Model comparison
GPT-5.5 vs Qwen3.6-27B
Head-to-head evidence from 23 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-5.5 #9 (Estimated); Qwen3.6-27B #93 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.5 and Qwen3.6-27B share 23 comparable benchmark results. 5 of 8 categories are comparable. 34 results are unique to GPT-5.5; 31 to Qwen3.6-27B.
Updated July 22, 2026- Shared results
- 23
- GPT-5.5 only
- 34
- Qwen3.6-27B only
- 31
- Comparable categories
- 5 / 8
Pick GPT-5.5 if you want the stronger benchmark profile. Qwen3.6-27B only becomes the better choice if mathematics is the priority or you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 23 shared benchmark results across 6 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
GPT-5.5 is clearly ahead on the BenchAlign aggregate, 73.51 to 53.82. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.5's sharpest advantage is in agentic, where it averages 81.6 against 59.3. The single biggest benchmark swing on the page is HLE, 52.2% to 24%. Qwen3.6-27B does hit back in mathematics, so the answer changes if that is the part of the workload you care about most.
GPT-5.5 is also the more expensive model on tokens at $5.00 input / $30.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen3.6-27B. That is roughly Infinityx on output cost alone. GPT-5.5 gives you the larger context window at 1M, compared with 262K for Qwen3.6-27B.
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 | GPT-5.5 | Δ | Qwen3.6-27B |
|---|---|---|---|
| Math | GPT-5.547.6 | Margin→ 41.6 | Qwen3.6-27B89.2 |
| Agentic | GPT-5.581.6 | Margin← 22.3 | Qwen3.6-27B59.3 |
| Coding | GPT-5.558.6 | Margin→ 18.9 | Qwen3.6-27B77.5 |
| Multimodal | GPT-5.570.4 | Margin→ 6.3 | Qwen3.6-27B76.7 |
| Knowledge | GPT-5.557.8 | Margin← 4.5 | Qwen3.6-27B53.3 |
| Reasoning | GPT-5.585.0 | MarginNo overlap | Qwen3.6-27BNot measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
HLE
KnowledgeA 52.2%B 24%Winner: GPT-5.5Δ 28.2HLE: GPT-5.5 scored 52.2%; Qwen3.6-27B scored 24%. GPT-5.5 wins this benchmark. - Source ↗
Terminal-Bench 2.0
AgenticA 82%B 59.3%Winner: GPT-5.5Δ 22.7Terminal-Bench 2.0: GPT-5.5 scored 82%; Qwen3.6-27B scored 59.3%. GPT-5.5 wins this benchmark. - Source ↗
GPQA
KnowledgeA 93.6%B 87.8%Winner: GPT-5.5Δ 5.8GPQA: GPT-5.5 scored 93.6%; Qwen3.6-27B scored 87.8%. GPT-5.5 wins this benchmark. - Source ↗
MMMU-Pro
MultimodalA 81.2%B 75.8%Winner: GPT-5.5Δ 5.4MMMU-Pro: GPT-5.5 scored 81.2%; Qwen3.6-27B scored 75.8%. GPT-5.5 wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 58.6%B 53.5%Winner: GPT-5.5Δ 5.1SWE-bench Pro: GPT-5.5 scored 58.6%; Qwen3.6-27B scored 53.5%. GPT-5.5 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-5.5 | Qwen3.6-27B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.5$5 input / $30 output | Qwen3.6-27B$0 input / $0 output | Qwen3.6-27B has the lower combined listed price. |
| Generation speedtokens per second | GPT-5.5Not available | Qwen3.6-27BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.5Not available | Qwen3.6-27BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.51M | Qwen3.6-27B262K | GPT-5.5 lists the larger context window. |
Benchmark Deep Dive
AgenticGPT-5.5 wins28 benchmarks
| Benchmark | GPT-5.5 | Qwen3.6-27B | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 82% | 59.3% | GPT-5.5 leads |
| CyberGymSource | 81.8% | — | Not comparable |
| BrowseCompSource | 84.4% | — | Not comparable |
| OSWorld-VerifiedSource | 78.7% | — | Not comparable |
| MCP AtlasSource | 75.3% | — | Not comparable |
| ToolathlonSource | 55.6% | — | Not comparable |
| τ²-bench resultsSource | 93.9% | 94.2% | Qwen3.6-27B leads |
| AA Agentic IndexSource | 44.9% | 27.0% | GPT-5.5 leads |
| APEX-Agents-AASource | 37.7% | — | Not comparable |
| GDPval-AASource | 49.5% | 32.0% | GPT-5.5 leads |
| GDPval-AASource | 1490 | 1140 | GPT-5.5 leads |
| Gert LabsSource | 72.93% | 54.84% | GPT-5.5 leads |
| ResearchClawBenchSource | 17.0% | — | Not comparable |
| OSWorld 2.0Source | 13.0% | — | Not comparable |
| JobBenchSource | 42.7% | — | Not comparable |
| ExploitGymSource | 13.4% | — | Not comparable |
| AA BriefcaseSource | 1154 | — | Not comparable |
| AA AutomationBenchSource | 42.1% | — | Not comparable |
| AA EnterpriseOps-GymSource | 46.6% | — | Not comparable |
| AA Harvey LABSource | 86.3% | — | Not comparable |
| AA ITBenchSource | 45.8% | — | Not comparable |
| AA Tau3 BankingSource | 31.3% | — | Not comparable |
| terminalBenchHardSource | 60.6% | — | Not comparable |
| aaTerminalBench21Source | 84.3% | — | Not comparable |
| Claw-EvalSource | — | 72.4% | Not comparable |
| QwenClawBenchSource | — | 53.4% | Not comparable |
| QwenWebBenchSource | — | 1487 | Not comparable |
| AndroidWorldSource | — | 70.3% | Not comparable |
CodingQwen3.6-27B wins13 benchmarks
| Benchmark | GPT-5.5 | Qwen3.6-27B | Result |
|---|---|---|---|
| SWE-bench ProSource | 58.6% | 53.5% | GPT-5.5 leads |
| Terminal-Bench 2.0Source | 82.0% | 59.3% | GPT-5.5 leads |
| Vibe Code BenchSource | 69.85% | — | Not comparable |
| React Native EvalsSource | 84.7% | — | Not comparable |
| cursorBench31Source | 59.2% | — | Not comparable |
| cursorBench32Source | 58.4% | — | Not comparable |
| AA Coding IndexSource | 74.9% | 53.7% | GPT-5.5 leads |
| AA-SciCodeSource | 56.1% | 39.8% | GPT-5.5 leads |
| FrontierCode 1.1 MainSource | 43.0% | — | Not comparable |
| SWE-bench VerifiedSource | — | 77.2% | Not comparable |
| SWE MultilingualSource | — | 71.3% | Not comparable |
| LiveCodeBenchSource | — | 83.9% | Not comparable |
| NL2RepoSource | — | 36.2% | Not comparable |
Reasoning5 benchmarks
KnowledgeGPT-5.5 wins14 benchmarks
| Benchmark | GPT-5.5 | Qwen3.6-27B | Result |
|---|---|---|---|
| GPQASource | 93.6% | 87.8% | GPT-5.5 leads |
| GPQA-DSource | 93.6% | — | Not comparable |
| HLESource | 52.2% | 24% | GPT-5.5 leads |
| HLE w/o toolsSource | 41.4% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 54.8% | 37.0% | GPT-5.5 leads |
| AA-GPQA DiamondSource | 93.5% | 84.2% | GPT-5.5 leads |
| AA-HLESource | 44.3% | 21.6% | GPT-5.5 leads |
| AA-Omniscience IndexSource | 20.1% | -19.8% | GPT-5.5 leads |
| AA-Omniscience AccuracySource | 56.9% | 19.2% | GPT-5.5 leads |
| AA-Omniscience Hallucination RateSource | 85.5% | 48.3% | Qwen3.6-27B leads |
| MMLU-ProSource | — | 86.2% | Not comparable |
| MMLU-ReduxSource | — | 93.5% | Not comparable |
| SuperGPQASource | — | 66% | Not comparable |
| C-EvalSource | — | 91.4% | Not comparable |
MathQwen3.6-27B wins8 benchmarks
| Benchmark | GPT-5.5 | Qwen3.6-27B | Result |
|---|---|---|---|
| FrontierMath (legacy)Source | 51.7% | — | Not comparable |
| FrontierMath v2 (Tiers 1-3)Source | 51.700% | — | Not comparable |
| FrontierMath v2 (Tier 4)Source | 35.400% | — | Not comparable |
| HMMT Feb 2025Source | — | 93.8% | Not comparable |
| HMMT Nov 2025Source | — | 90.7% | Not comparable |
| HMMT Feb 2026Source | — | 84.3% | Not comparable |
| MMAnswerBenchSource | — | 80.8% | Not comparable |
| AIME26Source | — | 94.1% | Not comparable |
MultimodalQwen3.6-27B wins19 benchmarks
| Benchmark | GPT-5.5 | Qwen3.6-27B | Result |
|---|---|---|---|
| MMMU-ProSource | 81.2% | 75.8% | GPT-5.5 leads |
| MMMU-Pro w/ PythonSource | 83.2% | — | Not comparable |
| OfficeQA ProSource | 54.1% | — | Not comparable |
| AA-MMMU-ProSource | 79.9% | 74.6% | GPT-5.5 leads |
| Design Arena WebsiteSource | 1282 | — | Not comparable |
| MMMUSource | — | 82.9% | Not comparable |
| RealWorldQASource | — | 84.1% | Not comparable |
| DynaMathSource | — | 85.6% | Not comparable |
| MStarSource | — | 81.4% | Not comparable |
| SimpleVQASource | — | 56.1% | Not comparable |
| CharXivSource | — | 78.4% | Not comparable |
| CC-OCRSource | — | 81.2% | Not comparable |
| CountBenchSource | — | 97.8% | Not comparable |
| RefCOCO (avg)Source | — | 92.5% | Not comparable |
| ERQASource | — | 62.5% | Not comparable |
| Video-MME (with subtitle)Source | — | 87.7% | Not comparable |
| VideoMMMUSource | — | 84.4% | Not comparable |
| MLVU (M-Avg)Source | — | 86.6% | Not comparable |
| V*Source | — | 94.7% | Not comparable |
Inst. Following1 benchmarks
| Benchmark | GPT-5.5 | Qwen3.6-27B | Result |
|---|---|---|---|
| AA-IFBenchSource | 75.9% | 67.6% | GPT-5.5 leads |
Frequently Asked Questions (6)
Which is better, GPT-5.5 or Qwen3.6-27B?
GPT-5.5 is ahead on BenchLM's BenchAlign leaderboard, 73.51 to 53.82. The biggest single separator in this matchup is HLE, where the scores are 52.2% and 24%.
Which is better for knowledge tasks, GPT-5.5 or Qwen3.6-27B?
GPT-5.5 has the edge for knowledge tasks in this comparison, averaging 57.8 versus 53.3. Inside this category, AA-Omniscience Index is the benchmark that creates the most daylight between them.
Which is better for coding, GPT-5.5 or Qwen3.6-27B?
Qwen3.6-27B has the edge for coding in this comparison, averaging 77.5 versus 58.6. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Which is better for math, GPT-5.5 or Qwen3.6-27B?
Qwen3.6-27B has the edge for math in this comparison, averaging 89.2 versus 47.6. GPT-5.5 stays close enough that the answer can still flip depending on your workload.
Which is better for agentic tasks, GPT-5.5 or Qwen3.6-27B?
GPT-5.5 has the edge for agentic tasks in this comparison, averaging 81.6 versus 59.3. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.
Which is better for multimodal and grounded tasks, GPT-5.5 or Qwen3.6-27B?
Qwen3.6-27B has the edge for multimodal and grounded tasks in this comparison, averaging 76.7 versus 70.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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