Model comparison
GPT-4.1 nano vs Qwen3.8 Max
Qwen3.8 Max is the stronger model overall, scoring 56.8 to 27.9 on the Noometry Index. GPT-4.1 nano costs 17× less per token, which makes it the better buy when Qwen3.8 Max's lead doesn't matter for your workload.
Last verified . 23 shared benchmarks.
Alibaba (Qwen)Summary
- They share 23 benchmarks with published results for both. GPT-4.1 nano scores higher in 0 categories and Qwen3.8 Max in 10 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3.8 Max leads 73.2 to 26.9.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 28.9% for GPT-4.1 nano and 100% for Qwen3.8 Max.
- GPT-4.1 nano is cheaper at $0.10 / $0.40 per million input/output tokens, against $2 / $6 for Qwen3.8 Max.
- GPT-4.1 nano accepts more context: 1.05M tokens versus 1M.
Side by side
| GPT-4.1 nano | Qwen3.8 Max | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 27.9 | 56.8 |
| Released | 2025-04-14 | 2026-08-02 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 1M |
| Max output | 33K | 131K |
| Input $ / M tokens | $0.10 | $2 |
| Output $ / M tokens | $0.40 | $6 |
| Results tracked | 38 | 39 |
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Category by category
Coding Qwen3.8 Max leads
GPT-4.1 nano: 24.1 (#330), Qwen3.8 Max: 53.5 (#29)
| Benchmark | GPT-4.1 nano | Qwen3.8 Max |
|---|---|---|
| SciCode | 25.9% | 53.2% |
| LMArena Coding | 1306 | 1502 |
| DeepSWE | — | 57.5% |
| Aider Polyglot | 8.9% | — |
| LMArena WebDev | — | 1674 |
| FrontierSWE | — | 17.8% |
| WeirdML | 19% | — |
Agentic & Tool Use Qwen3.8 Max leads
GPT-4.1 nano: 26.5 (#104), Qwen3.8 Max: 45.4 (#14)
| Benchmark | GPT-4.1 nano | Qwen3.8 Max |
|---|---|---|
| APEX-Agents | — | 63.3% |
| Berkeley Function Calling Leaderboard | 33% | — |
| τ²-bench Banking | — | 55.1% |
| GDP.pdf | — | 23.2% |
Reasoning Qwen3.8 Max leads
GPT-4.1 nano: 8.5 (#349), Qwen3.8 Max: 54.4 (#26)
| Benchmark | GPT-4.1 nano | Qwen3.8 Max |
|---|---|---|
| CritPt | 0% | 20% |
| LMArena Hard Prompts | 1286 | 1496 |
| DTBench | 52.5% | 92% |
| LMCA | 5.5% | 46.2% |
| Epoch Capabilities Index | 129.62 | 156.41 |
| ARC-AGI-2 | 0% | — |
| Kagi LLM Benchmark | 33.3% | — |
| NYT Connections (extended) | — | 88.3% |
| ARC-AGI-1 | 0% | — |
| Chess Puzzles | — | 40% |
| Mystery Game Puzzles | — | 38% |
Math Qwen3.8 Max leads
GPT-4.1 nano: 26.9 (#252), Qwen3.8 Max: 73.2 (#20)
| Benchmark | GPT-4.1 nano | Qwen3.8 Max |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 28.9% | 100% |
| LMArena Math | 1274 | 1499 |
| FrontierMath (Tiers 1-3) | — | 74.7% |
| FrontierMath Tier 4 | — | 46.3% |
| ProofBench | — | 58% |
| Omni-MATH | 36.7% | — |
| MATH Level 5 | 70% | — |
| FrontierMath (Feb 2025 set) | 1% | — |
Knowledge Qwen3.8 Max leads
GPT-4.1 nano: 21.8 (#273), Qwen3.8 Max: 61.7 (#27)
| Benchmark | GPT-4.1 nano | Qwen3.8 Max |
|---|---|---|
| GPQA Diamond | 48.9% | 92.7% |
| SimpleQA Verified | 6% | 47.3% |
| LMArena Expert | 1272 | 1507 |
| MMLU-Pro | 55% | — |
| GPQA (HELM) | 50.7% | — |
Multimodal Qwen3.8 Max leads
GPT-4.1 nano: 29.2 (#113), Qwen3.8 Max: 37.2 (#75)
| Benchmark | GPT-4.1 nano | Qwen3.8 Max |
|---|---|---|
| LMArena Vision | 1063 | 1314 |
| Furniture Assembly | — | 20% |
Multilingual Qwen3.8 Max leads
GPT-4.1 nano: 41.6 (#205), Qwen3.8 Max: 56.7 (#18)
| Benchmark | GPT-4.1 nano | Qwen3.8 Max |
|---|---|---|
| LMArena Non-English | 1260 | 1472 |
| LMArena Chinese | 1270 | 1538 |
| LMArena German | 1288 | 1483 |
| LMArena Japanese | 1198 | 1467 |
| LMArena Russian | 1261 | 1481 |
| LMArena French | — | 1503 |
| LMArena Korean | — | 1461 |
| LMArena Spanish | — | 1492 |
Instruction Following Qwen3.8 Max leads
GPT-4.1 nano: 67.8 (#193), Qwen3.8 Max: 77.6 (#17)
| Benchmark | GPT-4.1 nano | Qwen3.8 Max |
|---|---|---|
| LMArena Instruction Following | 1267 | 1479 |
| IFEval | 84.3% | — |
Long Context Qwen3.8 Max leads
GPT-4.1 nano: 23.7 (#296), Qwen3.8 Max: 45.6 (#31)
| Benchmark | GPT-4.1 nano | Qwen3.8 Max |
|---|---|---|
| LMArena Longer Query | 1283 | 1489 |
| Fiction.LiveBench | 25% | — |
Writing & Preference Qwen3.8 Max leads
GPT-4.1 nano: 40.5 (#243), Qwen3.8 Max: 67.1 (#30)
| Benchmark | GPT-4.1 nano | Qwen3.8 Max |
|---|---|---|
| LMArena Text | 1285 | 1483 |
| LMArena Creative Writing | 1260 | 1479 |
| LMArena Multi-Turn | 1277 | 1489 |
| EQ-Bench Creative Writing | 946 | — |
| WildBench | 81.2% | — |
Frequently asked questions
Is GPT-4.1 nano better than Qwen3.8 Max?
Qwen3.8 Max is the stronger model overall, scoring 56.8 to 27.9 on the Noometry Index. GPT-4.1 nano costs 17× less per token, which makes it the better buy when Qwen3.8 Max's lead doesn't matter for your workload.
Which is cheaper, GPT-4.1 nano or Qwen3.8 Max?
GPT-4.1 nano is cheaper. It lists at $0.10 per million input tokens and $0.40 per million output tokens; Qwen3.8 Max lists at $2 and $6.
Is GPT-4.1 nano or Qwen3.8 Max better for coding?
Qwen3.8 Max scores higher on coding benchmarks: 53.5 versus 24.1 in the Noometry coding category.
Which has the bigger context window?
GPT-4.1 nano does, with 1.05M tokens against 1M.
How many benchmarks do GPT-4.1 nano and Qwen3.8 Max share?
23 benchmarks have published results for both models. GPT-4.1 nano has 38 scored results on Noometry and Qwen3.8 Max has 39.