Model comparison
GPT-4.1 nano vs Qwen3.5 397B
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-4.1 nano #170 (Estimated); Qwen3.5 397B #77 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-4.1 nano and Qwen3.5 397B share 18 comparable benchmark results. 3 of 8 categories are comparable. 3 results are unique to GPT-4.1 nano; 37 to Qwen3.5 397B.
Updated July 24, 2026- Shared results
- 18
- GPT-4.1 nano only
- 3
- Qwen3.5 397B only
- 37
- Comparable categories
- 3 / 8
Pick Qwen3.5 397B if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if you want the cheaper token bill or you need the larger 1M context window.
Confidence note. This is a partial-evidence comparison with 18 shared benchmark results across 6 evidence categories; 3 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
Qwen3.5 397B is clearly ahead on the BenchAlign aggregate, 56.26 to 41.14. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.5 397B's sharpest advantage is in mathematics, where it averages 90.6 against 1. The single biggest benchmark swing on the page is GPQA, 50.3% to 88.4%.
Qwen3.5 397B is also the more expensive model on tokens at $0.60 input / $3.60 output per 1M tokens, versus $0.10 input / $0.40 output per 1M tokens for GPT-4.1 nano. That is roughly 9.0x on output cost alone. GPT-4.1 nano gives you the larger context window at 1M, compared with 128K for Qwen3.5 397B.
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 | Qwen3.5 397B |
|---|---|---|---|
| Math | GPT-4.1 nano1.0 | Margin→ 89.6 | Qwen3.5 397B90.6 |
| Inst. Following | GPT-4.1 nano83.2 | Margin→ 9.4 | Qwen3.5 397B92.6 |
| Knowledge | GPT-4.1 nano50.3 | Margin→ 6.3 | Qwen3.5 397B56.6 |
| Agentic | GPT-4.1 nanoNot measured | MarginNo overlap | Qwen3.5 397B56.5 |
| Coding | GPT-4.1 nanoNot measured | MarginNo overlap | Qwen3.5 397B66.5 |
| Reasoning | GPT-4.1 nanoNot measured | MarginNo overlap | Qwen3.5 397B63.2 |
| Multilingual | GPT-4.1 nanoNot measured | MarginNo overlap | Qwen3.5 397B84.7 |
| Multimodal | GPT-4.1 nanoNot measured | MarginNo overlap | Qwen3.5 397B79.6 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
GPQA
KnowledgeA 50.3%B 88.4%Winner: Qwen3.5 397BΔ 38.1GPQA: GPT-4.1 nano scored 50.3%; Qwen3.5 397B scored 88.4%. Qwen3.5 397B wins this benchmark. - Source ↗
IFEval
Inst. FollowingA 83.2%B 92.6%Winner: Qwen3.5 397BΔ 9.4IFEval: GPT-4.1 nano scored 83.2%; Qwen3.5 397B scored 92.6%. Qwen3.5 397B 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 | Qwen3.5 397B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-4.1 nano$0.1 input / $0.4 output | Qwen3.5 397B$0.6 input / $3.6 output | GPT-4.1 nano has the lower combined listed price. |
| Generation speedtokens per second | GPT-4.1 nano181 tok/s | Qwen3.5 397B96 tok/s | GPT-4.1 nano has the higher measured throughput. |
| First-answer latencyseconds to first token | GPT-4.1 nano0.63 s | Qwen3.5 397B2.44 s | GPT-4.1 nano reaches the first token sooner. |
| Context windowmaximum listed tokens | GPT-4.1 nano1M | Qwen3.5 397B128K | GPT-4.1 nano lists the larger context window. |
Benchmark Deep Dive
Agentic18 benchmarks
| Benchmark | GPT-4.1 nano | Qwen3.5 397B | Result |
|---|---|---|---|
| AA Agentic IndexSource | 1.2% | 19.9% | Qwen3.5 397B leads |
| τ²-bench resultsSource | 17.3% | 95.6% | Qwen3.5 397B leads |
| GDPval-AASource | 0.0% | 23.1% | Qwen3.5 397B leads |
| GDPval-AASource | 63 | 962 | Qwen3.5 397B leads |
| Terminal-Bench 2.0Source | — | 52.5% | Not comparable |
| BrowseCompSource | — | 62% | Not comparable |
| Claw-EvalSource | — | 56.8% | Not comparable |
| QwenClawBenchSource | — | 51.8% | Not comparable |
| τ³-bench resultsSource | — | 68.4% | Not comparable |
| VITA-BenchSource | — | 43.7% | Not comparable |
| DeepPlanningSource | — | 37.6% | Not comparable |
| ToolathlonSource | — | 36.3% | Not comparable |
| MCP AtlasSource | — | 46.1% | Not comparable |
| MCP-TasksSource | — | 74.2% | Not comparable |
| WideResearchSource | — | 74.0% | Not comparable |
| Gert LabsSource | — | 46.76% | Not comparable |
| ResearchClawBenchSource | — | 14.2% | Not comparable |
| APEX-Agents-AASource | — | 15.3% | Not comparable |
Coding5 benchmarks
Reasoning4 benchmarks
KnowledgeQwen3.5 397B wins13 benchmarks
| Benchmark | GPT-4.1 nano | Qwen3.5 397B | Result |
|---|---|---|---|
| MMLUSource | 80.1% | — | Not comparable |
| GPQASource | 50.3% | 88.4% | Qwen3.5 397B leads |
| Artificial Analysis Intelligence IndexSource | 9.6% | 33.7% | Qwen3.5 397B leads |
| AA-GPQA DiamondSource | 51.2% | 89.3% | Qwen3.5 397B leads |
| AA-HLESource | 3.9% | 27.3% | Qwen3.5 397B leads |
| AA-Omniscience IndexSource | -56.4% | -29.8% | Qwen3.5 397B leads |
| AA-Omniscience AccuracySource | 13.3% | 31.4% | Qwen3.5 397B leads |
| AA-Omniscience Hallucination RateSource | 80.4% | 89.1% | GPT-4.1 nano leads |
| SuperGPQASource | — | 70.4% | Not comparable |
| MMLU-ProSource | — | 87.8% | Not comparable |
| MMLU-ReduxSource | — | 94.9% | Not comparable |
| C-EvalSource | — | 93% | Not comparable |
| HLESource | — | 28.7% | Not comparable |
MathQwen3.5 397B wins6 benchmarks
Multilingual2 benchmarks
Multimodal8 benchmarks
| Benchmark | GPT-4.1 nano | Qwen3.5 397B | Result |
|---|---|---|---|
| AA-MMMU-ProSource | 40.1% | 77.3% | Qwen3.5 397B leads |
| Design Arena WebsiteSource | 1003 | — | Not comparable |
| MMMU-ProSource | — | 79% | Not comparable |
| MathVisionSource | — | 88.6% | Not comparable |
| CharXivSource | — | 80.8% | Not comparable |
| VideoMMMUSource | — | 84.7% | Not comparable |
| ScreenSpot ProSource | — | 65.6% | Not comparable |
| V*Source | — | 95.8% | Not comparable |
Frequently Asked Questions (4)
Which is better, GPT-4.1 nano or Qwen3.5 397B?
Qwen3.5 397B is ahead on BenchLM's BenchAlign leaderboard, 56.26 to 41.14. The biggest single separator in this matchup is GPQA, where the scores are 50.3% and 88.4%.
Which is better for knowledge tasks, GPT-4.1 nano or Qwen3.5 397B?
Qwen3.5 397B has the edge for knowledge tasks in this comparison, averaging 56.6 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 Qwen3.5 397B?
Qwen3.5 397B has the edge for math in this comparison, averaging 90.6 versus 1. GPT-4.1 nano stays close enough that the answer can still flip depending on your workload.
Which is better for instruction following, GPT-4.1 nano or Qwen3.5 397B?
Qwen3.5 397B has the edge for instruction following in this comparison, averaging 92.6 versus 83.2. Inside this category, AA-IFBench is the benchmark that creates the most daylight between them.