Model comparison
GPT-5.5 Pro vs Qwen3.6-27B
Head-to-head evidence from 2 shared benchmark results across 2 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-5.5 Pro #38 (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 Pro and Qwen3.6-27B share 2 comparable benchmark results. 3 of 8 categories are comparable. 5 results are unique to GPT-5.5 Pro; 52 to Qwen3.6-27B.
Updated July 22, 2026- Shared results
- 2
- GPT-5.5 Pro only
- 5
- Qwen3.6-27B only
- 52
- Comparable categories
- 3 / 8
Pick GPT-5.5 Pro 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 2 shared benchmark results across 2 evidence categories; 3 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 Pro is clearly ahead on the BenchAlign aggregate, 63.69 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 Pro's sharpest advantage is in agentic, where it averages 90.1 against 59.3. The single biggest benchmark swing on the page is HLE, 57.2% 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 Pro is also the more expensive model on tokens at $30.00 input / $180.00 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 Pro 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 Pro | Δ | Qwen3.6-27B |
|---|---|---|---|
| Math | GPT-5.5 Pro48.1 | Margin→ 41.1 | Qwen3.6-27B89.2 |
| Agentic | GPT-5.5 Pro90.1 | Margin← 30.8 | Qwen3.6-27B59.3 |
| Knowledge | GPT-5.5 Pro57.2 | Margin← 3.9 | Qwen3.6-27B53.3 |
| Coding | GPT-5.5 ProNot measured | MarginNo overlap | Qwen3.6-27B77.5 |
| Multimodal | GPT-5.5 ProNot measured | MarginNo overlap | Qwen3.6-27B76.7 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
HLE
KnowledgeA 57.2%B 24%Winner: GPT-5.5 ProΔ 33.2HLE: GPT-5.5 Pro scored 57.2%; Qwen3.6-27B scored 24%. GPT-5.5 Pro wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-5.5 Pro | Qwen3.6-27B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.5 Pro$30 input / $180 output | Qwen3.6-27B$0 input / $0 output | Qwen3.6-27B has the lower combined listed price. |
| Generation speedtokens per second | GPT-5.5 ProNot available | Qwen3.6-27BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.5 ProNot available | Qwen3.6-27BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.5 Pro1M | Qwen3.6-27B262K | GPT-5.5 Pro lists the larger context window. |
Benchmark Deep Dive
AgenticGPT-5.5 Pro wins11 benchmarks
| Benchmark | GPT-5.5 Pro | Qwen3.6-27B | Result |
|---|---|---|---|
| BrowseCompSource | 90.1% | — | Not comparable |
| Terminal-Bench 2.0Source | — | 59.3% | Not comparable |
| Claw-EvalSource | — | 72.4% | Not comparable |
| QwenClawBenchSource | — | 53.4% | Not comparable |
| QwenWebBenchSource | — | 1487 | Not comparable |
| AndroidWorldSource | — | 70.3% | Not comparable |
| AA Agentic IndexSource | — | 27.0% | Not comparable |
| τ²-bench resultsSource | — | 94.2% | Not comparable |
| GDPval-AASource | — | 32.0% | Not comparable |
| GDPval-AASource | — | 1140 | Not comparable |
| Gert LabsSource | — | 54.84% | Not comparable |
Coding8 benchmarks
| Benchmark | GPT-5.5 Pro | Qwen3.6-27B | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | — | 77.2% | Not comparable |
| SWE MultilingualSource | — | 71.3% | Not comparable |
| SWE-bench ProSource | — | 53.5% | Not comparable |
| Terminal-Bench 2.0Source | — | 59.3% | Not comparable |
| LiveCodeBenchSource | — | 83.9% | Not comparable |
| NL2RepoSource | — | 36.2% | Not comparable |
| AA Coding IndexSource | — | 53.7% | Not comparable |
| AA-SciCodeSource | — | 39.8% | Not comparable |
Reasoning2 benchmarks
KnowledgeGPT-5.5 Pro wins13 benchmarks
| Benchmark | GPT-5.5 Pro | Qwen3.6-27B | Result |
|---|---|---|---|
| HLESource | 57.2% | 24% | GPT-5.5 Pro leads |
| HLE w/o toolsSource | 43.1% | — | Not comparable |
| MMLU-ProSource | — | 86.2% | Not comparable |
| MMLU-ReduxSource | — | 93.5% | Not comparable |
| SuperGPQASource | — | 66% | Not comparable |
| C-EvalSource | — | 91.4% | Not comparable |
| GPQASource | — | 87.8% | Not comparable |
| Artificial Analysis Intelligence IndexSource | — | 37.0% | Not comparable |
| AA-GPQA DiamondSource | — | 84.2% | Not comparable |
| AA-HLESource | — | 21.6% | Not comparable |
| AA-Omniscience IndexSource | — | -19.8% | Not comparable |
| AA-Omniscience AccuracySource | — | 19.2% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 48.3% | Not comparable |
MathQwen3.6-27B wins8 benchmarks
| Benchmark | GPT-5.5 Pro | Qwen3.6-27B | Result |
|---|---|---|---|
| FrontierMath (legacy)Source | 52.4% | — | Not comparable |
| FrontierMath v2 (Tiers 1-3)Source | 51.000% | — | Not comparable |
| FrontierMath v2 (Tier 4)Source | 39.600% | — | 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 |
Multimodal16 benchmarks
| Benchmark | GPT-5.5 Pro | Qwen3.6-27B | Result |
|---|---|---|---|
| MMMUSource | — | 82.9% | Not comparable |
| MMMU-ProSource | — | 75.8% | 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 |
| AA-MMMU-ProSource | — | 74.6% | Not comparable |
Inst. Following1 benchmarks
| Benchmark | GPT-5.5 Pro | Qwen3.6-27B | Result |
|---|---|---|---|
| AA-IFBenchSource | — | 67.6% | Not comparable |
Frequently Asked Questions (4)
Which is better, GPT-5.5 Pro or Qwen3.6-27B?
GPT-5.5 Pro is ahead on BenchLM's BenchAlign leaderboard, 63.69 to 53.82. The biggest single separator in this matchup is HLE, where the scores are 57.2% and 24%.
Which is better for knowledge tasks, GPT-5.5 Pro or Qwen3.6-27B?
GPT-5.5 Pro has the edge for knowledge tasks in this comparison, averaging 57.2 versus 53.3. Inside this category, HLE is the benchmark that creates the most daylight between them.
Which is better for math, GPT-5.5 Pro or Qwen3.6-27B?
Qwen3.6-27B has the edge for math in this comparison, averaging 89.2 versus 48.1. GPT-5.5 Pro stays close enough that the answer can still flip depending on your workload.
Which is better for agentic tasks, GPT-5.5 Pro or Qwen3.6-27B?
GPT-5.5 Pro has the edge for agentic tasks in this comparison, averaging 90.1 versus 59.3. Qwen3.6-27B stays close enough that the answer can still flip depending on your workload.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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