Model comparison
Gemini 2.5 Pro vs Qwen3.7 Max
Qwen3.7 Max is the stronger model overall, scoring 51.5 to 45.0 on the Noometry Index.
Last verified . 26 shared benchmarks.
Summary
- They share 26 benchmarks with published results for both. Gemini 2.5 Pro scores higher in 2 categories and Qwen3.7 Max in 7 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3.7 Max leads 62.4 to 32.5.
- The biggest single-benchmark swing is FrontierMath (Tiers 1-3): 24.6% for Gemini 2.5 Pro and 64.6% for Qwen3.7 Max.
- Gemini 2.5 Pro is cheaper at $1.25 / $10 per million input/output tokens, against $2.50 / $7.50 for Qwen3.7 Max.
- Gemini 2.5 Pro accepts more context: 1.05M tokens versus 1M.
Side by side
| Gemini 2.5 Pro | Qwen3.7 Max | |
|---|---|---|
| Provider | Alibaba (Qwen) | |
| Noometry Index | 45.0 | 51.5 |
| Released | 2025-03-25 | 2026-05-19 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 1M |
| Max output | 66K | 131K |
| Input $ / M tokens | $1.25 | $2.50 |
| Output $ / M tokens | $10 | $7.50 |
| Results tracked | 78 | 33 |
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Category by category
Coding Qwen3.7 Max leads
Gemini 2.5 Pro: 42.4 (#101), Qwen3.7 Max: 50.4 (#45)
| Benchmark | Gemini 2.5 Pro | Qwen3.7 Max |
|---|---|---|
| SWE-bench Verified | 57.6% | 77.3% |
| LMArena WebDev | 1227 | 1515 |
| SciCode | 42.8% | 48.8% |
| LMArena Coding | 1452 | 1498 |
| ALE-Bench | 785.52 | 1,189 |
| SWE-bench Verified (bash only) | 53.6% | — |
| Aider Polyglot | 83.1% | — |
| GSO | 3.9% | — |
| WeirdML | 54% | — |
| LiveBench Coding | 85.9% | — |
| CadEval | 64% | — |
| AlgoTune | 1.51 | — |
Agentic & Tool Use Gemini 2.5 Pro leads
Gemini 2.5 Pro: 29.2 (#88), Qwen3.7 Max: 22.1 (#135)
| Benchmark | Gemini 2.5 Pro | Qwen3.7 Max |
|---|---|---|
| Terminal-Bench | 32.6% | — |
| GDPval | 23.3% | — |
| Remote Labor Index | 0.8% | — |
| TheAgentCompany | 30.3% | — |
| τ²-bench Banking | 13.7% | — |
| DeepResearch Bench | 42.8% | — |
| BALROG | 43.3% | — |
| GBAEval | — | 0.4% |
| LMArena Search | 1142 | — |
| METR Time Horizons | 55.4% | — |
| Vending-Bench 2 | 573.64 | — |
Reasoning Qwen3.7 Max leads
Gemini 2.5 Pro: 28.8 (#99), Qwen3.7 Max: 49.2 (#38)
| Benchmark | Gemini 2.5 Pro | Qwen3.7 Max |
|---|---|---|
| SimpleBench | 62.4% | 70.4% |
| CritPt | 2% | 13.4% |
| Chess Puzzles | 20% | 19% |
| LMArena Hard Prompts | 1455 | 1483 |
| DTBench | 82.4% | 92.3% |
| LMCA | 34.8% | 44% |
| Epoch Capabilities Index | 145.32 | 153.68 |
| ARC-AGI-2 | 4.9% | — |
| Kagi LLM Benchmark | 70.3% | — |
| NYT Connections (extended) | — | 85.1% |
| ARC-AGI-1 | 41% | — |
| EnigmaEval | 5.6% | — |
| EBR-Bench | — | 9.5% |
| LiveBench Reasoning | 89.8% | — |
| Mystery Game Puzzles | — | 32% |
| LiveBench Data Analysis | 79.9% | — |
| ForecastBench | 61.3 | — |
| LiveBench | 82.3% | — |
Math Qwen3.7 Max leads
Gemini 2.5 Pro: 32.5 (#213), Qwen3.7 Max: 62.4 (#32)
| Benchmark | Gemini 2.5 Pro | Qwen3.7 Max |
|---|---|---|
| FrontierMath (Tiers 1-3) | 24.6% | 64.6% |
| FrontierMath Tier 4 | 0% | 34.1% |
| OTIS Mock AIME 2024-2025 | 84.7% | 95.6% |
| LMArena Math | 1450 | 1490 |
| ProofBench | — | 26% |
| Omni-MATH | 41.6% | — |
| LiveBench Math | 90.2% | — |
| MATH Level 5 | 95.9% | — |
| FrontierMath (Feb 2025 set) | 14.1% | — |
| FrontierMath Tier 4 (v1) | 4.2% | — |
Knowledge Qwen3.7 Max leads
Gemini 2.5 Pro: 56.0 (#46), Qwen3.7 Max: 61.6 (#28)
| Benchmark | Gemini 2.5 Pro | Qwen3.7 Max |
|---|---|---|
| GPQA Diamond | 85.3% | 90.9% |
| LMArena Expert | 1452 | 1488 |
| Humanity's Last Exam | 21.6% | — |
| SimpleQA Verified | — | 55.8% |
| MMLU-Pro | 86.3% | — |
| Confabulations | 10.6% | — |
| Vectara Hallucination Rate | 7% | — |
| GPQA (HELM) | 74.9% | — |
Multimodal Not comparable
Gemini 2.5 Pro: 45.2 (#18), Qwen3.7 Max: —
| Benchmark | Gemini 2.5 Pro | Qwen3.7 Max |
|---|---|---|
| LMArena Vision | 1263 | — |
| GeoBench | 86% | — |
| VPCT | 48% | — |
| LMArena Document | 1421 | — |
| SpatialViz-Bench | 44.7% | — |
Multilingual Qwen3.7 Max leads
Gemini 2.5 Pro: 55.3 (#31), Qwen3.7 Max: 56.9 (#15)
| Benchmark | Gemini 2.5 Pro | Qwen3.7 Max |
|---|---|---|
| LMArena Non-English | 1451 | 1474 |
| LMArena Chinese | 1507 | 1530 |
| LMArena Russian | 1461 | 1484 |
| LMArena French | 1472 | — |
| LMArena German | 1487 | — |
| LMArena Japanese | 1461 | — |
| LMArena Korean | 1434 | — |
| LMArena Spanish | 1473 | — |
Instruction Following Qwen3.7 Max leads
Gemini 2.5 Pro: 75.0 (#75), Qwen3.7 Max: 76.7 (#38)
| Benchmark | Gemini 2.5 Pro | Qwen3.7 Max |
|---|---|---|
| LMArena Instruction Following | 1437 | 1460 |
| LiveBench Instruction Following | 80.6% | — |
| IFEval | 84% | — |
Long Context Gemini 2.5 Pro leads
Gemini 2.5 Pro: 59.8 (#5), Qwen3.7 Max: 45.4 (#40)
| Benchmark | Gemini 2.5 Pro | Qwen3.7 Max |
|---|---|---|
| LMArena Longer Query | 1449 | 1482 |
| Fiction.LiveBench | 91.7% | — |
Writing & Preference Qwen3.7 Max leads
Gemini 2.5 Pro: 63.7 (#62), Qwen3.7 Max: 65.0 (#54)
| Benchmark | Gemini 2.5 Pro | Qwen3.7 Max |
|---|---|---|
| LMArena Text | 1458 | 1476 |
| LMArena Creative Writing | 1454 | 1449 |
| LMArena Multi-Turn | 1453 | 1481 |
| Short-Story Creative Writing | 83.8% | — |
| EQ-Bench Creative Writing | 1421 | — |
| WildBench | 85.7% | — |
| EQ-Bench 4 | — | 1110 |
| LiveBench Language | 67.8% | — |
Frequently asked questions
Is Gemini 2.5 Pro better than Qwen3.7 Max?
Qwen3.7 Max is the stronger model overall, scoring 51.5 to 45.0 on the Noometry Index.
Which is cheaper, Gemini 2.5 Pro or Qwen3.7 Max?
Gemini 2.5 Pro is cheaper. It lists at $1.25 per million input tokens and $10 per million output tokens; Qwen3.7 Max lists at $2.50 and $7.50.
Is Gemini 2.5 Pro or Qwen3.7 Max better for coding?
Qwen3.7 Max scores higher on coding benchmarks: 50.4 versus 42.4 in the Noometry coding category.
Which has the bigger context window?
Gemini 2.5 Pro does, with 1.05M tokens against 1M.
How many benchmarks do Gemini 2.5 Pro and Qwen3.7 Max share?
26 benchmarks have published results for both models. Gemini 2.5 Pro has 78 scored results on Noometry and Qwen3.7 Max has 33.