Gemini 2.5 Pro vs Qwen3.7 Max
Updated October 21, 2026. 30 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick Qwen3.7 Max for coding: it scores 45.1 on the Vibe Coding Index against 37.0 for Gemini 2.5 Pro, though on 1 benchmark against 2.
Pick Gemini 2.5 Pro when a solved task costs $0.64 against $4.69, or when the context window is larger, 1.05M against 1M tokens.
- Shared results
- 30
- Gemini 2.5 Pro only
- 57
- Qwen3.7 Max only
- 29
- Like-for-like categories
- 8 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, four public coding benchmarks
Qwen3.7 Max
Qwen3.7 Max scores 45.1 against 37.0.
2 and 1 benchmarks behind the scores
Intelligence
AA Intelligence Index
Qwen3.7 Max
Qwen3.7 Max leads, 29.5 against 16.1.
Same board, both measured
Agentic work
AA Agentic Index
Qwen3.7 Max
Qwen3.7 Max leads, 22.5 against 1.6.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only Qwen3.7 Max has a GPQA Diamond result, 90.2%.
Directional only
Knowledge
MMLU Pro
No call
Only Qwen3.7 Max has a MMLU Pro result, 89.3%.
Directional only
Web design
Design Arena: websites
Qwen3.7 Max
Qwen3.7 Max leads, 1274 against 1167.
Same board, both measured
Long documents
Listed context window
Gemini 2.5 Pro
Gemini 2.5 Pro takes 1.05M tokens against 1M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.7 Max
Gemini 2.5 Pro $0.0063, Qwen3.7 Max $0.0037 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Gemini 2.5 Pro
Gemini 2.5 Pro $0.64, Qwen3.7 Max $4.69.
Published attempt cost and pass rate
At a glance
- Coding index
- Gemini 2.5 Pro 37.0 from 2 benchmarks, Qwen3.7 Max 45.1 from 1 benchmark.
- Widest category
- Agentic work, 30.8 points apart on average over 2 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): Gemini 2.5 Pro 0.4% against Qwen3.7 Max 47.7%, 47.3 points apart on a 0 to 100 scale.
- Output price
- Gemini 2.5 Pro $10.00 against Qwen3.7 Max $4.425 per million output tokens, 2.3x apart.
- Context window
- Gemini 2.5 Pro takes 1.05M tokens, Qwen3.7 Max 1M.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | Gemini 2.5 Pro | Bars | Qwen3.7 Max | Ties |
|---|---|---|---|---|
| Coding | 0 | 7 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 0 | 2 | ||
| Reasoning | 0 | 3 | ||
| Math | 0 | 3 | ||
| Knowledge | 1 | 1 | ||
| Web design | 0 | 6 | ||
| General | 0 | 5 | ||
| All shared boards | 1 | 29 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index07
| Benchmark | Gemini 2.5 Pro | Qwen3.7 Max | Leader |
|---|---|---|---|
| SWE-bench Verified | 53.6% | Not measured | Only Gemini 2.5 Pro measured |
| LMArena WebDev | 1226 | 1515 | Qwen3.7 Max |
| AA Coding Index | 33.3 | 66.0 | Qwen3.7 Max |
| LiveCodeBench (Vals AI) | Not measured | 87.1% | Only Qwen3.7 Max measured |
| SWE-bench Verified (Vals AI) | 54.4% | 68.8% | Qwen3.7 Max |
| Vibe Code Bench (Vals AI) | 0.4% | 47.7% | Qwen3.7 Max |
| Code migration (Vals AI) | Not measured | 13.1% | Only Qwen3.7 Max measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 61.0% | Only Qwen3.7 Max measured |
| LiveCodeBench (Artificial Analysis run) | 80.1% | Not measured | Only Gemini 2.5 Pro measured |
| SciCode | 46.3% | 49.5% | Qwen3.7 Max |
| Terminal-Bench Hard (Artificial Analysis run) | 26.5% | 50.8% | Qwen3.7 Max |
| Terminal-Bench | 32.6% | Not measured | Only Gemini 2.5 Pro measured |
| GSO-Bench | 3.9% | Not measured | Only Gemini 2.5 Pro measured |
| WeirdML | 54.0% | Not measured | Only Gemini 2.5 Pro measured |
| ALE-Bench | Not measured | 1189.4 | Only Qwen3.7 Max measured |
| LMArena Text: coding | 1452 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: software and IT | 1459 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena WebDev: React | Not measured | 1514 | Only Qwen3.7 Max measured |
| LMArena WebDev: HTML | 1235 | 1512 | Qwen3.7 Max |
| LMArena WebDev: from an image | 1285 | Not measured | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 18 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Gemini 2.5 Pro | Qwen3.7 Max | Leader |
|---|---|---|---|
| AA Intelligence Index | 16.1 | 29.5 | Qwen3.7 Max |
| Epoch Capabilities Index | 145.1 | 154.0 | Qwen3.7 Max |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Gemini 2.5 Pro | Qwen3.7 Max | Leader |
|---|---|---|---|
| AA Agentic Index | 1.6 | 22.5 | Qwen3.7 Max |
| Design Arena: agents building web apps | Not measured | 1208 | Only Qwen3.7 Max measured |
| Design Arena: agents building full-stack apps | Not measured | 1191 | Only Qwen3.7 Max measured |
| Design Arena: agents building mobile apps | Not measured | 1153 | Only Qwen3.7 Max measured |
| Design Arena: agents building Android apps | Not measured | 1165 | Only Qwen3.7 Max measured |
| Design Arena: agents building Godot games | Not measured | 1187 | Only Qwen3.7 Max measured |
| Design Arena: agents building games | Not measured | 1161 | Only Qwen3.7 Max measured |
| Design Arena: HTML slides | Not measured | 1158 | Only Qwen3.7 Max measured |
| Design Arena: PowerPoint slides | Not measured | 1196 | Only Qwen3.7 Max measured |
| Finance Agent (Vals AI) | Not measured | 47.8% | Only Qwen3.7 Max measured |
| Tax Agent Bench (Vals AI) | Not measured | 15.8% | Only Qwen3.7 Max measured |
| Legal Research Bench (Vals AI) | Not measured | 25.5% | Only Qwen3.7 Max measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 57.0% | Only Qwen3.7 Max measured |
| τ²-Bench Telecom | 54.1% | 94.7% | Qwen3.7 Max |
| GDPval | 23.3% | Not measured | Only Gemini 2.5 Pro measured |
| DeepResearch Bench | 41.5% | Not measured | Only Gemini 2.5 Pro measured |
| Vending-Bench 2 | $574 | Not measured | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 22 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Gemini 2.5 Pro | Qwen3.7 Max | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 90.2% | Only Qwen3.7 Max measured |
| GPQA Diamond (Artificial Analysis run) | 84.4% | 92.3% | Qwen3.7 Max |
| Humanity's Last Exam (Artificial Analysis run) | 22.5% | 40.5% | Qwen3.7 Max |
| GPQA Diamond (Epoch AI run) | 85.3% | 90.9% | Qwen3.7 Max |
| ARC-AGI | 41.0% | Not measured | Only Gemini 2.5 Pro measured |
| ARC-AGI-2 | 4.9% | Not measured | Only Gemini 2.5 Pro measured |
| SimpleBench | Not measured | 70.4% | Only Qwen3.7 Max measured |
| Chess puzzles | Not measured | 26.0% | Only Qwen3.7 Max measured |
| Mystery game puzzles | Not measured | 32.0% | Only Qwen3.7 Max measured |
| EBR-bench | Not measured | 9.5% | Only Qwen3.7 Max measured |
| LMArena Text: hard prompts | 1455 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: expert | 1452 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: hard prompts in English | 1453 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: diagrams | 1274 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: homework | 1281 | Not measured | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 6 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Gemini 2.5 Pro | Qwen3.7 Max | Leader |
|---|---|---|---|
| AA Math Index | 87.7 | Not measured | Only Gemini 2.5 Pro measured |
| AIME 2025 (Artificial Analysis run) | 87.7% | Not measured | Only Gemini 2.5 Pro measured |
| OTIS Mock AIME | 84.7% | 95.6% | Qwen3.7 Max |
| FrontierMath, tiers 1 to 3 | 24.6% | 64.6% | Qwen3.7 Max |
| FrontierMath, tier 4 | 0.0% | 34.1% | Qwen3.7 Max |
| ProofBench | Not measured | 26.0% | Only Qwen3.7 Max measured |
| LMArena Text: math | 1450 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: mathematical work | 1454 | Not measured | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | Gemini 2.5 Pro | Qwen3.7 Max | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 89.3% | Only Qwen3.7 Max measured |
| LegalBench (Vals AI) | Not measured | 84.9% | Only Qwen3.7 Max measured |
| CaseLaw (Vals AI) | 63.9% | Not measured | Only Gemini 2.5 Pro measured |
| CorpFin (Vals AI) | 60.8% | 63.7% | Qwen3.7 Max |
| TaxEval (Vals AI) | Not measured | 75.3% | Only Qwen3.7 Max measured |
| MortgageTax (Vals AI) | 68.9% | Not measured | Only Gemini 2.5 Pro measured |
| MedCode (Vals AI) | 50.6% | 38.8% | Gemini 2.5 Pro |
| MMLU Pro (Artificial Analysis run) | 86.2% | Not measured | Only Gemini 2.5 Pro measured |
| SimpleQA Verified | Not measured | 55.8% | Only Qwen3.7 Max measured |
| LMArena Text: science | 1480 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: medicine and healthcare | 1476 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: legal and government | 1480 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: business and finance | 1439 | Not measured | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 4 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | Gemini 2.5 Pro | Qwen3.7 Max | Leader |
|---|---|---|---|
| Design Arena: websites | 1167 | 1274 | Qwen3.7 Max |
| Design Arena: UI components | 1139 | 1269 | Qwen3.7 Max |
| Design Arena: game dev | 1112 | 1265 | Qwen3.7 Max |
| Design Arena: data viz | 1222 | 1275 | Qwen3.7 Max |
| Design Arena: 3D | 1080 | 1266 | Qwen3.7 Max |
| Design Arena: all code categories | 1152 | 1275 | Qwen3.7 Max |
| Design Arena: SVG | Not measured | 1216 | Only Qwen3.7 Max measured |
| Design Arena: ASCII art | Not measured | 1211 | Only Qwen3.7 Max measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | Gemini 2.5 Pro | Qwen3.7 Max | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 41.9% | Not measured | Only Gemini 2.5 Pro measured |
| MedScribe (Vals AI) | 73.5% | 79.4% | Qwen3.7 Max |
| IFBench | 48.7% | 80.5% | Qwen3.7 Max |
| Long context reasoning (AA-LCR) | 69.0% | 79.0% | Qwen3.7 Max |
| DTBench | 82.4% | 92.3% | Qwen3.7 Max |
| LMCA | 34.8% | 44.0% | Qwen3.7 Max |
| Creative writing (Lech Mazur) | 83.8% | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text | 1458 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: instruction following | 1437 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: creative writing | 1454 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision | 1263 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: English | 1458 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: non-English | 1452 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: Chinese | 1507 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: German | 1487 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: Japanese | 1461 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: Korean | 1434 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: French | 1473 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: Spanish | 1474 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: Russian | 1461 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: Polish | 1479 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: multi-turn | 1453 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: longer queries | 1449 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: writing and language | 1454 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: entertainment and media | 1441 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: English | 1261 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: Chinese | 1300 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: reading text | 1270 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: humor | 1262 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: captioning | 1304 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: recognizing things | 1283 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: creative writing | 1265 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Document | 1421 | Not measured | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 13 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemini 2.5 Pro | Qwen3.7 Max | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Qwen3.7 Max costs less
Repository review
50K input + 3K output tokens
Qwen3.7 Max costs less
Agent loop
200K input + 10K output tokens
Qwen3.7 Max costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | Google Gemini 2.5 Pro | Qwen3.7 Max |
|---|---|---|
| Maker | Qwen | |
| Weights | No public weights listed | No public weights listed |
| Released | June 17, 2025 | May 21, 2026 |
| Context window | 1.05M | 1M |
| Input $/Mtok | $1.25 | $1.475 |
| Output $/Mtok | $10.00 | $4.425 |
| Coding index | 37.0 | 45.1 |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | $0.64 | $4.69 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Gemini 2.5 Pro better than Qwen3.7 Max for coding?
Pick Qwen3.7 Max for coding: it scores 45.1 on the Vibe Coding Index against 37.0 for Gemini 2.5 Pro, though on 1 benchmark against 2. Pick Gemini 2.5 Pro when a solved task costs $0.64 against $4.69, or when the context window is larger, 1.05M against 1M tokens.
Which is cheaper, Gemini 2.5 Pro or Qwen3.7 Max?
Gemini 2.5 Pro costs $1.25 per million input tokens and $10.00 per million output tokens; Qwen3.7 Max costs $1.475 and $4.425. Per solved SWE-bench Verified task, Gemini 2.5 Pro costs $0.64 and Qwen3.7 Max $4.69.
Which has the bigger context window, Gemini 2.5 Pro or Qwen3.7 Max?
Gemini 2.5 Pro, with 1.05M tokens against 1M.
How many benchmarks is the Gemini 2.5 Pro vs Qwen3.7 Max comparison based on?
30 benchmarks have a result for both models as of October 21, 2026. Gemini 2.5 Pro's coding index rests on 2 independent benchmarks and Qwen3.7 Max's on 1. Where only one model has a result, the page says so instead of naming a winner.