Model comparison
GPT-4.1 mini vs Qwen2.5-Max
Qwen2.5-Max is the stronger model overall, scoring 40.7 to 33.6 on the Noometry Index.
Last verified . 18 shared benchmarks.
Summary
- They share 18 benchmarks with published results for both. GPT-4.1 mini scores higher in 1 category and Qwen2.5-Max in 7 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Qwen2.5-Max leads 25.6 to 10.8.
Side by side
| GPT-4.1 mini | Qwen2.5-Max | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 33.6 | 40.7 |
| Released | 2025-04-14 | 2025-01-25 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | — |
| Max output | 33K | — |
| Input $ / M tokens | $0.40 | — |
| Output $ / M tokens | $1.60 | — |
| Results tracked | 47 | 27 |
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Category by category
Coding Qwen2.5-Max leads
GPT-4.1 mini: 30.6 (#293), Qwen2.5-Max: 41.8 (#117)
| Benchmark | GPT-4.1 mini | Qwen2.5-Max |
|---|---|---|
| LMArena Coding | 1367 | 1359 |
| SWE-bench Verified (bash only) | 23.9% | — |
| Aider Polyglot | 32.4% | — |
| SciCode | 40.4% | — |
| WeirdML | 37.6% | — |
| BigCodeBench Instruct | 48.9% | — |
| LiveBench Coding | — | 64.4% |
| CadEval | 16% | — |
Agentic & Tool Use Not comparable
GPT-4.1 mini: 33.3 (#55), Qwen2.5-Max: —
| Benchmark | GPT-4.1 mini | Qwen2.5-Max |
|---|---|---|
| Berkeley Function Calling Leaderboard | 50.5% | — |
Reasoning Qwen2.5-Max leads
GPT-4.1 mini: 10.8 (#340), Qwen2.5-Max: 25.6 (#147)
| Benchmark | GPT-4.1 mini | Qwen2.5-Max |
|---|---|---|
| LMArena Hard Prompts | 1349 | 1360 |
| Epoch Capabilities Index | 135.01 | 132.53 |
| ARC-AGI-2 | 0% | — |
| Kagi LLM Benchmark | 48.6% | — |
| ARC-AGI-1 | 3.5% | — |
| CritPt | 0% | — |
| Chess Puzzles | 7% | — |
| LiveBench Reasoning | — | 51.4% |
| Mystery Game Puzzles | 7% | — |
| DTBench | 68.8% | — |
| LiveBench Data Analysis | — | 67.9% |
| LMCA | 21.1% | — |
| LiveBench | — | 62.3% |
Math Qwen2.5-Max leads
GPT-4.1 mini: 24.1 (#270), Qwen2.5-Max: 36.9 (#162)
| Benchmark | GPT-4.1 mini | Qwen2.5-Max |
|---|---|---|
| LMArena Math | 1343 | 1369 |
| FrontierMath (Tiers 1-3) | 6.7% | — |
| OTIS Mock AIME 2024-2025 | 44.7% | — |
| Omni-MATH | 49.1% | — |
| LiveBench Math | — | 58.4% |
| MATH Level 5 | 87.3% | — |
| FrontierMath (Feb 2025 set) | 4.5% | — |
Knowledge Too close to call
GPT-4.1 mini: 34.7 (#194), Qwen2.5-Max: 35.3 (#186)
| Benchmark | GPT-4.1 mini | Qwen2.5-Max |
|---|---|---|
| LMArena Expert | 1338 | 1337 |
| GPQA Diamond | 65.8% | — |
| SimpleQA Verified | 12.7% | — |
| MMLU-Pro | 78.3% | — |
| Confabulations | — | 21.8% |
| GPQA (HELM) | 61.4% | — |
Multimodal Not comparable
GPT-4.1 mini: 35.8 (#82), Qwen2.5-Max: —
| Benchmark | GPT-4.1 mini | Qwen2.5-Max |
|---|---|---|
| LMArena Vision | 1181 | — |
Multilingual Qwen2.5-Max leads
GPT-4.1 mini: 45.7 (#166), Qwen2.5-Max: 48.1 (#146)
| Benchmark | GPT-4.1 mini | Qwen2.5-Max |
|---|---|---|
| LMArena Non-English | 1318 | 1352 |
| LMArena Chinese | 1329 | 1382 |
| LMArena French | 1358 | 1396 |
| LMArena German | 1351 | 1350 |
| LMArena Japanese | 1290 | 1300 |
| LMArena Korean | 1298 | 1304 |
| LMArena Russian | 1324 | 1353 |
| LMArena Spanish | 1319 | 1377 |
Instruction Following GPT-4.1 mini leads
GPT-4.1 mini: 73.7 (#118), Qwen2.5-Max: 71.3 (#152)
| Benchmark | GPT-4.1 mini | Qwen2.5-Max |
|---|---|---|
| LMArena Instruction Following | 1333 | 1335 |
| LiveBench Instruction Following | — | 75.3% |
| IFEval | 90.4% | — |
Long Context Qwen2.5-Max leads
GPT-4.1 mini: 31.8 (#275), Qwen2.5-Max: 41.4 (#142)
| Benchmark | GPT-4.1 mini | Qwen2.5-Max |
|---|---|---|
| LMArena Longer Query | 1344 | 1358 |
| Fiction.LiveBench | 44.4% | — |
Writing & Preference Qwen2.5-Max leads
GPT-4.1 mini: 48.6 (#199), Qwen2.5-Max: 55.4 (#146)
| Benchmark | GPT-4.1 mini | Qwen2.5-Max |
|---|---|---|
| LMArena Text | 1340 | 1367 |
| LMArena Creative Writing | 1300 | 1339 |
| LMArena Multi-Turn | 1354 | 1364 |
| Short-Story Creative Writing | — | 72.9% |
| EQ-Bench Creative Writing | 1147 | — |
| WildBench | 83.8% | — |
| LiveBench Language | — | 56.3% |
Frequently asked questions
Is GPT-4.1 mini better than Qwen2.5-Max?
Qwen2.5-Max is the stronger model overall, scoring 40.7 to 33.6 on the Noometry Index.
Is GPT-4.1 mini or Qwen2.5-Max better for coding?
Qwen2.5-Max scores higher on coding benchmarks: 41.8 versus 30.6 in the Noometry coding category.
How many benchmarks do GPT-4.1 mini and Qwen2.5-Max share?
18 benchmarks have published results for both models. GPT-4.1 mini has 47 scored results on Noometry and Qwen2.5-Max has 27.