Model comparison
GPT-4.1 nano vs Qwen3.6-35B-A3B
Head-to-head evidence from 18 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Verified leaderboard positions: GPT-4.1 nano unranked; Qwen3.6-35B-A3B #31
BenchAlign evidence: GPT-4.1 nano estimated; Qwen3.6-35B-A3B estimated. Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-4.1 nano and Qwen3.6-35B-A3B share 18 comparable benchmark results. 2 of 8 categories are comparable. 4 results are unique to GPT-4.1 nano; 40 to Qwen3.6-35B-A3B.
Updated July 15, 2026- Shared results
- 18
- GPT-4.1 nano only
- 4
- Qwen3.6-35B-A3B only
- 40
- Comparable categories
- 2 / 8
Pick Qwen3.6-35B-A3B 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 18 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
Qwen3.6-35B-A3B is clearly ahead on the provisional aggregate, 59 to 30. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.6-35B-A3B's sharpest advantage is in mathematics, where it averages 88.2 against 1. The single biggest benchmark swing on the page is GPQA, 50.3% to 86%.
Qwen3.6-35B-A3B 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 Qwen3.6-35B-A3B.
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 | Qwen3.6-35B-A3B |
|---|---|---|---|
| Math | GPT-4.1 nano1.0 | Margin→ 87.2 | Qwen3.6-35B-A3B88.2 |
| Knowledge | GPT-4.1 nano50.3 | Margin→ 1.5 | Qwen3.6-35B-A3B51.8 |
| Agentic | GPT-4.1 nanoNot measured | MarginNo overlap | Qwen3.6-35B-A3B51.5 |
| Coding | GPT-4.1 nanoNot measured | MarginNo overlap | Qwen3.6-35B-A3B73.8 |
| Multimodal | GPT-4.1 nanoNot measured | MarginNo overlap | Qwen3.6-35B-A3B76.3 |
| Inst. Following | GPT-4.1 nano83.2 | MarginNo overlap | Qwen3.6-35B-A3BNot measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
GPQA
KnowledgeA 50.3%B 86%Winner: Qwen3.6-35B-A3BΔ 35.7GPQA: GPT-4.1 nano scored 50.3%; Qwen3.6-35B-A3B scored 86%. Qwen3.6-35B-A3B 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 | Qwen3.6-35B-A3B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-4.1 nano$0.1 input / $0.4 output | Qwen3.6-35B-A3BNot available | A complete price comparison is not available. |
| Generation speedtokens per second | GPT-4.1 nano181 tok/s | Qwen3.6-35B-A3BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-4.1 nano0.63 s | Qwen3.6-35B-A3BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-4.1 nano1M | Qwen3.6-35B-A3B262K | GPT-4.1 nano lists the larger context window. |
Benchmark Deep Dive
Agentic15 benchmarks
| Benchmark | GPT-4.1 nano | Qwen3.6-35B-A3B | Result |
|---|---|---|---|
| AA Agentic IndexSource | 1.2% | 21.4% | Qwen3.6-35B-A3B leads |
| τ²-bench resultsSource | 17.3% | 95.3% | Qwen3.6-35B-A3B leads |
| GDPval-AASource | 0.0% | 27.4% | Qwen3.6-35B-A3B leads |
| GDPval-AASource | 41 | 1049 | Qwen3.6-35B-A3B leads |
| Terminal-Bench 2.0Source | — | 51.5% | Not comparable |
| Claw-EvalSource | — | 68.7% | Not comparable |
| QwenClawBenchSource | — | 52.6% | Not comparable |
| QwenWebBenchSource | — | 1397 | Not comparable |
| τ³-bench resultsSource | — | 67.2% | Not comparable |
| VITA-BenchSource | — | 35.6% | Not comparable |
| DeepPlanningSource | — | 25.9% | Not comparable |
| ToolathlonSource | — | 26.9% | Not comparable |
| MCP AtlasSource | — | 62.8% | Not comparable |
| WideResearchSource | — | 60.1% | Not comparable |
| Gert LabsSource | — | 42.65% | Not comparable |
Coding9 benchmarks
| Benchmark | GPT-4.1 nano | Qwen3.6-35B-A3B | Result |
|---|---|---|---|
| AA Coding IndexSource | 11.1% | 41.9% | Qwen3.6-35B-A3B leads |
| Terminal-Bench HardSource | 3.8% | 34.8% | Qwen3.6-35B-A3B leads |
| AA-SciCodeSource | 25.9% | 35.8% | Qwen3.6-35B-A3B leads |
| SWE-bench VerifiedSource | — | 73.4% | Not comparable |
| SWE MultilingualSource | — | 67.2% | Not comparable |
| SWE-bench ProSource | — | 49.5% | Not comparable |
| Terminal-Bench 2.0Source | — | 51.5% | Not comparable |
| LiveCodeBenchSource | — | 80.4% | Not comparable |
| NL2RepoSource | — | 29.4% | Not comparable |
Reasoning2 benchmarks
KnowledgeQwen3.6-35B-A3B wins12 benchmarks
| Benchmark | GPT-4.1 nano | Qwen3.6-35B-A3B | Result |
|---|---|---|---|
| MMLUSource | 80.1% | — | Not comparable |
| GPQASource | 50.3% | 86% | Qwen3.6-35B-A3B leads |
| Artificial Analysis Intelligence IndexSource | 9.6% | 31.6% | Qwen3.6-35B-A3B leads |
| AA-GPQA DiamondSource | 51.2% | 84.1% | Qwen3.6-35B-A3B leads |
| AA-HLESource | 3.9% | 20.2% | Qwen3.6-35B-A3B leads |
| AA-Omniscience IndexSource | -56.4% | -21.4% | Qwen3.6-35B-A3B leads |
| AA-Omniscience AccuracySource | 13.3% | 18.9% | Qwen3.6-35B-A3B leads |
| AA-Omniscience Hallucination RateSource | 80.4% | 49.7% | Qwen3.6-35B-A3B leads |
| MMLU-ProSource | — | 85.2% | Not comparable |
| SuperGPQASource | — | 64.7% | Not comparable |
| C-EvalSource | — | 90% | Not comparable |
| HLESource | — | 21.4% | Not comparable |
MathQwen3.6-35B-A3B wins6 benchmarks
Multimodal16 benchmarks
| Benchmark | GPT-4.1 nano | Qwen3.6-35B-A3B | Result |
|---|---|---|---|
| AA-MMMU-ProSource | 40.1% | 75.0% | Qwen3.6-35B-A3B leads |
| Design Arena WebsiteSource | 1007 | — | Not comparable |
| MMMUSource | — | 81.7% | Not comparable |
| MMMU-ProSource | — | 75.3% | Not comparable |
| RealWorldQASource | — | 85.3% | Not comparable |
| OmniDocBench 1.5Source | — | 89.9% | Not comparable |
| CharXivSource | — | 78% | Not comparable |
| SimpleVQASource | — | 58.9% | Not comparable |
| CC-OCRSource | — | 81.9% | Not comparable |
| AI2D_TESTSource | — | 92.7% | Not comparable |
| RefCOCO (avg)Source | — | 92.0% | Not comparable |
| ODINW13Source | — | 50.8% | Not comparable |
| Video-MME (with subtitle)Source | — | 86.6% | Not comparable |
| Video-MME (w/o subtitle)Source | — | 82.5% | Not comparable |
| VideoMMMUSource | — | 83.7% | Not comparable |
| MLVU (M-Avg)Source | — | 86.2% | Not comparable |
Frequently Asked Questions (3)
Which is better, GPT-4.1 nano or Qwen3.6-35B-A3B?
Qwen3.6-35B-A3B is ahead on BenchLM's provisional leaderboard, 59 to 30. The biggest single separator in this matchup is GPQA, where the scores are 50.3% and 86%.
Which is better for knowledge tasks, GPT-4.1 nano or Qwen3.6-35B-A3B?
Qwen3.6-35B-A3B has the edge for knowledge tasks in this comparison, averaging 51.8 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 Qwen3.6-35B-A3B?
Qwen3.6-35B-A3B has the edge for math in this comparison, averaging 88.2 versus 1. GPT-4.1 nano stays close enough that the answer can still flip depending on your workload.
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