Model comparison
GPT-4.1 vs Qwen-14B
GPT-4.1 is the stronger model overall, scoring 35.9 to 31.4 on the Noometry Index.
Last verified . 11 shared benchmarks.
Alibaba (Qwen)Summary
- They share 11 benchmarks with published results for both. GPT-4.1 scores higher in 5 categories and Qwen-14B in 2 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GPT-4.1 leads 57.6 to 27.6.
- Qwen-14B has downloadable open weights; the other is API-only.
Side by side
| GPT-4.1 | Qwen-14B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 35.9 | 31.4 |
| Released | 2025-04-14 | 2023-09-24 |
| Weights | Proprietary | Open |
| Context window | 1.05M | — |
| Max output | 33K | — |
| Input $ / M tokens | $2 | — |
| Output $ / M tokens | $8 | — |
| Results tracked | 52 | 18 |
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Category by category
Coding GPT-4.1 leads
GPT-4.1: 34.4 (#238), Qwen-14B: 31.2 (#288)
| Benchmark | GPT-4.1 | Qwen-14B |
|---|---|---|
| LMArena Coding | 1391 | 1071 |
| SWE-bench Verified | 48.5% | — |
| SWE-bench Verified (bash only) | 39.6% | — |
| Aider Polyglot | 52.4% | — |
| WeirdML | 39% | — |
| CadEval | 42% | — |
| ALE-Bench | 558.1 | — |
Agentic & Tool Use Not comparable
GPT-4.1: 34.7 (#43), Qwen-14B: —
| Benchmark | GPT-4.1 | Qwen-14B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 54% | — |
Reasoning Qwen-14B leads
GPT-4.1: 11.7 (#339), Qwen-14B: 19.6 (#257)
| Benchmark | GPT-4.1 | Qwen-14B |
|---|---|---|
| LMArena Hard Prompts | 1384 | 1027 |
| Epoch Capabilities Index | 136.78 | 113.03 |
| ARC-AGI-2 | 0.4% | — |
| SimpleBench | 27% | — |
| Kagi LLM Benchmark | 52.3% | — |
| ARC-AGI-1 | 5.5% | — |
| Chess Puzzles | 6% | — |
| EnigmaEval | 2.2% | — |
| DTBench | 68.3% | — |
| LMCA | 25.6% | — |
| BIG-Bench Hard | — | 55% |
| ForecastBench | 61.5 | — |
| LAMBADA | — | 71.1% |
| PIQA | — | 79.9% |
Math Qwen-14B leads
GPT-4.1: 22.3 (#280), Qwen-14B: 31.2 (#227)
| Benchmark | GPT-4.1 | Qwen-14B |
|---|---|---|
| LMArena Math | 1370 | 1068 |
| FrontierMath (Tiers 1-3) | 6% | — |
| OTIS Mock AIME 2024-2025 | 38.3% | — |
| Omni-MATH | 47.1% | — |
| MATH Level 5 | 83% | — |
| FrontierMath (Feb 2025 set) | 5.5% | — |
| FrontierMath Tier 4 (v1) | 0% | — |
| GSM8K | — | 61.3% |
Knowledge Not comparable
GPT-4.1: 37.1 (#160), Qwen-14B: —
| Benchmark | GPT-4.1 | Qwen-14B |
|---|---|---|
| GPQA Diamond | 66.9% | — |
| Humanity's Last Exam | 5.4% | — |
| SimpleQA Verified | 31.1% | — |
| MMLU-Pro | 81.1% | — |
| Vectara Hallucination Rate | 5.6% | — |
| GPQA (HELM) | 65.9% | — |
| LMArena Expert | 1364 | — |
| ARC (AI2) Challenge | — | 84.4% |
| BoolQ | — | 86.2% |
| MMLU | — | 66.3% |
Multimodal Not comparable
GPT-4.1: 38.2 (#67), Qwen-14B: —
| Benchmark | GPT-4.1 | Qwen-14B |
|---|---|---|
| LMArena Vision | 1211 | — |
| GeoBench | 72% | — |
Multilingual GPT-4.1 leads
GPT-4.1: 49.4 (#133), Qwen-14B: 27.5 (#275)
| Benchmark | GPT-4.1 | Qwen-14B |
|---|---|---|
| LMArena Non-English | 1370 | 1041 |
| LMArena Chinese | 1382 | 1077 |
| LMArena French | 1382 | — |
| LMArena German | 1381 | — |
| LMArena Japanese | 1319 | — |
| LMArena Korean | 1339 | — |
| LMArena Russian | 1377 | — |
| LMArena Spanish | 1376 | — |
Instruction Following GPT-4.1 leads
GPT-4.1: 71.3 (#153), Qwen-14B: 52.4 (#289)
| Benchmark | GPT-4.1 | Qwen-14B |
|---|---|---|
| LMArena Instruction Following | 1367 | 1031 |
| IFEval | 83.8% | — |
Long Context GPT-4.1 leads
GPT-4.1: 40.0 (#163), Qwen-14B: 31.3 (#280)
| Benchmark | GPT-4.1 | Qwen-14B |
|---|---|---|
| LMArena Longer Query | 1385 | 1028 |
| Fiction.LiveBench | 63.9% | — |
Writing & Preference GPT-4.1 leads
GPT-4.1: 57.6 (#125), Qwen-14B: 27.6 (#299)
| Benchmark | GPT-4.1 | Qwen-14B |
|---|---|---|
| LMArena Text | 1383 | 1051 |
| LMArena Creative Writing | 1363 | 1028 |
| LMArena Multi-Turn | 1398 | 1022 |
| EQ-Bench Creative Writing | 1420 | — |
| WildBench | 85.4% | — |
Frequently asked questions
Is GPT-4.1 better than Qwen-14B?
GPT-4.1 is the stronger model overall, scoring 35.9 to 31.4 on the Noometry Index.
Is GPT-4.1 or Qwen-14B better for coding?
GPT-4.1 scores higher on coding benchmarks: 34.4 versus 31.2 in the Noometry coding category.
How many benchmarks do GPT-4.1 and Qwen-14B share?
11 benchmarks have published results for both models. GPT-4.1 has 52 scored results on Noometry and Qwen-14B has 18.