Model comparison
GPT-4o mini vs Qwen3.7 Max
Qwen3.7 Max is the stronger model overall, scoring 51.5 to 25.5 on the Noometry Index. GPT-4o mini costs 14× less per token, which makes it the better buy when Qwen3.7 Max's lead doesn't matter for your workload.
Last verified . 22 shared benchmarks.
Alibaba (Qwen)Summary
- They share 22 benchmarks with published results for both. GPT-4o mini scores higher in 1 category and Qwen3.7 Max in 8 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3.7 Max leads 62.4 to 10.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 6.9% for GPT-4o mini and 95.6% for Qwen3.7 Max.
- GPT-4o mini is cheaper at $0.15 / $0.60 per million input/output tokens, against $2.50 / $7.50 for Qwen3.7 Max.
- Qwen3.7 Max accepts more context: 1M tokens versus 128K.
Side by side
| GPT-4o mini | Qwen3.7 Max | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 25.5 | 51.5 |
| Released | 2024-07-18 | 2026-05-19 |
| Weights | Proprietary | Proprietary |
| Context window | 128K | 1M |
| Max output | 16K | 131K |
| Input $ / M tokens | $0.15 | $2.50 |
| Output $ / M tokens | $0.60 | $7.50 |
| Results tracked | 60 | 33 |
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Category by category
Coding Qwen3.7 Max leads
GPT-4o mini: 22.0 (#335), Qwen3.7 Max: 50.4 (#45)
| Benchmark | GPT-4o mini | Qwen3.7 Max |
|---|---|---|
| LMArena Coding | 1290 | 1498 |
| SWE-bench Verified | — | 77.3% |
| Aider Polyglot | 3.6% | — |
| LMArena WebDev | — | 1515 |
| SciCode | — | 48.8% |
| WeirdML | 11.8% | — |
| BigCodeBench Instruct | 46.1% | — |
| LiveBench Coding | 43.1% | — |
| BigCodeBench Complete | 57.4% | — |
| ALE-Bench | — | 1,189 |
| HumanEval+ | 83.5% | — |
| MBPP+ | 72.2% | — |
Agentic & Tool Use GPT-4o mini leads
GPT-4o mini: 27.5 (#101), Qwen3.7 Max: 22.1 (#135)
Reasoning Qwen3.7 Max leads
GPT-4o mini: 8.7 (#347), Qwen3.7 Max: 49.2 (#38)
| Benchmark | GPT-4o mini | Qwen3.7 Max |
|---|---|---|
| SimpleBench | 10.7% | 70.4% |
| Chess Puzzles | 0% | 19% |
| LMArena Hard Prompts | 1267 | 1483 |
| Mystery Game Puzzles | 12% | 32% |
| DTBench | 54.4% | 92.3% |
| LMCA | 10.4% | 44% |
| Epoch Capabilities Index | 126.56 | 153.68 |
| ARC-AGI-2 | 0% | — |
| Kagi LLM Benchmark | 28.8% | — |
| NYT Connections (extended) | — | 85.1% |
| CritPt | — | 13.4% |
| EBR-Bench | — | 9.5% |
| LiveBench Reasoning | 32.8% | — |
| LiveBench Data Analysis | 50% | — |
| LiveBench | 41.3% | — |
| PIQA | 88.7% | — |
Math Qwen3.7 Max leads
GPT-4o mini: 10.4 (#314), Qwen3.7 Max: 62.4 (#32)
| Benchmark | GPT-4o mini | Qwen3.7 Max |
|---|---|---|
| FrontierMath (Tiers 1-3) | 0.7% | 64.6% |
| OTIS Mock AIME 2024-2025 | 6.9% | 95.6% |
| LMArena Math | 1267 | 1490 |
| FrontierMath Tier 4 | — | 34.1% |
| ProofBench | — | 26% |
| Omni-MATH | 28% | — |
| LiveBench Math | 36.3% | — |
| MATH Level 5 | 52.6% | — |
| GSM8K | 91.3% | — |
Knowledge Qwen3.7 Max leads
GPT-4o mini: 17.7 (#284), Qwen3.7 Max: 61.6 (#28)
| Benchmark | GPT-4o mini | Qwen3.7 Max |
|---|---|---|
| GPQA Diamond | 37.7% | 90.9% |
| SimpleQA Verified | 8.3% | 55.8% |
| LMArena Expert | 1235 | 1488 |
| MMLU-Pro | 60.3% | — |
| Confabulations | 37.2% | — |
| GPQA (HELM) | 36.8% | — |
| BoolQ | 88.7% | — |
| MMLU | 81.8% | — |
Multimodal Not comparable
GPT-4o mini: 25.9 (#122), Qwen3.7 Max: —
| Benchmark | GPT-4o mini | Qwen3.7 Max |
|---|---|---|
| LMArena Vision | 1066 | — |
| Video-MME | 64.8% | — |
| GeoBench | 64% | — |
| VPCT | 34% | — |
Multilingual Qwen3.7 Max leads
GPT-4o mini: 42.0 (#199), Qwen3.7 Max: 56.9 (#15)
| Benchmark | GPT-4o mini | Qwen3.7 Max |
|---|---|---|
| LMArena Non-English | 1266 | 1474 |
| LMArena Chinese | 1265 | 1530 |
| LMArena Russian | 1275 | 1484 |
| LMArena French | 1297 | — |
| LMArena German | 1272 | — |
| LMArena Japanese | 1216 | — |
| LMArena Korean | 1195 | — |
| LMArena Spanish | 1276 | — |
Instruction Following Qwen3.7 Max leads
GPT-4o mini: 61.9 (#239), Qwen3.7 Max: 76.7 (#38)
| Benchmark | GPT-4o mini | Qwen3.7 Max |
|---|---|---|
| LMArena Instruction Following | 1258 | 1460 |
| LiveBench Instruction Following | 56.8% | — |
| IFEval | 78.2% | — |
Long Context Qwen3.7 Max leads
GPT-4o mini: 39.1 (#186), Qwen3.7 Max: 45.4 (#40)
| Benchmark | GPT-4o mini | Qwen3.7 Max |
|---|---|---|
| LMArena Longer Query | 1289 | 1482 |
Writing & Preference Qwen3.7 Max leads
GPT-4o mini: 39.5 (#248), Qwen3.7 Max: 65.0 (#54)
| Benchmark | GPT-4o mini | Qwen3.7 Max |
|---|---|---|
| LMArena Text | 1286 | 1476 |
| LMArena Creative Writing | 1268 | 1449 |
| LMArena Multi-Turn | 1285 | 1481 |
| Short-Story Creative Writing | 67.2% | — |
| EQ-Bench Creative Writing | 873 | — |
| WildBench | 79.1% | — |
| EQ-Bench 4 | — | 1110 |
| LiveBench Language | 28.6% | — |
Frequently asked questions
Is GPT-4o mini better than Qwen3.7 Max?
Qwen3.7 Max is the stronger model overall, scoring 51.5 to 25.5 on the Noometry Index. GPT-4o mini costs 14× less per token, which makes it the better buy when Qwen3.7 Max's lead doesn't matter for your workload.
Which is cheaper, GPT-4o mini or Qwen3.7 Max?
GPT-4o mini is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; Qwen3.7 Max lists at $2.50 and $7.50.
Is GPT-4o mini or Qwen3.7 Max better for coding?
Qwen3.7 Max scores higher on coding benchmarks: 50.4 versus 22.0 in the Noometry coding category.
Which has the bigger context window?
Qwen3.7 Max does, with 1M tokens against 128K.
How many benchmarks do GPT-4o mini and Qwen3.7 Max share?
22 benchmarks have published results for both models. GPT-4o mini has 60 scored results on Noometry and Qwen3.7 Max has 33.