Model comparison
GPT-5.2 vs Qwen3.6-27B
Head-to-head evidence from 18 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-5.2 #64 (Estimated); Qwen3.6-27B #93 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.2 and Qwen3.6-27B share 18 comparable benchmark results. 5 of 8 categories are comparable. 10 results are unique to GPT-5.2; 36 to Qwen3.6-27B.
Updated July 21, 2026- Shared results
- 18
- GPT-5.2 only
- 10
- Qwen3.6-27B only
- 36
- Comparable categories
- 5 / 8
Pick GPT-5.2 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 18 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-5.2 is clearly ahead on the BenchAlign aggregate, 58.43 to 53.82. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.2's sharpest advantage is in knowledge, where it averages 92.4 against 53.3. The single biggest benchmark swing on the page is GPQA, 92.4% to 87.8%. 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-5.2 is also the more expensive model on tokens at $1.75 input / $14.00 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-5.2 gives you the larger context window at 400K, 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-5.2 | Δ | Qwen3.6-27B |
|---|---|---|---|
| Math | GPT-5.235.2 | Margin→ 54.0 | Qwen3.6-27B89.2 |
| Knowledge | GPT-5.292.4 | Margin← 39.1 | Qwen3.6-27B53.3 |
| Coding | GPT-5.270.6 | Margin→ 6.9 | Qwen3.6-27B77.5 |
| Multimodal | GPT-5.280.4 | Margin← 3.7 | Qwen3.6-27B76.7 |
| Agentic | GPT-5.255.7 | Margin→ 3.6 | Qwen3.6-27B59.3 |
| Reasoning | GPT-5.252.9 | MarginNo overlap | Qwen3.6-27BNot measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
GPQA
KnowledgeA 92.4%B 87.8%Winner: GPT-5.2Δ 4.6GPQA: GPT-5.2 scored 92.4%; Qwen3.6-27B scored 87.8%. GPT-5.2 wins this benchmark. - Source ↗
MMMU-Pro
MultimodalA 79.5%B 75.8%Winner: GPT-5.2Δ 3.7MMMU-Pro: GPT-5.2 scored 79.5%; Qwen3.6-27B scored 75.8%. GPT-5.2 wins this benchmark. - Source ↗
CharXiv
MultimodalA 82.1%B 78.4%Winner: GPT-5.2Δ 3.7CharXiv: GPT-5.2 scored 82.1%; Qwen3.6-27B scored 78.4%. GPT-5.2 wins this benchmark. - Source ↗
SWE-bench Verified
CodingA 80%B 77.2%Winner: GPT-5.2Δ 2.8SWE-bench Verified: GPT-5.2 scored 80%; Qwen3.6-27B scored 77.2%. GPT-5.2 wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 55.6%B 53.5%Winner: GPT-5.2Δ 2.1SWE-bench Pro: GPT-5.2 scored 55.6%; Qwen3.6-27B scored 53.5%. GPT-5.2 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-5.2 | Qwen3.6-27B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.2$1.75 input / $14 output | Qwen3.6-27B$0 input / $0 output | Qwen3.6-27B has the lower combined listed price. |
| Generation speedtokens per second | GPT-5.273 tok/s | Qwen3.6-27BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.2130.34 s | Qwen3.6-27BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.2400K | Qwen3.6-27B262K | GPT-5.2 lists the larger context window. |
Benchmark Deep Dive
AgenticQwen3.6-27B wins13 benchmarks
| Benchmark | GPT-5.2 | Qwen3.6-27B | Result |
|---|---|---|---|
| BrowseCompSource | 65.8% | — | Not comparable |
| OSWorld-VerifiedSource | 47.3% | — | Not comparable |
| τ²-bench resultsSource | 84.8% | 94.2% | Qwen3.6-27B leads |
| Gert LabsSource | 46.54% | 54.84% | Qwen3.6-27B leads |
| JobBenchSource | 34.3% | — | 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 |
| GDPval-AASource | — | 32.0% | Not comparable |
| GDPval-AASource | — | 1140 | Not comparable |
CodingQwen3.6-27B wins9 benchmarks
| Benchmark | GPT-5.2 | Qwen3.6-27B | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 80% | 77.2% | GPT-5.2 leads |
| SWE-bench ProSource | 55.6% | 53.5% | GPT-5.2 leads |
| Vibe Code BenchSource | 53.50% | — | Not comparable |
| AA-SciCodeSource | 52.1% | 39.8% | GPT-5.2 leads |
| SWE MultilingualSource | — | 71.3% | 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 |
Reasoning3 benchmarks
KnowledgeGPT-5.2 wins12 benchmarks
| Benchmark | GPT-5.2 | Qwen3.6-27B | Result |
|---|---|---|---|
| GPQASource | 92.4% | 87.8% | GPT-5.2 leads |
| Artificial Analysis Intelligence IndexSource | 42.2% | 37.0% | GPT-5.2 leads |
| AA-GPQA DiamondSource | 90.3% | 84.2% | GPT-5.2 leads |
| AA-HLESource | 35.4% | 21.6% | GPT-5.2 leads |
| AA-Omniscience IndexSource | -1.0% | -19.8% | GPT-5.2 leads |
| AA-Omniscience AccuracySource | 43.8% | 19.2% | GPT-5.2 leads |
| AA-Omniscience Hallucination RateSource | 79.7% | 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 |
| HLESource | — | 24% | Not comparable |
MathQwen3.6-27B wins8 benchmarks
| Benchmark | GPT-5.2 | Qwen3.6-27B | Result |
|---|---|---|---|
| AA AIME 2025Source | 99.0% | — | Not comparable |
| FrontierMath v2 (Tiers 1-3)Source | 40.700% | — | Not comparable |
| FrontierMath v2 (Tier 4)Source | 18.800% | — | 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 |
MultimodalGPT-5.2 wins18 benchmarks
| Benchmark | GPT-5.2 | Qwen3.6-27B | Result |
|---|---|---|---|
| MMMU-ProSource | 79.5% | 75.8% | GPT-5.2 leads |
| MathVisionSource | 83.0% | — | Not comparable |
| CharXivSource | 82.1% | 78.4% | GPT-5.2 leads |
| V*Source | 75.9% | 94.7% | Qwen3.6-27B leads |
| Design Arena WebsiteSource | 1224 | — | 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 |
| 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 |
| AA-MMMU-ProSource | — | 74.6% | Not comparable |
Inst. Following1 benchmarks
| Benchmark | GPT-5.2 | Qwen3.6-27B | Result |
|---|---|---|---|
| AA-IFBenchSource | 75.4% | 67.6% | GPT-5.2 leads |
Frequently Asked Questions (6)
Which is better, GPT-5.2 or Qwen3.6-27B?
GPT-5.2 is ahead on BenchLM's BenchAlign leaderboard, 58.43 to 53.82. The biggest single separator in this matchup is GPQA, where the scores are 92.4% and 87.8%.
Which is better for knowledge tasks, GPT-5.2 or Qwen3.6-27B?
GPT-5.2 has the edge for knowledge tasks in this comparison, averaging 92.4 versus 53.3. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.
Which is better for coding, GPT-5.2 or Qwen3.6-27B?
Qwen3.6-27B has the edge for coding in this comparison, averaging 77.5 versus 70.6. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
Which is better for math, GPT-5.2 or Qwen3.6-27B?
Qwen3.6-27B has the edge for math in this comparison, averaging 89.2 versus 35.2. GPT-5.2 stays close enough that the answer can still flip depending on your workload.
Which is better for agentic tasks, GPT-5.2 or Qwen3.6-27B?
Qwen3.6-27B has the edge for agentic tasks in this comparison, averaging 59.3 versus 55.7. Inside this category, τ²-bench results is the benchmark that creates the most daylight between them.
Which is better for multimodal and grounded tasks, GPT-5.2 or Qwen3.6-27B?
GPT-5.2 has the edge for multimodal and grounded tasks in this comparison, averaging 80.4 versus 76.7. Inside this category, V* 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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