Model comparison
MAI-Thinking-1 vs Qwen3.5-35B-A3B
Head-to-head evidence from 4 shared benchmark results across 3 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: MAI-Thinking-1 unranked (Not scored); Qwen3.5-35B-A3B #72 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. MAI-Thinking-1 and Qwen3.5-35B-A3B share 4 comparable benchmark results. 4 of 8 categories are comparable. 9 results are unique to MAI-Thinking-1; 24 to Qwen3.5-35B-A3B.
Updated July 23, 2026- Shared results
- 4
- MAI-Thinking-1 only
- 9
- Qwen3.5-35B-A3B only
- 24
- Comparable categories
- 4 / 8
Treat this as a split decision. MAI-Thinking-1 makes more sense if coding is the priority; Qwen3.5-35B-A3B is the better fit if knowledge is the priority or you need the larger 262K context window.
Confidence note. This is a partial-evidence comparison with 4 shared benchmark results across 3 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 Qwen3.5-35B-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.
Qwen3.5-35B-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 | Qwen3.5-35B-A3B |
|---|---|---|---|
| Knowledge | MAI-Thinking-172.5 | Margin→ 9.1 | Qwen3.5-35B-A3B81.6 |
| Inst. Following | MAI-Thinking-185.0 | Margin→ 6.9 | Qwen3.5-35B-A3B91.9 |
| Agentic | MAI-Thinking-146.0 | Margin→ 5.0 | Qwen3.5-35B-A3B51.0 |
| Coding | MAI-Thinking-165.5 | Margin← 4.9 | Qwen3.5-35B-A3B60.6 |
| Reasoning | MAI-Thinking-1Not measured | MarginNo overlap | Qwen3.5-35B-A3B59.0 |
| Math | MAI-Thinking-189.7 | MarginNo overlap | Qwen3.5-35B-A3BNot measured |
| Multilingual | MAI-Thinking-1Not measured | MarginNo overlap | Qwen3.5-35B-A3B81.0 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 46%B 40.5%Winner: MAI-Thinking-1Δ 5.5Terminal-Bench 2.0: MAI-Thinking-1 scored 46%; Qwen3.5-35B-A3B scored 40.5%. MAI-Thinking-1 wins this benchmark. - Source ↗
SWE-bench Verified
CodingA 73.5%B 69.2%Winner: MAI-Thinking-1Δ 4.3SWE-bench Verified: MAI-Thinking-1 scored 73.5%; Qwen3.5-35B-A3B scored 69.2%. MAI-Thinking-1 wins this benchmark. - Source ↗
MMLU-Pro
KnowledgeA 85%B 85.3%Winner: Qwen3.5-35B-A3BΔ 0.3MMLU-Pro: MAI-Thinking-1 scored 85%; Qwen3.5-35B-A3B scored 85.3%. Qwen3.5-35B-A3B wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | MAI-Thinking-1 | Qwen3.5-35B-A3B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | MAI-Thinking-1Not available | Qwen3.5-35B-A3B$0 input / $0 output | A complete price comparison is not available. |
| Generation speedtokens per second | MAI-Thinking-1Not available | Qwen3.5-35B-A3BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | MAI-Thinking-1Not available | Qwen3.5-35B-A3BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | MAI-Thinking-1256K | Qwen3.5-35B-A3B262K | Qwen3.5-35B-A3B lists the larger context window. |
Benchmark Deep Dive
AgenticQwen3.5-35B-A3B wins5 benchmarks
CodingMAI-Thinking-1 wins5 benchmarks
Reasoning4 benchmarks
KnowledgeQwen3.5-35B-A3B wins11 benchmarks
| Benchmark | MAI-Thinking-1 | Qwen3.5-35B-A3B | Result |
|---|---|---|---|
| GPQASource | 84.2% | 84.2% | Tie |
| GPQA-DSource | 84.2% | — | Not comparable |
| MMLU-ProSource | 85% | 85.3% | Qwen3.5-35B-A3B leads |
| SimpleQASource | 31% | — | Not comparable |
| SuperGPQASource | — | 63.4% | Not comparable |
| Artificial Analysis Intelligence IndexSource | — | 29.3% | Not comparable |
| AA-GPQA DiamondSource | — | 84.5% | Not comparable |
| AA-HLESource | — | 19.7% | Not comparable |
| AA-Omniscience IndexSource | — | -46.4% | Not comparable |
| AA-Omniscience AccuracySource | — | 20.5% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 84.0% | Not comparable |
Math3 benchmarks
Multilingual1 benchmarks
| Benchmark | MAI-Thinking-1 | Qwen3.5-35B-A3B | Result |
|---|---|---|---|
| MMLU-ProXSource | — | 81% | Not comparable |
Multimodal5 benchmarks
Frequently Asked Questions (5)
Which is better, MAI-Thinking-1 or Qwen3.5-35B-A3B?
MAI-Thinking-1 and Qwen3.5-35B-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 Qwen3.5-35B-A3B?
Qwen3.5-35B-A3B has the edge for knowledge tasks in this comparison, averaging 81.6 versus 72.5. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Which is better for coding, MAI-Thinking-1 or Qwen3.5-35B-A3B?
MAI-Thinking-1 has the edge for coding in this comparison, averaging 65.5 versus 60.6. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, MAI-Thinking-1 or Qwen3.5-35B-A3B?
Qwen3.5-35B-A3B has the edge for agentic tasks in this comparison, averaging 51 versus 46. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Which is better for instruction following, MAI-Thinking-1 or Qwen3.5-35B-A3B?
Qwen3.5-35B-A3B has the edge for instruction following in this comparison, averaging 91.9 versus 85. MAI-Thinking-1 stays close enough that the answer can still flip depending on your workload.
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