o3 vs Qwen3.7 Max
Updated October 21, 2026. 31 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick o3 for coding: it scores 70.7 on the Vibe Coding Index against 45.1 for Qwen3.7 Max.
Pick Qwen3.7 Max when output tokens cost 1.8x less, or when the context window is larger, 1M against 200K tokens.
- Shared results
- 31
- o3 only
- 66
- Qwen3.7 Max only
- 28
- 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
o3
o3 scores 70.7 against 45.1.
2 and 1 benchmarks behind the scores
Intelligence
AA Intelligence Index
Qwen3.7 Max
Qwen3.7 Max leads, 29.5 against 20.2.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Qwen3.7 Max has a AA Agentic Index result, 22.5.
Directional only
Reasoning
GPQA Diamond
Qwen3.7 Max
Qwen3.7 Max leads, 90.2% against 84.1%.
Same board, both measured
Math
AIME
No call
Only o3 has a AIME result, 85.3%.
Directional only
Knowledge
MMLU Pro
Qwen3.7 Max
Qwen3.7 Max leads, 89.3% against 85.6%.
Same board, both measured
Web design
Design Arena: websites
Qwen3.7 Max
Qwen3.7 Max leads, 1274 against 1036.
Same board, both measured
Long documents
Listed context window
Qwen3.7 Max
Qwen3.7 Max takes 1M tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.7 Max
o3 $0.0060, Qwen3.7 Max $0.0037 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
o3
o3 $0.571, Qwen3.7 Max $4.69.
Published attempt cost and pass rate
At a glance
- Coding index
- o3 70.7 from 2 benchmarks, Qwen3.7 Max 45.1 from 1 benchmark.
- Widest category
- Web design, 26.3 points apart on average over 5 shared benchmarks.
- Widest single gap
- Design Arena: all code categories: o3 1021 against Qwen3.7 Max 1275, 31.8 points apart on a 0 to 100 scale.
- Output price
- o3 $8.00 against Qwen3.7 Max $4.425 per million output tokens, 1.8x apart.
- Context window
- o3 takes 200K 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 | o3 | Bars | Qwen3.7 Max | Ties |
|---|---|---|---|---|
| Coding | 0 | 3 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 1 | 6 | ||
| Math | 0 | 2 | ||
| Knowledge | 1 | 5 | ||
| Web design | 0 | 5 | ||
| General | 0 | 5 | ||
| All shared boards | 2 | 29 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index03
| Benchmark | o3 | Qwen3.7 Max | Leader |
|---|---|---|---|
| Aider polyglot | 81.3% | Not measured | Only o3 measured |
| SWE-bench Verified | 58.4% | Not measured | Only o3 measured |
| SWE-bench Multimodal | 36.0% | Not measured | Only o3 measured |
| LMArena WebDev | Not measured | 1515 | Only Qwen3.7 Max measured |
| AA Coding Index | Not measured | 66.0 | Only Qwen3.7 Max measured |
| LiveCodeBench (Vals AI) | 83.9% | 87.1% | Qwen3.7 Max |
| SWE-bench Verified (Vals AI) | Not measured | 68.8% | Only Qwen3.7 Max measured |
| Vibe Code Bench (Vals AI) | Not measured | 47.7% | Only Qwen3.7 Max measured |
| 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) | 80.8% | Not measured | Only o3 measured |
| SciCode | Not measured | 49.5% | Only Qwen3.7 Max measured |
| Terminal-Bench Hard (Artificial Analysis run) | 37.1% | 50.8% | Qwen3.7 Max |
| GSO-Bench | 8.8% | Not measured | Only o3 measured |
| WeirdML | 52.4% | Not measured | Only o3 measured |
| ALE-Bench | 933.5 | 1189.4 | Qwen3.7 Max |
| LMArena Text: coding | 1407 | Not measured | Only o3 measured |
| LMArena Text: software and IT | 1415 | Not measured | Only o3 measured |
| LMArena WebDev: React | Not measured | 1514 | Only Qwen3.7 Max measured |
| LMArena WebDev: HTML | Not measured | 1512 | Only Qwen3.7 Max 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 | o3 | Qwen3.7 Max | Leader |
|---|---|---|---|
| AA Intelligence Index | 20.2 | 29.5 | Qwen3.7 Max |
| Epoch Capabilities Index | 146.7 | 154.0 | Qwen3.7 Max |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | o3 | Qwen3.7 Max | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 22.5 | Only Qwen3.7 Max measured |
| 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 | 80.7% | 94.7% | Qwen3.7 Max |
| METR time horizons | 65.4% | Not measured | Only o3 measured |
| GDPval | 30.8% | Not measured | Only o3 measured |
| DeepResearch Bench | 45.2% | Not measured | Only o3 measured |
| OSWorld | 23.0% | Not measured | Only o3 measured |
| Neither model has a result on the other 21 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index16
| Benchmark | o3 | Qwen3.7 Max | Leader |
|---|---|---|---|
| GPQA Diamond | 84.1% | 90.2% | Qwen3.7 Max |
| MMMU Pro (Vals AI) | 80.4% | Not measured | Only o3 measured |
| GPQA Diamond (Artificial Analysis run) | 82.7% | 92.3% | Qwen3.7 Max |
| Humanity's Last Exam (Artificial Analysis run) | 20.1% | 40.5% | Qwen3.7 Max |
| GPQA Diamond (Epoch AI run) | 81.8% | 90.9% | Qwen3.7 Max |
| ARC-AGI | 60.8% | Not measured | Only o3 measured |
| ARC-AGI-2 | 6.5% | Not measured | Only o3 measured |
| SimpleBench | 53.1% | 70.4% | Qwen3.7 Max |
| Chess puzzles | 37.0% | 26.0% | o3 |
| Mystery game puzzles | 29.0% | 32.0% | Qwen3.7 Max |
| EBR-bench | Not measured | 9.5% | Only Qwen3.7 Max measured |
| VPCT | 52.0% | Not measured | Only o3 measured |
| LMArena Text: hard prompts | 1402 | Not measured | Only o3 measured |
| LMArena Text: expert | 1402 | Not measured | Only o3 measured |
| LMArena Text: hard prompts in English | 1411 | Not measured | Only o3 measured |
| LMArena Vision: diagrams | 1220 | Not measured | Only o3 measured |
| LMArena Vision: homework | 1234 | Not measured | Only o3 measured |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | o3 | Qwen3.7 Max | Leader |
|---|---|---|---|
| AIME | 85.3% | Not measured | Only o3 measured |
| MATH 500 (Vals AI) | 94.6% | Not measured | Only o3 measured |
| MGSM (Vals AI) | 91.8% | Not measured | Only o3 measured |
| AA Math Index | 88.3 | Not measured | Only o3 measured |
| AIME 2025 (Artificial Analysis run) | 88.3% | Not measured | Only o3 measured |
| OTIS Mock AIME | 84.4% | 95.6% | Qwen3.7 Max |
| FrontierMath, tiers 1 to 3 | 33.3% | 64.6% | Qwen3.7 Max |
| FrontierMath, tier 4 | Not measured | 34.1% | Only Qwen3.7 Max measured |
| ProofBench | Not measured | 26.0% | Only Qwen3.7 Max measured |
| MATH level 5 | 97.8% | Not measured | Only o3 measured |
| LMArena Text: math | 1426 | Not measured | Only o3 measured |
| LMArena Text: mathematical work | 1423 | Not measured | Only o3 measured |
| Neither model has a result on the other 1 board here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index15
| Benchmark | o3 | Qwen3.7 Max | Leader |
|---|---|---|---|
| MMLU Pro | 85.6% | 89.3% | Qwen3.7 Max |
| LegalBench (Vals AI) | 83.8% | 84.9% | Qwen3.7 Max |
| CorpFin (Vals AI) | 59.7% | 63.7% | Qwen3.7 Max |
| TaxEval (Vals AI) | 74.6% | 75.3% | Qwen3.7 Max |
| MortgageTax (Vals AI) | 65.7% | Not measured | Only o3 measured |
| MedQA (Vals AI) | 96.1% | Not measured | Only o3 measured |
| MedCode (Vals AI) | 47.3% | 38.8% | o3 |
| MMLU Pro (Artificial Analysis run) | 85.3% | Not measured | Only o3 measured |
| SimpleQA Verified | 49.4% | 55.8% | Qwen3.7 Max |
| GeoBench | 74.0% | Not measured | Only o3 measured |
| LMArena Text: science | 1429 | Not measured | Only o3 measured |
| LMArena Text: medicine and healthcare | 1448 | Not measured | Only o3 measured |
| LMArena Text: legal and government | 1428 | Not measured | Only o3 measured |
| LMArena Text: business and finance | 1393 | Not measured | Only o3 measured |
| Neither model has a result on the other 3 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | o3 | Qwen3.7 Max | Leader |
|---|---|---|---|
| Design Arena: websites | 1036 | 1274 | Qwen3.7 Max |
| Design Arena: UI components | 1016 | 1269 | Qwen3.7 Max |
| Design Arena: game dev | 1034 | 1265 | Qwen3.7 Max |
| Design Arena: data viz | 1200 | 1275 | Qwen3.7 Max |
| Design Arena: 3D | Not measured | 1266 | Only Qwen3.7 Max measured |
| Design Arena: all code categories | 1021 | 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 | o3 | Qwen3.7 Max | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 41.8% | Not measured | Only o3 measured |
| MedScribe (Vals AI) | 76.7% | 79.4% | Qwen3.7 Max |
| IFBench | 71.4% | 80.5% | Qwen3.7 Max |
| Long context reasoning (AA-LCR) | 74.7% | 79.0% | Qwen3.7 Max |
| DTBench | 84.8% | 92.3% | Qwen3.7 Max |
| LMCA | 39.7% | 44.0% | Qwen3.7 Max |
| Fiction.LiveBench | 88.9% | Not measured | Only o3 measured |
| Creative writing (Lech Mazur) | 83.9% | Not measured | Only o3 measured |
| CL-bench | 17.8% | Not measured | Only o3 measured |
| LMArena Text | 1410 | Not measured | Only o3 measured |
| LMArena Text: instruction following | 1368 | Not measured | Only o3 measured |
| LMArena Text: creative writing | 1359 | Not measured | Only o3 measured |
| LMArena Vision | 1214 | Not measured | Only o3 measured |
| LMArena Text: English | 1417 | Not measured | Only o3 measured |
| LMArena Text: non-English | 1401 | Not measured | Only o3 measured |
| LMArena Text: Chinese | 1437 | Not measured | Only o3 measured |
| LMArena Text: German | 1420 | Not measured | Only o3 measured |
| LMArena Text: Japanese | 1403 | Not measured | Only o3 measured |
| LMArena Text: Korean | 1370 | Not measured | Only o3 measured |
| LMArena Text: French | 1430 | Not measured | Only o3 measured |
| LMArena Text: Spanish | 1395 | Not measured | Only o3 measured |
| LMArena Text: Russian | 1406 | Not measured | Only o3 measured |
| LMArena Text: Polish | 1438 | Not measured | Only o3 measured |
| LMArena Text: multi-turn | 1405 | Not measured | Only o3 measured |
| LMArena Text: longer queries | 1372 | Not measured | Only o3 measured |
| LMArena Text: writing and language | 1372 | Not measured | Only o3 measured |
| LMArena Text: entertainment and media | 1373 | Not measured | Only o3 measured |
| LMArena Vision: English | 1221 | Not measured | Only o3 measured |
| LMArena Vision: Chinese | 1237 | Not measured | Only o3 measured |
| LMArena Vision: reading text | 1218 | Not measured | Only o3 measured |
| LMArena Vision: humor | 1222 | Not measured | Only o3 measured |
| LMArena Vision: captioning | 1210 | Not measured | Only o3 measured |
| LMArena Vision: recognizing things | 1234 | Not measured | Only o3 measured |
| LMArena Vision: creative writing | 1201 | Not measured | Only o3 measured |
| Neither model has a result on the other 12 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | o3 | 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 | OpenAI o3 | Qwen3.7 Max |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | No public weights listed |
| Released | April 16, 2025 | May 21, 2026 |
| Context window | 200K | 1M |
| Input $/Mtok | $2.00 | $1.475 |
| Output $/Mtok | $8.00 | $4.425 |
| Coding index | 70.7 | 45.1 |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | $0.571 | $4.69 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is o3 better than Qwen3.7 Max for coding?
Pick o3 for coding: it scores 70.7 on the Vibe Coding Index against 45.1 for Qwen3.7 Max. Pick Qwen3.7 Max when output tokens cost 1.8x less, or when the context window is larger, 1M against 200K tokens.
Which is cheaper, o3 or Qwen3.7 Max?
o3 costs $2.00 per million input tokens and $8.00 per million output tokens; Qwen3.7 Max costs $1.475 and $4.425. Per solved SWE-bench Verified task, o3 costs $0.571 and Qwen3.7 Max $4.69.
Which has the bigger context window, o3 or Qwen3.7 Max?
Qwen3.7 Max, with 1M tokens against 200K.
How many benchmarks is the o3 vs Qwen3.7 Max comparison based on?
31 benchmarks have a result for both models as of October 21, 2026. o3'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.