Model comparison
GPT-4.1 vs Qwen3.5 397B-A17B
Qwen3.5 397B-A17B is the stronger model overall, scoring 46.0 to 35.9 on the Noometry Index.
Last verified . 27 shared benchmarks.
Alibaba (Qwen)Summary
- They share 27 benchmarks with published results for both. GPT-4.1 scores higher in 1 category and Qwen3.5 397B-A17B in 9 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3.5 397B-A17B leads 46.1 to 22.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 38.3% for GPT-4.1 and 88.9% for Qwen3.5 397B-A17B.
- Qwen3.5 397B-A17B is cheaper at $0.60 / $3.60 per million input/output tokens, against $2 / $8 for GPT-4.1.
- GPT-4.1 accepts more context: 1.05M tokens versus 262K.
- Qwen3.5 397B-A17B has downloadable open weights; the other is API-only.
Side by side
| GPT-4.1 | Qwen3.5 397B-A17B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 35.9 | 46.0 |
| Released | 2025-04-14 | 2026-02-01 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 262K |
| Max output | 33K | 66K |
| Input $ / M tokens | $2 | $0.60 |
| Output $ / M tokens | $8 | $3.60 |
| Results tracked | 52 | 36 |
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Category by category
Coding Qwen3.5 397B-A17B leads
GPT-4.1: 34.4 (#238), Qwen3.5 397B-A17B: 42.0 (#114)
| Benchmark | GPT-4.1 | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Coding | 1391 | 1465 |
| SWE-bench Verified | 48.5% | — |
| SWE-bench Verified (bash only) | 39.6% | — |
| Aider Polyglot | 52.4% | — |
| LMArena WebDev | — | 1400 |
| WeirdML | 39% | — |
| CadEval | 42% | — |
| ALE-Bench | 558.1 | — |
Agentic & Tool Use GPT-4.1 leads
GPT-4.1: 34.7 (#43), Qwen3.5 397B-A17B: 33.3 (#53)
| Benchmark | GPT-4.1 | Qwen3.5 397B-A17B |
|---|---|---|
| APEX-Agents | — | 24.9% |
| Berkeley Function Calling Leaderboard | 54% | — |
| τ²-bench Airline | — | 81.5% |
| τ²-bench Banking | — | 9.8% |
| τ²-bench Retail | — | 84.4% |
| τ²-bench Telecom | — | 97.8% |
Reasoning Qwen3.5 397B-A17B leads
GPT-4.1: 11.7 (#339), Qwen3.5 397B-A17B: 34.5 (#70)
| Benchmark | GPT-4.1 | Qwen3.5 397B-A17B |
|---|---|---|
| Kagi LLM Benchmark | 52.3% | 73.7% |
| Chess Puzzles | 6% | 13% |
| LMArena Hard Prompts | 1384 | 1448 |
| DTBench | 68.3% | 87.5% |
| LMCA | 25.6% | 37.9% |
| Epoch Capabilities Index | 136.78 | 146.65 |
| ARC-AGI-2 | 0.4% | — |
| SimpleBench | 27% | — |
| NYT Connections (extended) | — | 58.9% |
| ARC-AGI-1 | 5.5% | — |
| EnigmaEval | 2.2% | — |
| Thematic Generalization | — | 65.1% |
| Mystery Game Puzzles | — | 18% |
| ForecastBench | 61.5 | — |
Math Qwen3.5 397B-A17B leads
GPT-4.1: 22.3 (#280), Qwen3.5 397B-A17B: 46.1 (#73)
| Benchmark | GPT-4.1 | Qwen3.5 397B-A17B |
|---|---|---|
| FrontierMath (Tiers 1-3) | 6% | 31.2% |
| OTIS Mock AIME 2024-2025 | 38.3% | 88.9% |
| LMArena Math | 1370 | 1454 |
| Omni-MATH | 47.1% | — |
| MATH Level 5 | 83% | — |
| FrontierMath (Feb 2025 set) | 5.5% | — |
| FrontierMath Tier 4 (v1) | 0% | — |
Knowledge Qwen3.5 397B-A17B leads
GPT-4.1: 37.1 (#160), Qwen3.5 397B-A17B: 53.3 (#58)
| Benchmark | GPT-4.1 | Qwen3.5 397B-A17B |
|---|---|---|
| GPQA Diamond | 66.9% | 86.4% |
| LMArena Expert | 1364 | 1462 |
| Humanity's Last Exam | 5.4% | — |
| SimpleQA Verified | 31.1% | — |
| MMLU-Pro | 81.1% | — |
| Vectara Hallucination Rate | 5.6% | — |
| GPQA (HELM) | 65.9% | — |
Multimodal Qwen3.5 397B-A17B leads
GPT-4.1: 38.2 (#67), Qwen3.5 397B-A17B: 40.7 (#44)
| Benchmark | GPT-4.1 | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Vision | 1211 | 1263 |
| GeoBench | 72% | — |
Multilingual Qwen3.5 397B-A17B leads
GPT-4.1: 49.4 (#133), Qwen3.5 397B-A17B: 53.7 (#59)
| Benchmark | GPT-4.1 | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Non-English | 1370 | 1430 |
| LMArena Chinese | 1382 | 1500 |
| LMArena French | 1382 | 1461 |
| LMArena German | 1381 | 1447 |
| LMArena Japanese | 1319 | 1426 |
| LMArena Korean | 1339 | 1384 |
| LMArena Russian | 1377 | 1429 |
| LMArena Spanish | 1376 | 1441 |
Instruction Following Qwen3.5 397B-A17B leads
GPT-4.1: 71.3 (#153), Qwen3.5 397B-A17B: 75.0 (#77)
| Benchmark | GPT-4.1 | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Instruction Following | 1367 | 1424 |
| IFEval | 83.8% | — |
Long Context Qwen3.5 397B-A17B leads
GPT-4.1: 40.0 (#163), Qwen3.5 397B-A17B: 44.1 (#74)
| Benchmark | GPT-4.1 | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Longer Query | 1385 | 1442 |
| Fiction.LiveBench | 63.9% | — |
Writing & Preference Qwen3.5 397B-A17B leads
GPT-4.1: 57.6 (#125), Qwen3.5 397B-A17B: 62.3 (#79)
| Benchmark | GPT-4.1 | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Text | 1383 | 1438 |
| LMArena Creative Writing | 1363 | 1401 |
| EQ-Bench Creative Writing | 1420 | 1478 |
| LMArena Multi-Turn | 1398 | 1446 |
| WildBench | 85.4% | — |
Frequently asked questions
Is GPT-4.1 better than Qwen3.5 397B-A17B?
Qwen3.5 397B-A17B is the stronger model overall, scoring 46.0 to 35.9 on the Noometry Index.
Which is cheaper, GPT-4.1 or Qwen3.5 397B-A17B?
Qwen3.5 397B-A17B is cheaper. It lists at $0.60 per million input tokens and $3.60 per million output tokens; GPT-4.1 lists at $2 and $8.
Is GPT-4.1 or Qwen3.5 397B-A17B better for coding?
Qwen3.5 397B-A17B scores higher on coding benchmarks: 42.0 versus 34.4 in the Noometry coding category.
Which has the bigger context window?
GPT-4.1 does, with 1.05M tokens against 262K.
How many benchmarks do GPT-4.1 and Qwen3.5 397B-A17B share?
27 benchmarks have published results for both models. GPT-4.1 has 52 scored results on Noometry and Qwen3.5 397B-A17B has 36.