Model comparison
MAI-Thinking-1 vs Qwen3.6-35B-A3B
Head-to-head evidence from 8 shared benchmark results across 4 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: MAI-Thinking-1 unranked (Not scored); Qwen3.6-35B-A3B #104 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. MAI-Thinking-1 and Qwen3.6-35B-A3B share 8 comparable benchmark results. 4 of 8 categories are comparable. 5 results are unique to MAI-Thinking-1; 49 to Qwen3.6-35B-A3B.
Updated July 23, 2026- Shared results
- 8
- MAI-Thinking-1 only
- 5
- Qwen3.6-35B-A3B only
- 49
- Comparable categories
- 4 / 8
Treat this as a split decision. MAI-Thinking-1 makes more sense if knowledge is the priority; Qwen3.6-35B-A3B is the better fit if coding is the priority or you need the larger 262K context window.
Confidence note. This is a partial-evidence comparison with 8 shared benchmark results across 4 evidence categories; 4 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
MAI-Thinking-1 and Qwen3.6-35B-A3B finish on the same BenchAlign overall score, so this is less about a single winner and more about where the edge shows up. The BenchAlign headline says tie; the benchmark table is where the real choice happens.
Qwen3.6-35B-A3B gives you the larger context window at 262K, compared with 256K for MAI-Thinking-1.
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 | MAI-Thinking-1 | Δ | Qwen3.6-35B-A3B |
|---|---|---|---|
| Knowledge | MAI-Thinking-172.5 | Margin← 21.1 | Qwen3.6-35B-A3B51.4 |
| Coding | MAI-Thinking-165.5 | Margin→ 8.3 | Qwen3.6-35B-A3B73.8 |
| Agentic | MAI-Thinking-146.0 | Margin→ 5.5 | Qwen3.6-35B-A3B51.5 |
| Math | MAI-Thinking-189.7 | Margin← 1.5 | Qwen3.6-35B-A3B88.2 |
| Multimodal | MAI-Thinking-1Not measured | MarginNo overlap | Qwen3.6-35B-A3B76.3 |
| Inst. Following | MAI-Thinking-185.0 | MarginNo overlap | Qwen3.6-35B-A3BNot measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 46%B 51.5%Winner: Qwen3.6-35B-A3BΔ 5.5Terminal-Bench 2.0: MAI-Thinking-1 scored 46%; Qwen3.6-35B-A3B scored 51.5%. Qwen3.6-35B-A3B wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 52.8%B 49.5%Winner: MAI-Thinking-1Δ 3.3SWE-bench Pro: MAI-Thinking-1 scored 52.8%; Qwen3.6-35B-A3B scored 49.5%. MAI-Thinking-1 wins this benchmark. - Source ↗
GPQA
KnowledgeA 84.2%B 86%Winner: Qwen3.6-35B-A3BΔ 1.8GPQA: MAI-Thinking-1 scored 84.2%; Qwen3.6-35B-A3B scored 86%. Qwen3.6-35B-A3B wins this benchmark. - Source ↗
AIME26
MathA 94.5%B 92.7%Winner: MAI-Thinking-1Δ 1.8AIME26: MAI-Thinking-1 scored 94.5%; Qwen3.6-35B-A3B scored 92.7%. MAI-Thinking-1 wins this benchmark. - Source ↗
HMMT Feb 2026
MathA 84.9%B 83.6%Winner: MAI-Thinking-1Δ 1.3HMMT Feb 2026: MAI-Thinking-1 scored 84.9%; Qwen3.6-35B-A3B scored 83.6%. MAI-Thinking-1 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | MAI-Thinking-1 | Qwen3.6-35B-A3B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | MAI-Thinking-1Not available | Qwen3.6-35B-A3BNot available | A complete price comparison is not available. |
| Generation speedtokens per second | MAI-Thinking-1Not available | Qwen3.6-35B-A3BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | MAI-Thinking-1Not available | Qwen3.6-35B-A3BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | MAI-Thinking-1256K | Qwen3.6-35B-A3B262K | Qwen3.6-35B-A3B lists the larger context window. |
Benchmark Deep Dive
AgenticQwen3.6-35B-A3B wins15 benchmarks
| Benchmark | MAI-Thinking-1 | Qwen3.6-35B-A3B | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 46% | 51.5% | Qwen3.6-35B-A3B leads |
| 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 |
CodingQwen3.6-35B-A3B wins8 benchmarks
| Benchmark | MAI-Thinking-1 | Qwen3.6-35B-A3B | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 73.5% | 73.4% | MAI-Thinking-1 leads |
| SWE-bench ProSource | 52.8% | 49.5% | MAI-Thinking-1 leads |
| Terminal-Bench 2.0Source | 46.0% | 51.5% | Qwen3.6-35B-A3B leads |
| SWE MultilingualSource | — | 67.2% | 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 |
Reasoning3 benchmarks
KnowledgeMAI-Thinking-1 wins13 benchmarks
| Benchmark | MAI-Thinking-1 | Qwen3.6-35B-A3B | Result |
|---|---|---|---|
| GPQASource | 84.2% | 86% | Qwen3.6-35B-A3B leads |
| GPQA-DSource | 84.2% | — | Not comparable |
| MMLU-ProSource | 85% | 85.2% | Qwen3.6-35B-A3B leads |
| SimpleQASource | 31% | — | Not comparable |
| SuperGPQASource | — | 64.7% | Not comparable |
| C-EvalSource | — | 90% | 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 |
MathMAI-Thinking-1 wins6 benchmarks
Multimodal15 benchmarks
| Benchmark | MAI-Thinking-1 | Qwen3.6-35B-A3B | 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 |
Frequently Asked Questions (5)
Which is better, MAI-Thinking-1 or Qwen3.6-35B-A3B?
MAI-Thinking-1 and Qwen3.6-35B-A3B are tied on the BenchAlign overall score, so the right pick depends on which category matters most for your use case.
Which is better for knowledge tasks, MAI-Thinking-1 or Qwen3.6-35B-A3B?
MAI-Thinking-1 has the edge for knowledge tasks in this comparison, averaging 72.5 versus 51.4. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Which is better for coding, MAI-Thinking-1 or Qwen3.6-35B-A3B?
Qwen3.6-35B-A3B has the edge for coding in this comparison, averaging 73.8 versus 65.5. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Which is better for math, MAI-Thinking-1 or Qwen3.6-35B-A3B?
MAI-Thinking-1 has the edge for math in this comparison, averaging 89.7 versus 88.2. Inside this category, AIME26 is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, MAI-Thinking-1 or Qwen3.6-35B-A3B?
Qwen3.6-35B-A3B has the edge for agentic tasks in this comparison, averaging 51.5 versus 46. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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