Model comparison
MAI-Thinking-1 vs Qwen3.5 397B
Head-to-head evidence from 7 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); Qwen3.5 397B #71 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. MAI-Thinking-1 and Qwen3.5 397B share 7 comparable benchmark results. 5 of 8 categories are comparable. 6 results are unique to MAI-Thinking-1; 48 to Qwen3.5 397B.
Updated July 23, 2026- Shared results
- 7
- MAI-Thinking-1 only
- 6
- Qwen3.5 397B only
- 48
- Comparable categories
- 5 / 8
Treat this as a split decision. MAI-Thinking-1 makes more sense if knowledge is the priority or you need the larger 256K context window; Qwen3.5 397B is the better fit if agentic is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Confidence note. This is a partial-evidence comparison with 7 shared benchmark results across 4 evidence categories; 5 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 397B 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.
MAI-Thinking-1 is the reasoning model in the pair, while Qwen3.5 397B is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. MAI-Thinking-1 gives you the larger context window at 256K, compared with 128K for Qwen3.5 397B.
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 397B |
|---|---|---|---|
| Knowledge | MAI-Thinking-172.5 | Margin← 15.9 | Qwen3.5 397B56.6 |
| Agentic | MAI-Thinking-146.0 | Margin→ 10.5 | Qwen3.5 397B56.5 |
| Inst. Following | MAI-Thinking-185.0 | Margin→ 7.6 | Qwen3.5 397B92.6 |
| Coding | MAI-Thinking-165.5 | Margin→ 1.0 | Qwen3.5 397B66.5 |
| Math | MAI-Thinking-189.7 | Margin→ 0.9 | Qwen3.5 397B90.6 |
| Reasoning | MAI-Thinking-1Not measured | MarginNo overlap | Qwen3.5 397B63.2 |
| Multilingual | MAI-Thinking-1Not measured | MarginNo overlap | Qwen3.5 397B84.7 |
| Multimodal | MAI-Thinking-1Not measured | MarginNo overlap | Qwen3.5 397B79.6 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 46%B 52.5%Winner: Qwen3.5 397BΔ 6.5Terminal-Bench 2.0: MAI-Thinking-1 scored 46%; Qwen3.5 397B scored 52.5%. Qwen3.5 397B wins this benchmark. - Source ↗
GPQA
KnowledgeA 84.2%B 88.4%Winner: Qwen3.5 397BΔ 4.2GPQA: MAI-Thinking-1 scored 84.2%; Qwen3.5 397B scored 88.4%. Qwen3.5 397B wins this benchmark. - Source ↗
HMMT Feb 2026
MathA 84.9%B 87.9%Winner: Qwen3.5 397BΔ 3HMMT Feb 2026: MAI-Thinking-1 scored 84.9%; Qwen3.5 397B scored 87.9%. Qwen3.5 397B wins this benchmark. - Source ↗
MMLU-Pro
KnowledgeA 85%B 87.8%Winner: Qwen3.5 397BΔ 2.8MMLU-Pro: MAI-Thinking-1 scored 85%; Qwen3.5 397B scored 87.8%. Qwen3.5 397B wins this benchmark. - Source ↗
SWE-bench Verified
CodingA 73.5%B 76.2%Winner: Qwen3.5 397BΔ 2.7SWE-bench Verified: MAI-Thinking-1 scored 73.5%; Qwen3.5 397B scored 76.2%. Qwen3.5 397B 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 397B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | MAI-Thinking-1Not available | Qwen3.5 397B$0.6 input / $3.6 output | A complete price comparison is not available. |
| Generation speedtokens per second | MAI-Thinking-1Not available | Qwen3.5 397B96 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | MAI-Thinking-1Not available | Qwen3.5 397B2.44 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | MAI-Thinking-1256K | Qwen3.5 397B128K | MAI-Thinking-1 lists the larger context window. |
Benchmark Deep Dive
AgenticQwen3.5 397B wins18 benchmarks
| Benchmark | MAI-Thinking-1 | Qwen3.5 397B | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 46% | 52.5% | Qwen3.5 397B leads |
| BrowseCompSource | — | 62% | Not comparable |
| Claw-EvalSource | — | 56.8% | Not comparable |
| QwenClawBenchSource | — | 51.8% | Not comparable |
| τ³-bench resultsSource | — | 68.4% | Not comparable |
| VITA-BenchSource | — | 43.7% | Not comparable |
| DeepPlanningSource | — | 37.6% | Not comparable |
| ToolathlonSource | — | 36.3% | Not comparable |
| MCP AtlasSource | — | 46.1% | Not comparable |
| MCP-TasksSource | — | 74.2% | Not comparable |
| WideResearchSource | — | 74.0% | Not comparable |
| τ²-bench resultsSource | — | 95.6% | Not comparable |
| Gert LabsSource | — | 46.76% | Not comparable |
| ResearchClawBenchSource | — | 14.2% | Not comparable |
| AA Agentic IndexSource | — | 19.9% | Not comparable |
| APEX-Agents-AASource | — | 15.3% | Not comparable |
| GDPval-AASource | — | 23.1% | Not comparable |
| GDPval-AASource | — | 962 | Not comparable |
CodingQwen3.5 397B wins6 benchmarks
| Benchmark | MAI-Thinking-1 | Qwen3.5 397B | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 73.5% | 76.2% | Qwen3.5 397B leads |
| SWE-bench ProSource | 52.8% | 50.9% | MAI-Thinking-1 leads |
| Terminal-Bench 2.0Source | 46.0% | — | Not comparable |
| LiveCodeBench v6Source | — | 83.6% | Not comparable |
| AA-SciCodeSource | — | 42.0% | Not comparable |
| AA Coding IndexSource | — | 48.2% | Not comparable |
Reasoning5 benchmarks
KnowledgeMAI-Thinking-1 wins14 benchmarks
| Benchmark | MAI-Thinking-1 | Qwen3.5 397B | Result |
|---|---|---|---|
| GPQASource | 84.2% | 88.4% | Qwen3.5 397B leads |
| GPQA-DSource | 84.2% | — | Not comparable |
| MMLU-ProSource | 85% | 87.8% | Qwen3.5 397B leads |
| SimpleQASource | 31% | — | Not comparable |
| SuperGPQASource | — | 70.4% | Not comparable |
| MMLU-ReduxSource | — | 94.9% | Not comparable |
| C-EvalSource | — | 93% | Not comparable |
| HLESource | — | 28.7% | Not comparable |
| Artificial Analysis Intelligence IndexSource | — | 33.7% | Not comparable |
| AA-GPQA DiamondSource | — | 89.3% | Not comparable |
| AA-HLESource | — | 27.3% | Not comparable |
| AA-Omniscience IndexSource | — | -29.8% | Not comparable |
| AA-Omniscience AccuracySource | — | 31.4% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 89.1% | Not comparable |
MathQwen3.5 397B wins6 benchmarks
Multilingual2 benchmarks
Multimodal7 benchmarks
Frequently Asked Questions (6)
Which is better, MAI-Thinking-1 or Qwen3.5 397B?
MAI-Thinking-1 and Qwen3.5 397B 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 397B?
MAI-Thinking-1 has the edge for knowledge tasks in this comparison, averaging 72.5 versus 56.6. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Which is better for coding, MAI-Thinking-1 or Qwen3.5 397B?
Qwen3.5 397B has the edge for coding in this comparison, averaging 66.5 versus 65.5. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Which is better for math, MAI-Thinking-1 or Qwen3.5 397B?
Qwen3.5 397B has the edge for math in this comparison, averaging 90.6 versus 89.7. Inside this category, HMMT Feb 2026 is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, MAI-Thinking-1 or Qwen3.5 397B?
Qwen3.5 397B has the edge for agentic tasks in this comparison, averaging 56.5 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 397B?
Qwen3.5 397B has the edge for instruction following in this comparison, averaging 92.6 versus 85. MAI-Thinking-1 stays close enough that the answer can still flip depending on your workload.
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