Model comparison
Qwen3.6-35B-A3B vs Ternary Bonsai 1.7B
Head-to-head evidence from 0 shared benchmark results across 0 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Qwen3.6-35B-A3B #104 (Estimated); Ternary Bonsai 1.7B unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Qwen3.6-35B-A3B and Ternary Bonsai 1.7B share 0 comparable benchmark results. 0 of 8 categories are comparable. 57 results are unique to Qwen3.6-35B-A3B; 0 to Ternary Bonsai 1.7B.
Updated July 21, 2026- Shared results
- 0
- Qwen3.6-35B-A3B only
- 57
- Ternary Bonsai 1.7B only
- 0
- Comparable categories
- 0 / 8
Benchmark data for Qwen3.6-35B-A3B and Ternary Bonsai 1.7B is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 0 shared benchmark results across 0 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 does not have sourced benchmark coverage for Ternary Bonsai 1.7B yet. This comparison is currently limited to metadata such as context window, reasoning mode, and pricing where available.
Qwen3.6-35B-A3B has the larger context window at 262K, compared with 32K for Ternary Bonsai 1.7B.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Qwen3.6-35B-A3B | Ternary Bonsai 1.7B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Qwen3.6-35B-A3BNot available | Ternary Bonsai 1.7B$0 input / $0 output | A complete price comparison is not available. |
| Generation speedtokens per second | Qwen3.6-35B-A3BNot available | Ternary Bonsai 1.7BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Qwen3.6-35B-A3BNot available | Ternary Bonsai 1.7BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Qwen3.6-35B-A3B262K | Ternary Bonsai 1.7B32K | Qwen3.6-35B-A3B lists the larger context window. |
Benchmark Deep Dive
Agentic15 benchmarks
| Benchmark | Qwen3.6-35B-A3B | Ternary Bonsai 1.7B | 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% | — | Not comparable |
| GDPval-AASource | 27.4% | — | Not comparable |
| GDPval-AASource | 1049 | — | Not comparable |
| Gert LabsSource | 42.65% | — | Not comparable |
Coding8 benchmarks
| Benchmark | Qwen3.6-35B-A3B | Ternary Bonsai 1.7B | 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 |
| AA-SciCodeSource | 35.8% | — | Not comparable |
Reasoning2 benchmarks
Knowledge11 benchmarks
| Benchmark | Qwen3.6-35B-A3B | Ternary Bonsai 1.7B | 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% | — | Not comparable |
| AA-GPQA DiamondSource | 84.1% | — | Not comparable |
| AA-HLESource | 20.2% | — | Not comparable |
| AA-Omniscience IndexSource | -21.4% | — | Not comparable |
| AA-Omniscience AccuracySource | 18.9% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 49.7% | — | Not comparable |
Math5 benchmarks
Multimodal15 benchmarks
| Benchmark | Qwen3.6-35B-A3B | Ternary Bonsai 1.7B | 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 | Ternary Bonsai 1.7B | Result |
|---|---|---|---|
| AA-IFBenchSource | 64.4% | — | Not comparable |
Frequently Asked Questions (3)
Can I compare Qwen3.6-35B-A3B and Ternary Bonsai 1.7B 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 Ternary Bonsai 1.7B today?
Ternary Bonsai 1.7B: $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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