Model comparison
GPT-4 Turbo vs Qwen3.7 Max
Qwen3.7 Max is the stronger model overall, scoring 51.5 to 30.5 on the Noometry Index.
Last verified . 20 shared benchmarks.
Alibaba (Qwen)Summary
- They share 20 benchmarks with published results for both. GPT-4 Turbo scores higher in 0 categories and Qwen3.7 Max in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3.7 Max leads 62.4 to 9.0.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 6.7% for GPT-4 Turbo and 95.6% for Qwen3.7 Max.
- Qwen3.7 Max is cheaper at $2.50 / $7.50 per million input/output tokens, against $10 / $30 for GPT-4 Turbo.
- Qwen3.7 Max accepts more context: 1M tokens versus 128K.
Side by side
| GPT-4 Turbo | Qwen3.7 Max | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 30.5 | 51.5 |
| Released | 2023-11-06 | 2026-05-19 |
| Weights | Proprietary | Proprietary |
| Context window | 128K | 1M |
| Max output | 4K | 131K |
| Input $ / M tokens | $10 | $2.50 |
| Output $ / M tokens | $30 | $7.50 |
| Results tracked | 36 | 33 |
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Category by category
Coding Qwen3.7 Max leads
GPT-4 Turbo: 33.8 (#249), Qwen3.7 Max: 50.4 (#45)
| Benchmark | GPT-4 Turbo | Qwen3.7 Max |
|---|---|---|
| LMArena Coding | 1268 | 1498 |
| SWE-bench Verified | — | 77.3% |
| LMArena WebDev | — | 1515 |
| SciCode | — | 48.8% |
| WeirdML | 18% | — |
| BigCodeBench Instruct | 48.2% | — |
| BigCodeBench Complete | 58.2% | — |
| ALE-Bench | — | 1,189 |
| HumanEval+ | 86.6% | — |
| MBPP+ | 73.3% | — |
Agentic & Tool Use Not comparable
GPT-4 Turbo: —, Qwen3.7 Max: 22.1 (#135)
| Benchmark | GPT-4 Turbo | Qwen3.7 Max |
|---|---|---|
| GBAEval | — | 0.4% |
| METR Time Horizons | 36.7% | — |
Reasoning Qwen3.7 Max leads
GPT-4 Turbo: 15.3 (#317), Qwen3.7 Max: 49.2 (#38)
| Benchmark | GPT-4 Turbo | Qwen3.7 Max |
|---|---|---|
| SimpleBench | 25.1% | 70.4% |
| Chess Puzzles | 6% | 19% |
| LMArena Hard Prompts | 1251 | 1483 |
| DTBench | 61.6% | 92.3% |
| LMCA | 9.8% | 44% |
| Epoch Capabilities Index | 127.25 | 153.68 |
| NYT Connections (extended) | — | 85.1% |
| CritPt | — | 13.4% |
| EBR-Bench | — | 9.5% |
| Mystery Game Puzzles | — | 32% |
| ForecastBench | 59.4 | — |
Math Qwen3.7 Max leads
GPT-4 Turbo: 9.0 (#322), Qwen3.7 Max: 62.4 (#32)
| Benchmark | GPT-4 Turbo | Qwen3.7 Max |
|---|---|---|
| FrontierMath (Tiers 1-3) | 0.7% | 64.6% |
| OTIS Mock AIME 2024-2025 | 6.7% | 95.6% |
| LMArena Math | 1272 | 1490 |
| FrontierMath Tier 4 | — | 34.1% |
| ProofBench | — | 26% |
| MATH Level 5 | 46.7% | — |
Knowledge Qwen3.7 Max leads
GPT-4 Turbo: 24.3 (#268), Qwen3.7 Max: 61.6 (#28)
| Benchmark | GPT-4 Turbo | Qwen3.7 Max |
|---|---|---|
| GPQA Diamond | 46.6% | 90.9% |
| LMArena Expert | 1223 | 1488 |
| SimpleQA Verified | — | 55.8% |
| Confabulations | 28.4% | — |
| MMLU | 81.3% | — |
Multimodal Not comparable
GPT-4 Turbo: 30.6 (#110), Qwen3.7 Max: —
| Benchmark | GPT-4 Turbo | Qwen3.7 Max |
|---|---|---|
| LMArena Vision | 1090 | — |
Multilingual Qwen3.7 Max leads
GPT-4 Turbo: 40.5 (#216), Qwen3.7 Max: 56.9 (#15)
| Benchmark | GPT-4 Turbo | Qwen3.7 Max |
|---|---|---|
| LMArena Non-English | 1245 | 1474 |
| LMArena Chinese | 1242 | 1530 |
| LMArena Russian | 1259 | 1484 |
| LMArena French | 1276 | — |
| LMArena German | 1259 | — |
| LMArena Japanese | 1194 | — |
| LMArena Korean | 1187 | — |
| LMArena Spanish | 1260 | — |
Instruction Following Qwen3.7 Max leads
GPT-4 Turbo: 65.8 (#216), Qwen3.7 Max: 76.7 (#38)
| Benchmark | GPT-4 Turbo | Qwen3.7 Max |
|---|---|---|
| LMArena Instruction Following | 1249 | 1460 |
Long Context Qwen3.7 Max leads
GPT-4 Turbo: 38.0 (#206), Qwen3.7 Max: 45.4 (#40)
| Benchmark | GPT-4 Turbo | Qwen3.7 Max |
|---|---|---|
| LMArena Longer Query | 1254 | 1482 |
Writing & Preference Qwen3.7 Max leads
GPT-4 Turbo: 47.7 (#206), Qwen3.7 Max: 65.0 (#54)
| Benchmark | GPT-4 Turbo | Qwen3.7 Max |
|---|---|---|
| LMArena Text | 1272 | 1476 |
| LMArena Creative Writing | 1269 | 1449 |
| LMArena Multi-Turn | 1267 | 1481 |
| EQ-Bench 4 | — | 1110 |
Frequently asked questions
Is GPT-4 Turbo better than Qwen3.7 Max?
Qwen3.7 Max is the stronger model overall, scoring 51.5 to 30.5 on the Noometry Index.
Which is cheaper, GPT-4 Turbo or Qwen3.7 Max?
Qwen3.7 Max is cheaper. It lists at $2.50 per million input tokens and $7.50 per million output tokens; GPT-4 Turbo lists at $10 and $30.
Is GPT-4 Turbo or Qwen3.7 Max better for coding?
Qwen3.7 Max scores higher on coding benchmarks: 50.4 versus 33.8 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-4 Turbo and Qwen3.7 Max share?
20 benchmarks have published results for both models. GPT-4 Turbo has 36 scored results on Noometry and Qwen3.7 Max has 33.