP AtlasSource | 62.8% | — | Not comparable |
| WideResearchSource | 60.1% | — | Not comparable |
| AA Agentic IndexSource | 21.4% | — | Not comparable |
| τ²-bench resultsSource | 95.3% | 46.8% | Qwen3.6-35B-A3B leads |
| GDPval-AASource | 27.4% | — | Not comparable |
| GDPval-AASource | 1049 | — | Not comparable |
| Gert LabsSource | 42.65% | — | Not comparable |
Coding9 benchmarks
| Benchmark | Qwen3.6-35B-A3B | Sarvam 105B | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 73.4% | — | Not comparable |
| SWE MultilingualSource | 67.2% | — | Not comparable |
| SWE-bench ProSource | 49.5% | — | Not comparable |
| Terminal-Bench 2.0Source | 51.5% | — | Not comparable |
| LiveCodeBenchSource | 80.4% | — | Not comparable |
| NL2RepoSource | 29.4% | — | Not comparable |
| AA Coding IndexSource | 41.9% | — | Not comparable |
| Terminal-Bench HardSource | 34.8% | 1.5% | Qwen3.6-35B-A3B leads |
| AA-SciCodeSource | 35.8% | 26.4% | Qwen3.6-35B-A3B leads |
Reasoning2 benchmarks
Knowledge11 benchmarks
| Benchmark | Qwen3.6-35B-A3B | Sarvam 105B | Result |
|---|---|---|---|
| MMLU-ProSource | 85.2% | — | Not comparable |
| SuperGPQASource | 64.7% | — | Not comparable |
| C-EvalSource | 90% | — | Not comparable |
| GPQASource | 86% | — | Not comparable |
| HLESource | 21.4% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 31.6% | 11.9% | Qwen3.6-35B-A3B leads |
| AA-GPQA DiamondSource | 84.1% | 73.8% | Qwen3.6-35B-A3B leads |
| AA-HLESource | 20.2% | 10.1% | Qwen3.6-35B-A3B leads |
| AA-Omniscience IndexSource | -21.4% | -59.5% | Qwen3.6-35B-A3B leads |
| AA-Omniscience AccuracySource | 18.9% | 17.6% | Qwen3.6-35B-A3B leads |
| AA-Omniscience Hallucination RateSource | 49.7% | 93.5% | Qwen3.6-35B-A3B leads |
Math5 benchmarks
Multimodal15 benchmarks
| Benchmark | Qwen3.6-35B-A3B | Sarvam 105B | Result |
|---|---|---|---|
| MMMUSource | 81.7% | — | Not comparable |
| MMMU-ProSource | 75.3% | — | Not comparable |
| RealWorldQASource | 85.3% | — | Not comparable |
| OmniDocBench 1.5Source | 89.9% | — | Not comparable |
| CharXivSource | 78% | — | Not comparable |
| SimpleVQASource | 58.9% | — | Not comparable |
| CC-OCRSource | 81.9% | — | Not comparable |
| AI2D_TESTSource | 92.7% | — | Not comparable |
| RefCOCO (avg)Source | 92.0% | — | Not comparable |
| ODINW13Source | 50.8% | — | Not comparable |
| Video-MME (with subtitle)Source | 86.6% | — | Not comparable |
| Video-MME (w/o subtitle)Source | 82.5% | — | Not comparable |
| VideoMMMUSource | 83.7% | — | Not comparable |
| MLVU (M-Avg)Source | 86.2% | — | Not comparable |
| AA-MMMU-ProSource | 75.0% | — | Not comparable |
Inst. Following1 benchmarks
| Benchmark | Qwen3.6-35B-A3B | Sarvam 105B | Result |
|---|---|---|---|
| AA-IFBenchSource | 64.4% | 34.4% | Qwen3.6-35B-A3B leads |
Frequently Asked Questions (3)
Can I compare Qwen3.6-35B-A3B and Sarvam 105B 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 Qwen3.6-35B-A3B and Sarvam 105B today?
Sarvam 105B: $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.
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