Model comparison
GPT-4.1 nano vs Qwen3.6-27B
Head-to-head evidence from 17 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-4.1 nano #161 (Estimated); Qwen3.6-27B #93 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-4.1 nano and Qwen3.6-27B share 17 comparable benchmark results. 2 of 8 categories are comparable. 4 results are unique to GPT-4.1 nano; 37 to Qwen3.6-27B.
Updated July 21, 2026- Shared results
- 17
- GPT-4.1 nano only
- 4
- Qwen3.6-27B only
- 37
- Comparable categories
- 2 / 8
Pick Qwen3.6-27B if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if you need the larger 1M context window or you would rather avoid the extra latency and token burn of a reasoning model.
Confidence note. This is a partial-evidence comparison with 17 shared benchmark results across 6 evidence categories; 2 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
Qwen3.6-27B is clearly ahead on the BenchAlign aggregate, 53.82 to 42.06. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.6-27B's sharpest advantage is in mathematics, where it averages 89.2 against 1. The single biggest benchmark swing on the page is GPQA, 50.3% to 87.8%.
GPT-4.1 nano is also the more expensive model on tokens at $0.10 input / $0.40 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. Qwen3.6-27B is the reasoning model in the pair, while GPT-4.1 nano is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. GPT-4.1 nano 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-4.1 nano | Δ | Qwen3.6-27B |
|---|---|---|---|
| Math | GPT-4.1 nano1.0 | Margin→ 88.2 | Qwen3.6-27B89.2 |
| Knowledge | GPT-4.1 nano50.3 | Margin→ 3.0 | Qwen3.6-27B53.3 |
| Agentic | GPT-4.1 nanoNot measured | MarginNo overlap | Qwen3.6-27B59.3 |
| Coding | GPT-4.1 nanoNot measured | MarginNo overlap | Qwen3.6-27B77.5 |
| Multimodal | GPT-4.1 nanoNot measured | MarginNo overlap | Qwen3.6-27B76.7 |
| Inst. Following | GPT-4.1 nano83.2 | 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 50.3%B 87.8%Winner: Qwen3.6-27BΔ 37.5GPQA: GPT-4.1 nano scored 50.3%; 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-4.1 nano | Qwen3.6-27B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-4.1 nano$0.1 input / $0.4 output | Qwen3.6-27B$0 input / $0 output | Qwen3.6-27B has the lower combined listed price. |
| Generation speedtokens per second | GPT-4.1 nano181 tok/s | Qwen3.6-27BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-4.1 nano0.63 s | Qwen3.6-27BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-4.1 nano1M | Qwen3.6-27B262K | GPT-4.1 nano lists the larger context window. |
Benchmark Deep Dive
Agentic10 benchmarks
| Benchmark | GPT-4.1 nano | Qwen3.6-27B | Result |
|---|---|---|---|
| AA Agentic IndexSource | 1.2% | 27.0% | Qwen3.6-27B leads |
| τ²-bench resultsSource | 17.3% | 94.2% | Qwen3.6-27B leads |
| GDPval-AASource | 0.0% | 32.0% | Qwen3.6-27B leads |
| GDPval-AASource | 41 | 1140 | Qwen3.6-27B leads |
| 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 |
| Gert LabsSource | — | 54.84% | Not comparable |
Coding8 benchmarks
| Benchmark | GPT-4.1 nano | Qwen3.6-27B | Result |
|---|---|---|---|
| AA Coding IndexSource | 11.1% | 53.7% | Qwen3.6-27B leads |
| AA-SciCodeSource | 25.9% | 39.8% | Qwen3.6-27B 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-4.1 nano | Qwen3.6-27B | Result |
|---|---|---|---|
| MMLUSource | 80.1% | — | Not comparable |
| GPQASource | 50.3% | 87.8% | Qwen3.6-27B leads |
| Artificial Analysis Intelligence IndexSource | 9.6% | 37.0% | Qwen3.6-27B leads |
| AA-GPQA DiamondSource | 51.2% | 84.2% | Qwen3.6-27B leads |
| AA-HLESource | 3.9% | 21.6% | Qwen3.6-27B leads |
| AA-Omniscience IndexSource | -56.4% | -19.8% | Qwen3.6-27B leads |
| AA-Omniscience AccuracySource | 13.3% | 19.2% | Qwen3.6-27B leads |
| AA-Omniscience Hallucination RateSource | 80.4% | 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 wins6 benchmarks
Multimodal17 benchmarks
| Benchmark | GPT-4.1 nano | Qwen3.6-27B | Result |
|---|---|---|---|
| AA-MMMU-ProSource | 40.1% | 74.6% | Qwen3.6-27B leads |
| Design Arena WebsiteSource | 1003 | — | Not comparable |
| 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 |
Frequently Asked Questions (3)
Which is better, GPT-4.1 nano or Qwen3.6-27B?
Qwen3.6-27B is ahead on BenchLM's BenchAlign leaderboard, 53.82 to 42.06. The biggest single separator in this matchup is GPQA, where the scores are 50.3% and 87.8%.
Which is better for knowledge tasks, GPT-4.1 nano or Qwen3.6-27B?
Qwen3.6-27B has the edge for knowledge tasks in this comparison, averaging 53.3 versus 50.3. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Which is better for math, GPT-4.1 nano or Qwen3.6-27B?
Qwen3.6-27B has the edge for math in this comparison, averaging 89.2 versus 1. GPT-4.1 nano 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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