Model comparison
GPT-4.1 nano vs Qwen3 32B
Qwen3 32B is the stronger model overall, scoring 39.2 to 27.9 on the Noometry Index. GPT-4.1 nano costs 7.0× less per token, which makes it the better buy when Qwen3 32B's lead doesn't matter for your workload.
Last verified . 24 shared benchmarks.
Alibaba (Qwen)Summary
- They share 24 benchmarks with published results for both. GPT-4.1 nano scores higher in 0 categories and Qwen3 32B in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in long context, where Qwen3 32B leads 43.8 to 23.7.
- The biggest single-benchmark swing is Fiction.LiveBench: 25% for GPT-4.1 nano and 74.2% for Qwen3 32B.
- GPT-4.1 nano is cheaper at $0.10 / $0.40 per million input/output tokens, against $0.70 / $2.80 for Qwen3 32B.
- GPT-4.1 nano accepts more context: 1.05M tokens versus 131K.
- Qwen3 32B has downloadable open weights; the other is API-only.
Side by side
| GPT-4.1 nano | Qwen3 32B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 27.9 | 39.2 |
| Released | 2025-04-14 | 2025-04 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 131K |
| Max output | 33K | 16K |
| Input $ / M tokens | $0.10 | $0.70 |
| Output $ / M tokens | $0.40 | $2.80 |
| Results tracked | 38 | 26 |
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Category by category
Coding Qwen3 32B leads
GPT-4.1 nano: 24.1 (#330), Qwen3 32B: 37.7 (#190)
| Benchmark | GPT-4.1 nano | Qwen3 32B |
|---|---|---|
| Aider Polyglot | 8.9% | 40% |
| SciCode | 25.9% | 35.4% |
| LMArena Coding | 1306 | 1358 |
| WeirdML | 19% | — |
Agentic & Tool Use Qwen3 32B leads
GPT-4.1 nano: 26.5 (#104), Qwen3 32B: 32.6 (#62)
| Benchmark | GPT-4.1 nano | Qwen3 32B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 33% | 48.7% |
Reasoning Qwen3 32B leads
GPT-4.1 nano: 8.5 (#349), Qwen3 32B: 20.2 (#241)
| Benchmark | GPT-4.1 nano | Qwen3 32B |
|---|---|---|
| Kagi LLM Benchmark | 33.3% | 54.9% |
| CritPt | 0% | 0.3% |
| LMArena Hard Prompts | 1286 | 1334 |
| DTBench | 52.5% | 67.5% |
| LMCA | 5.5% | 17.3% |
| Epoch Capabilities Index | 129.62 | 138.51 |
| ARC-AGI-2 | 0% | — |
| ARC-AGI-1 | 0% | — |
| Chess Puzzles | — | 5% |
Math Qwen3 32B leads
GPT-4.1 nano: 26.9 (#252), Qwen3 32B: 39.7 (#99)
| Benchmark | GPT-4.1 nano | Qwen3 32B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 28.9% | 66.9% |
| LMArena Math | 1274 | 1399 |
| Omni-MATH | 36.7% | — |
| MATH Level 5 | 70% | — |
| FrontierMath (Feb 2025 set) | 1% | — |
Knowledge Qwen3 32B leads
GPT-4.1 nano: 21.8 (#273), Qwen3 32B: 40.0 (#125)
| Benchmark | GPT-4.1 nano | Qwen3 32B |
|---|---|---|
| GPQA Diamond | 48.9% | 65.7% |
| LMArena Expert | 1272 | 1362 |
| SimpleQA Verified | 6% | — |
| MMLU-Pro | 55% | — |
| Vectara Hallucination Rate | — | 5.9% |
| GPQA (HELM) | 50.7% | — |
Multimodal Not comparable
GPT-4.1 nano: 29.2 (#113), Qwen3 32B: —
| Benchmark | GPT-4.1 nano | Qwen3 32B |
|---|---|---|
| LMArena Vision | 1063 | — |
Multilingual Qwen3 32B leads
GPT-4.1 nano: 41.6 (#205), Qwen3 32B: 45.6 (#167)
| Benchmark | GPT-4.1 nano | Qwen3 32B |
|---|---|---|
| LMArena Non-English | 1260 | 1317 |
| LMArena Chinese | 1270 | 1357 |
| LMArena German | 1288 | 1341 |
| LMArena Russian | 1261 | 1311 |
| LMArena Japanese | 1198 | — |
Instruction Following Qwen3 32B leads
GPT-4.1 nano: 67.8 (#193), Qwen3 32B: 68.9 (#179)
| Benchmark | GPT-4.1 nano | Qwen3 32B |
|---|---|---|
| LMArena Instruction Following | 1267 | 1305 |
| IFEval | 84.3% | — |
Long Context Qwen3 32B leads
GPT-4.1 nano: 23.7 (#296), Qwen3 32B: 43.8 (#87)
| Benchmark | GPT-4.1 nano | Qwen3 32B |
|---|---|---|
| Fiction.LiveBench | 25% | 74.2% |
| LMArena Longer Query | 1283 | 1327 |
Writing & Preference Qwen3 32B leads
GPT-4.1 nano: 40.5 (#243), Qwen3 32B: 52.9 (#163)
| Benchmark | GPT-4.1 nano | Qwen3 32B |
|---|---|---|
| LMArena Text | 1285 | 1340 |
| LMArena Creative Writing | 1260 | 1297 |
| LMArena Multi-Turn | 1277 | 1331 |
| EQ-Bench Creative Writing | 946 | — |
| WildBench | 81.2% | — |
Frequently asked questions
Is GPT-4.1 nano better than Qwen3 32B?
Qwen3 32B is the stronger model overall, scoring 39.2 to 27.9 on the Noometry Index. GPT-4.1 nano costs 7.0× less per token, which makes it the better buy when Qwen3 32B's lead doesn't matter for your workload.
Which is cheaper, GPT-4.1 nano or Qwen3 32B?
GPT-4.1 nano is cheaper. It lists at $0.10 per million input tokens and $0.40 per million output tokens; Qwen3 32B lists at $0.70 and $2.80.
Is GPT-4.1 nano or Qwen3 32B better for coding?
Qwen3 32B scores higher on coding benchmarks: 37.7 versus 24.1 in the Noometry coding category.
Which has the bigger context window?
GPT-4.1 nano does, with 1.05M tokens against 131K.
How many benchmarks do GPT-4.1 nano and Qwen3 32B share?
24 benchmarks have published results for both models. GPT-4.1 nano has 38 scored results on Noometry and Qwen3 32B has 26.