Model comparison
GPT-5.4 vs Qwen3.6-27B
Head-to-head evidence from 26 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-5.4 #8 (Supported); Qwen3.6-27B #93 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.4 and Qwen3.6-27B share 26 comparable benchmark results. 5 of 8 categories are comparable. 26 results are unique to GPT-5.4; 28 to Qwen3.6-27B.
Updated July 21, 2026- Shared results
- 26
- GPT-5.4 only
- 26
- Qwen3.6-27B only
- 28
- Comparable categories
- 5 / 8
Pick GPT-5.4 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 26 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-5.4 is clearly ahead on the BenchAlign aggregate, 74.24 to 53.82. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.4's sharpest advantage is in agentic, where it averages 77.2 against 59.3. The single biggest benchmark swing on the page is HLE, 52.1% 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-5.4 is also the more expensive model on tokens at $2.50 input / $15.00 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-5.4 gives you the larger context window at 1.05M, 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-5.4 | Δ | Qwen3.6-27B |
|---|---|---|---|
| Math | GPT-5.442.5 | Margin→ 46.7 | Qwen3.6-27B89.2 |
| Coding | GPT-5.457.7 | Margin→ 19.8 | Qwen3.6-27B77.5 |
| Agentic | GPT-5.477.2 | Margin← 17.9 | Qwen3.6-27B59.3 |
| Knowledge | GPT-5.457.6 | Margin← 4.3 | Qwen3.6-27B53.3 |
| Multimodal | GPT-5.473.2 | Margin→ 3.5 | Qwen3.6-27B76.7 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
HLE
KnowledgeA 52.1%B 24%Winner: GPT-5.4Δ 28.1HLE: GPT-5.4 scored 52.1%; Qwen3.6-27B scored 24%. GPT-5.4 wins this benchmark. - Source ↗
Terminal-Bench 2.0
AgenticA 75.1%B 59.3%Winner: GPT-5.4Δ 15.8Terminal-Bench 2.0: GPT-5.4 scored 75.1%; Qwen3.6-27B scored 59.3%. GPT-5.4 wins this benchmark. - Source ↗
MMMU-Pro
MultimodalA 81.2%B 75.8%Winner: GPT-5.4Δ 5.4MMMU-Pro: GPT-5.4 scored 81.2%; Qwen3.6-27B scored 75.8%. GPT-5.4 wins this benchmark. - Source ↗
GPQA
KnowledgeA 92.8%B 87.8%Winner: GPT-5.4Δ 5GPQA: GPT-5.4 scored 92.8%; Qwen3.6-27B scored 87.8%. GPT-5.4 wins this benchmark. - Source ↗
CharXiv
MultimodalA 82.8%B 78.4%Winner: GPT-5.4Δ 4.4CharXiv: GPT-5.4 scored 82.8%; Qwen3.6-27B scored 78.4%. GPT-5.4 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-5.4 | Qwen3.6-27B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.4$2.5 input / $15 output | Qwen3.6-27B$0 input / $0 output | Qwen3.6-27B has the lower combined listed price. |
| Generation speedtokens per second | GPT-5.474 tok/s | Qwen3.6-27BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.4151.79 s | Qwen3.6-27BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.41.05M | Qwen3.6-27B262K | GPT-5.4 lists the larger context window. |
Benchmark Deep Dive
AgenticGPT-5.4 wins20 benchmarks
| Benchmark | GPT-5.4 | Qwen3.6-27B | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 75.1% | 59.3% | GPT-5.4 leads |
| CyberGymSource | 79.0% | — | Not comparable |
| BrowseCompSource | 82.7% | — | Not comparable |
| OSWorld-VerifiedSource | 75% | — | Not comparable |
| MCP AtlasSource | 70.6% | — | Not comparable |
| ToolathlonSource | 54.6% | — | Not comparable |
| τ²-bench resultsSource | 87.1% | 94.2% | Qwen3.6-27B leads |
| Claw-EvalSource | 60.3% | 72.4% | Qwen3.6-27B leads |
| DeepSearchQASource | 73.6% | — | Not comparable |
| AA Agentic IndexSource | 41.1% | 27.0% | GPT-5.4 leads |
| APEX-Agents-AASource | 33.3% | — | Not comparable |
| GDPval-AASource | 44.7% | 32.0% | GPT-5.4 leads |
| GDPval-AASource | 1395 | 1140 | GPT-5.4 leads |
| Gert LabsSource | 64.89% | 54.84% | GPT-5.4 leads |
| ResearchClawBenchSource | 15.3% | — | Not comparable |
| JobBenchSource | 38.9% | — | Not comparable |
| ExploitGymSource | 6.0% | — | Not comparable |
| QwenClawBenchSource | — | 53.4% | Not comparable |
| QwenWebBenchSource | — | 1487 | Not comparable |
| AndroidWorldSource | — | 70.3% | Not comparable |
CodingQwen3.6-27B wins11 benchmarks
| Benchmark | GPT-5.4 | Qwen3.6-27B | Result |
|---|---|---|---|
| LiveCodeBench ProSource | 87.5% | — | Not comparable |
| SWE-bench ProSource | 57.7% | 53.5% | GPT-5.4 leads |
| React Native EvalsSource | 85.3% | — | Not comparable |
| Vibe Code BenchSource | 67.42% | — | Not comparable |
| AA Coding IndexSource | 71.0% | 53.7% | GPT-5.4 leads |
| AA-SciCodeSource | 56.6% | 39.8% | GPT-5.4 leads |
| SWE-bench VerifiedSource | — | 77.2% | Not comparable |
| SWE MultilingualSource | — | 71.3% | Not comparable |
| Terminal-Bench 2.0Source | — | 59.3% | Not comparable |
| LiveCodeBenchSource | — | 83.9% | Not comparable |
| NL2RepoSource | — | 36.2% | Not comparable |
Reasoning2 benchmarks
KnowledgeGPT-5.4 wins17 benchmarks
| Benchmark | GPT-5.4 | Qwen3.6-27B | Result |
|---|---|---|---|
| GPQASource | 92.8% | 87.8% | GPT-5.4 leads |
| HLESource | 52.1% | 24% | GPT-5.4 leads |
| HLE w/o toolsSource | 39.8% | — | Not comparable |
| GPQA-DSource | 92.8% | — | Not comparable |
| HealthBench HardSource | 40.1% | — | Not comparable |
| MedXpertQA (Text)Source | 59.6% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 51.4% | 37.0% | GPT-5.4 leads |
| AA-GPQA DiamondSource | 92.0% | 84.2% | GPT-5.4 leads |
| AA-HLESource | 41.6% | 21.6% | GPT-5.4 leads |
| AA-Omniscience IndexSource | 5.7% | -19.8% | GPT-5.4 leads |
| AA-Omniscience AccuracySource | 50.0% | 19.2% | GPT-5.4 leads |
| AA-Omniscience Hallucination RateSource | 88.6% | 48.3% | Qwen3.6-27B leads |
| HealthBench ProfessionalSource | 48.1% | — | Not comparable |
| MMLU-ProSource | — | 86.2% | Not comparable |
| MMLU-ReduxSource | — | 93.5% | Not comparable |
| SuperGPQASource | — | 66% | Not comparable |
| C-EvalSource | — | 91.4% | Not comparable |
MathQwen3.6-27B wins7 benchmarks
| Benchmark | GPT-5.4 | Qwen3.6-27B | Result |
|---|---|---|---|
| FrontierMath v2 (Tiers 1-3)Source | 47.600% | — | Not comparable |
| FrontierMath v2 (Tier 4)Source | 27.100% | — | 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 wins22 benchmarks
| Benchmark | GPT-5.4 | Qwen3.6-27B | Result |
|---|---|---|---|
| MMMU-ProSource | 81.2% | 75.8% | GPT-5.4 leads |
| OfficeQA ProSource | 53.2% | — | Not comparable |
| MMMU-Pro w/ PythonSource | 82.1% | — | Not comparable |
| CharXivSource | 82.8% | 78.4% | GPT-5.4 leads |
| ERQASource | 65.4% | 62.5% | GPT-5.4 leads |
| SimpleVQASource | 61.1% | 56.1% | GPT-5.4 leads |
| ScreenSpot ProSource | 85.4% | — | Not comparable |
| ZeroBenchSource | 41.0% | — | Not comparable |
| MedXpertQA (MM)Source | 77.1% | — | Not comparable |
| AA-MMMU-ProSource | 78.4% | 74.6% | GPT-5.4 leads |
| Design Arena WebsiteSource | 1250 | — | Not comparable |
| MMMUSource | — | 82.9% | Not comparable |
| RealWorldQASource | — | 84.1% | Not comparable |
| DynaMathSource | — | 85.6% | Not comparable |
| MStarSource | — | 81.4% | Not comparable |
| CC-OCRSource | — | 81.2% | Not comparable |
| CountBenchSource | — | 97.8% | Not comparable |
| RefCOCO (avg)Source | — | 92.5% | 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-5.4 | Qwen3.6-27B | Result |
|---|---|---|---|
| AA-IFBenchSource | 73.9% | 67.6% | GPT-5.4 leads |
Frequently Asked Questions (6)
Which is better, GPT-5.4 or Qwen3.6-27B?
GPT-5.4 is ahead on BenchLM's BenchAlign leaderboard, 74.24 to 53.82. The biggest single separator in this matchup is HLE, where the scores are 52.1% and 24%.
Which is better for knowledge tasks, GPT-5.4 or Qwen3.6-27B?
GPT-5.4 has the edge for knowledge tasks in this comparison, averaging 57.6 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-5.4 or Qwen3.6-27B?
Qwen3.6-27B has the edge for coding in this comparison, averaging 77.5 versus 57.7. Inside this category, AA Coding Index is the benchmark that creates the most daylight between them.
Which is better for math, GPT-5.4 or Qwen3.6-27B?
Qwen3.6-27B has the edge for math in this comparison, averaging 89.2 versus 42.5. GPT-5.4 stays close enough that the answer can still flip depending on your workload.
Which is better for agentic tasks, GPT-5.4 or Qwen3.6-27B?
GPT-5.4 has the edge for agentic tasks in this comparison, averaging 77.2 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-5.4 or Qwen3.6-27B?
Qwen3.6-27B has the edge for multimodal and grounded tasks in this comparison, averaging 76.7 versus 73.2. 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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