Model comparison
GPT-5.4 nano vs Qwen3.6-27B
Head-to-head evidence from 20 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-5.4 nano #25 (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 nano and Qwen3.6-27B share 20 comparable benchmark results. 4 of 8 categories are comparable. 9 results are unique to GPT-5.4 nano; 34 to Qwen3.6-27B.
Updated July 21, 2026- Shared results
- 20
- GPT-5.4 nano only
- 9
- Qwen3.6-27B only
- 34
- Comparable categories
- 4 / 8
Pick GPT-5.4 nano 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 20 shared benchmark results across 6 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
GPT-5.4 nano is clearly ahead on the BenchAlign aggregate, 66.79 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 nano is also the more expensive model on tokens at $0.20 input / $1.25 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 nano 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-5.4 nano | Δ | Qwen3.6-27B |
|---|---|---|---|
| Math | GPT-5.4 nano21.0 | Margin→ 68.2 | Qwen3.6-27B89.2 |
| Agentic | GPT-5.4 nano42.9 | Margin→ 16.4 | Qwen3.6-27B59.3 |
| Multimodal | GPT-5.4 nano66.1 | Margin→ 10.6 | Qwen3.6-27B76.7 |
| Knowledge | GPT-5.4 nano43.8 | Margin→ 9.5 | Qwen3.6-27B53.3 |
| Coding | GPT-5.4 nanoNot measured | MarginNo overlap | Qwen3.6-27B77.5 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
HLE
KnowledgeA 37.7%B 24%Winner: GPT-5.4 nanoΔ 13.7HLE: GPT-5.4 nano scored 37.7%; Qwen3.6-27B scored 24%. GPT-5.4 nano wins this benchmark. - Source ↗
Terminal-Bench 2.0
AgenticA 46.3%B 59.3%Winner: Qwen3.6-27BΔ 13Terminal-Bench 2.0: GPT-5.4 nano scored 46.3%; Qwen3.6-27B scored 59.3%. Qwen3.6-27B wins this benchmark. - Source ↗
MMMU-Pro
MultimodalA 66.1%B 75.8%Winner: Qwen3.6-27BΔ 9.7MMMU-Pro: GPT-5.4 nano scored 66.1%; Qwen3.6-27B scored 75.8%. Qwen3.6-27B wins this benchmark. - Source ↗
GPQA
KnowledgeA 82.8%B 87.8%Winner: Qwen3.6-27BΔ 5GPQA: GPT-5.4 nano scored 82.8%; Qwen3.6-27B scored 87.8%. Qwen3.6-27B wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-5.4 nano | Qwen3.6-27B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.4 nano$0.2 input / $1.25 output | Qwen3.6-27B$0 input / $0 output | Qwen3.6-27B has the lower combined listed price. |
| Generation speedtokens per second | GPT-5.4 nano191 tok/s | Qwen3.6-27BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.4 nano3.64 s | Qwen3.6-27BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.4 nano400K | Qwen3.6-27B262K | GPT-5.4 nano lists the larger context window. |
Benchmark Deep Dive
AgenticQwen3.6-27B wins14 benchmarks
| Benchmark | GPT-5.4 nano | Qwen3.6-27B | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 46.3% | 59.3% | Qwen3.6-27B leads |
| OSWorld-VerifiedSource | 39% | — | Not comparable |
| MCP AtlasSource | 56.1% | — | Not comparable |
| ToolathlonSource | 35.5% | — | Not comparable |
| τ²-bench resultsSource | 76% | 94.2% | Qwen3.6-27B leads |
| AA Agentic IndexSource | 27.5% | 27.0% | GPT-5.4 nano leads |
| APEX-Agents-AASource | 24.9% | — | Not comparable |
| GDPval-AASource | 30.0% | 32.0% | Qwen3.6-27B leads |
| GDPval-AASource | 1100 | 1140 | Qwen3.6-27B leads |
| Claw-EvalSource | — | 72.4% | Not comparable |
| QwenClawBenchSource | — | 53.4% | Not comparable |
| QwenWebBenchSource | — | 1487 | Not comparable |
| AndroidWorldSource | — | 70.3% | Not comparable |
| Gert LabsSource | — | 54.84% | Not comparable |
Coding9 benchmarks
| Benchmark | GPT-5.4 nano | Qwen3.6-27B | Result |
|---|---|---|---|
| Vibe Code BenchSource | 26.10% | — | Not comparable |
| AA Coding IndexSource | 56.1% | 53.7% | GPT-5.4 nano leads |
| AA-SciCodeSource | 46.9% | 39.8% | GPT-5.4 nano leads |
| 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 |
Reasoning2 benchmarks
KnowledgeQwen3.6-27B wins13 benchmarks
| Benchmark | GPT-5.4 nano | Qwen3.6-27B | Result |
|---|---|---|---|
| GPQASource | 82.8% | 87.8% | Qwen3.6-27B leads |
| HLESource | 37.7% | 24% | GPT-5.4 nano leads |
| HLE w/o toolsSource | 24.3% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 38.2% | 37.0% | GPT-5.4 nano leads |
| AA-GPQA DiamondSource | 81.7% | 84.2% | Qwen3.6-27B leads |
| AA-HLESource | 26.5% | 21.6% | GPT-5.4 nano leads |
| AA-Omniscience IndexSource | -29.5% | -19.8% | Qwen3.6-27B leads |
| AA-Omniscience AccuracySource | 25.4% | 19.2% | GPT-5.4 nano leads |
| AA-Omniscience Hallucination RateSource | 73.6% | 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 wins7 benchmarks
| Benchmark | GPT-5.4 nano | Qwen3.6-27B | Result |
|---|---|---|---|
| FrontierMath v2 (Tiers 1-3)Source | 25.860% | — | Not comparable |
| FrontierMath v2 (Tier 4)Source | 6.250% | — | 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 wins17 benchmarks
| Benchmark | GPT-5.4 nano | Qwen3.6-27B | Result |
|---|---|---|---|
| MMMU-ProSource | 66.1% | 75.8% | Qwen3.6-27B leads |
| MMMU-Pro w/ PythonSource | 69.5% | — | Not comparable |
| AA-MMMU-ProSource | 65.4% | 74.6% | Qwen3.6-27B leads |
| 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-5.4 nano | Qwen3.6-27B | Result |
|---|---|---|---|
| AA-IFBenchSource | 75.9% | 67.6% | GPT-5.4 nano leads |
Frequently Asked Questions (5)
Which is better, GPT-5.4 nano or Qwen3.6-27B?
GPT-5.4 nano is ahead on BenchLM's BenchAlign leaderboard, 66.79 to 53.82. The biggest single separator in this matchup is HLE, where the scores are 37.7% and 24%.
Which is better for knowledge tasks, GPT-5.4 nano or Qwen3.6-27B?
Qwen3.6-27B has the edge for knowledge tasks in this comparison, averaging 53.3 versus 43.8. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.
Which is better for math, GPT-5.4 nano or Qwen3.6-27B?
Qwen3.6-27B has the edge for math in this comparison, averaging 89.2 versus 21. GPT-5.4 nano stays close enough that the answer can still flip depending on your workload.
Which is better for agentic tasks, GPT-5.4 nano or Qwen3.6-27B?
Qwen3.6-27B has the edge for agentic tasks in this comparison, averaging 59.3 versus 42.9. 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 nano or Qwen3.6-27B?
Qwen3.6-27B has the edge for multimodal and grounded tasks in this comparison, averaging 76.7 versus 66.1. 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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