Model comparison
MAI-Thinking-1 vs Qwen3.5-27B
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); Qwen3.5-27B #45 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. MAI-Thinking-1 and Qwen3.5-27B share 4 comparable benchmark results. 4 of 8 categories are comparable. 9 results are unique to MAI-Thinking-1; 24 to Qwen3.5-27B.
Updated July 23, 2026- Shared results
- 4
- MAI-Thinking-1 only
- 9
- Qwen3.5-27B only
- 24
- Comparable categories
- 4 / 8
Treat this as a split decision. MAI-Thinking-1 makes more sense if coding is the priority; Qwen3.5-27B 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 Qwen3.5-27B 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.
Qwen3.5-27B 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 | Qwen3.5-27B |
|---|---|---|---|
| Knowledge | MAI-Thinking-172.5 | Margin→ 10.2 | Qwen3.5-27B82.7 |
| Inst. Following | MAI-Thinking-185.0 | Margin→ 10.0 | Qwen3.5-27B95.0 |
| Agentic | MAI-Thinking-146.0 | Margin→ 6.0 | Qwen3.5-27B52.0 |
| Coding | MAI-Thinking-165.5 | Margin← 0.6 | Qwen3.5-27B64.9 |
| Reasoning | MAI-Thinking-1Not measured | MarginNo overlap | Qwen3.5-27B60.6 |
| Math | MAI-Thinking-189.7 | MarginNo overlap | Qwen3.5-27BNot measured |
| Multilingual | MAI-Thinking-1Not measured | MarginNo overlap | Qwen3.5-27B82.2 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 46%B 41.6%Winner: MAI-Thinking-1Δ 4.4Terminal-Bench 2.0: MAI-Thinking-1 scored 46%; Qwen3.5-27B scored 41.6%. MAI-Thinking-1 wins this benchmark. - Source ↗
GPQA
KnowledgeA 84.2%B 85.5%Winner: Qwen3.5-27BΔ 1.3GPQA: MAI-Thinking-1 scored 84.2%; Qwen3.5-27B scored 85.5%. Qwen3.5-27B wins this benchmark. - Source ↗
SWE-bench Verified
CodingA 73.5%B 72.4%Winner: MAI-Thinking-1Δ 1.1SWE-bench Verified: MAI-Thinking-1 scored 73.5%; Qwen3.5-27B scored 72.4%. MAI-Thinking-1 wins this benchmark. - Source ↗
MMLU-Pro
KnowledgeA 85%B 86.1%Winner: Qwen3.5-27BΔ 1.1MMLU-Pro: MAI-Thinking-1 scored 85%; Qwen3.5-27B scored 86.1%. Qwen3.5-27B wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | MAI-Thinking-1 | Qwen3.5-27B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | MAI-Thinking-1Not available | Qwen3.5-27B$0 input / $0 output | A complete price comparison is not available. |
| Generation speedtokens per second | MAI-Thinking-1Not available | Qwen3.5-27BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | MAI-Thinking-1Not available | Qwen3.5-27BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | MAI-Thinking-1256K | Qwen3.5-27B262K | Qwen3.5-27B lists the larger context window. |
Benchmark Deep Dive
AgenticQwen3.5-27B wins5 benchmarks
CodingMAI-Thinking-1 wins5 benchmarks
Reasoning4 benchmarks
KnowledgeQwen3.5-27B wins11 benchmarks
| Benchmark | MAI-Thinking-1 | Qwen3.5-27B | Result |
|---|---|---|---|
| GPQASource | 84.2% | 85.5% | Qwen3.5-27B leads |
| GPQA-DSource | 84.2% | — | Not comparable |
| MMLU-ProSource | 85% | 86.1% | Qwen3.5-27B leads |
| SimpleQASource | 31% | — | Not comparable |
| SuperGPQASource | — | 65.6% | Not comparable |
| Artificial Analysis Intelligence IndexSource | — | 33.8% | Not comparable |
| AA-GPQA DiamondSource | — | 85.8% | Not comparable |
| AA-HLESource | — | 22.2% | Not comparable |
| AA-Omniscience IndexSource | — | -42.0% | Not comparable |
| AA-Omniscience AccuracySource | — | 21.0% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 79.7% | Not comparable |
Math3 benchmarks
Multilingual1 benchmarks
| Benchmark | MAI-Thinking-1 | Qwen3.5-27B | Result |
|---|---|---|---|
| MMLU-ProXSource | — | 82.2% | Not comparable |
Multimodal5 benchmarks
Frequently Asked Questions (5)
Which is better, MAI-Thinking-1 or Qwen3.5-27B?
MAI-Thinking-1 and Qwen3.5-27B 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 Qwen3.5-27B?
Qwen3.5-27B has the edge for knowledge tasks in this comparison, averaging 82.7 versus 72.5. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Which is better for coding, MAI-Thinking-1 or Qwen3.5-27B?
MAI-Thinking-1 has the edge for coding in this comparison, averaging 65.5 versus 64.9. 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 Qwen3.5-27B?
Qwen3.5-27B has the edge for agentic tasks in this comparison, averaging 52 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 Qwen3.5-27B?
Qwen3.5-27B has the edge for instruction following in this comparison, averaging 95 versus 85. MAI-Thinking-1 stays close enough that the answer can still flip depending on your workload.
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