GPT-5.6 Terra vs Qwen3.7 Max
GPT-5.6 Terra leads Qwen3.7 Max on coding, 63.9 to 54.1 on the Vibe Coding Index, and Qwen3.7 Max costs 2.7x less per output token ($4.425 vs $12.00 per million). Updated October 22, 2026; 58 benchmarks have a result for both.
GPT-5.6 Terra or Qwen3.7 Max: which to pick
Pick GPT-5.6 Terra for coding: it scores 63.9 on the Vibe Coding Index against 54.1 for Qwen3.7 Max.
Pick Qwen3.7 Max for output tokens 2.7x cheaper ($4.425 vs $12.00 per million) and work measured by LiveCodeBench (Vals AI) or LMArena WebDev: React, where it leads.
- Shared results
- 58
- GPT-5.6 Terra only
- 71
- Qwen3.7 Max only
- 2
- Like-for-like categories
- 8 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
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, six public coding benchmarks
GPT-5.6 Terra
GPT-5.6 Terra scores 63.9 against 54.1.
6 and 3 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-5.6 Terra
GPT-5.6 Terra leads, 42.1 against 29.5.
Same board, both measured
Agentic work
AA Agentic Index
GPT-5.6 Terra
GPT-5.6 Terra leads, 43.2 against 22.5.
Same board, both measured
Reasoning
GPQA Diamond
GPT-5.6 Terra
GPT-5.6 Terra leads, 90.9% against 90.2%.
Same board, both measured
Knowledge
MMLU Pro
Qwen3.7 Max
Qwen3.7 Max leads, 89.3% against 86.7%.
Same board, both measured
Web design
Design Arena: websites
Qwen3.7 Max
Qwen3.7 Max leads, 1274 against 1268.
Same board, both measured
Long documents
Listed context window
GPT-5.6 Terra
GPT-5.6 Terra takes 1.05M tokens against 1M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.7 Max
GPT-5.6 Terra $0.0080, Qwen3.7 Max $0.0037 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5.6 Terra
GPT-5.6 Terra $0.42, Qwen3.7 Max $4.69.
Published attempt cost and pass rate
At a glance
- Coding index
- GPT-5.6 Terra 63.9 from 6 benchmarks, Qwen3.7 Max 54.1 from 3 benchmarks.
- Widest category
- Math, 27.5 points apart on average over 4 shared benchmarks.
- Widest single gap
- ProofBench: GPT-5.6 Terra 74.0% against Qwen3.7 Max 26.0%, 48.0 points apart on a 0 to 100 scale.
- Output price
- GPT-5.6 Terra $12.00 against Qwen3.7 Max $4.425 per million output tokens, 2.7x apart.
- Context window
- GPT-5.6 Terra 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 | GPT-5.6 Terra | Bars | Qwen3.7 Max | Ties |
|---|---|---|---|---|
| Coding | 10 | 2 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 5 | 8 | ||
| Reasoning | 6 | 1 | ||
| Math | 4 | 0 | ||
| Knowledge | 4 | 2 | ||
| Web design | 1 | 7 | ||
| General | 5 | 1 | ||
| All shared boards | 37 | 21 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index102
| Benchmark | GPT-5.6 Terra | Qwen3.7 Max | Leader |
|---|---|---|---|
| LiveBench coding | 86.2% | Not measured | Only GPT-5.6 Terra measured |
| LMArena WebDev | 1522 | 1515 | GPT-5.6 Terra |
| AA Coding Index | 76.7 | 66.0 | GPT-5.6 Terra |
| LiveCodeBench (Vals AI) | 85.9% | 87.1% | Qwen3.7 Max |
| SWE-bench Verified (Vals AI) | 95.4% | 68.8% | GPT-5.6 Terra |
| Terminal-Bench 4 (Vals AI) | 22.7% | Not measured | Only GPT-5.6 Terra measured |
| Vibe Code Bench (Vals AI) | 74.6% | 47.7% | GPT-5.6 Terra |
| Code migration (Vals AI) | 47.8% | 13.1% | GPT-5.6 Terra |
| ProgramBench (Vals AI) | 0.5% | Not measured | Only GPT-5.6 Terra measured |
| IOI (Vals AI) | 87.6% | Not measured | Only GPT-5.6 Terra measured |
| Terminal-Bench 2.1 (Vals AI) | 77.5% | 61.0% | GPT-5.6 Terra |
| Vibe Code Bench 1-100 (Vals AI) | 14.8% | Not measured | Only GPT-5.6 Terra measured |
| SkillsBench (Vals AI) | 58.9% | Not measured | Only GPT-5.6 Terra measured |
| CyberBench (Vals AI) | 72.1% | Not measured | Only GPT-5.6 Terra measured |
| SciCode | 55.0% | 49.5% | GPT-5.6 Terra |
| Terminal-Bench Hard (Artificial Analysis run) | 62.9% | 50.8% | GPT-5.6 Terra |
| FrontierCode | 41.3% | Not measured | Only GPT-5.6 Terra measured |
| WeirdML | 78.3% | Not measured | Only GPT-5.6 Terra measured |
| ALE-Bench | 1951.4 | 1189.4 | GPT-5.6 Terra |
| LMArena Text: coding | 1485 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: software and IT | 1482 | Not measured | Only GPT-5.6 Terra measured |
| LMArena WebDev: React | 1513 | 1514 | Qwen3.7 Max |
| LMArena WebDev: HTML | 1526 | 1512 | GPT-5.6 Terra |
| LMArena WebDev: from an image | 1541 | Not measured | Only GPT-5.6 Terra measured |
| SciCode | 10.8% | Not measured | Only GPT-5.6 Terra measured |
| SWE-Bench Pro V2 | 92.4% | Not measured | Only GPT-5.6 Terra measured |
| Neither model has a result on the other 17 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.6 Terra | Qwen3.7 Max | Leader |
|---|---|---|---|
| AA Intelligence Index | 42.1 | 29.5 | GPT-5.6 Terra |
| Epoch Capabilities Index | 160.1 | 154.0 | GPT-5.6 Terra |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index58
| Benchmark | GPT-5.6 Terra | Qwen3.7 Max | Leader |
|---|---|---|---|
| AA Agentic Index | 43.2 | 22.5 | GPT-5.6 Terra |
| Design Arena: agents building web apps | 1120 | 1208 | Qwen3.7 Max |
| Design Arena: agents building full-stack apps | 1061 | 1191 | Qwen3.7 Max |
| Design Arena: agents building mobile apps | 1110 | 1154 | Qwen3.7 Max |
| Design Arena: agents building Android apps | 1060 | 1159 | Qwen3.7 Max |
| Design Arena: agents building Godot games | Not measured | 1187 | Only Qwen3.7 Max measured |
| Design Arena: agents building games | 1083 | 1161 | Qwen3.7 Max |
| Design Arena: HTML slides | 1047 | 1098 | Qwen3.7 Max |
| Design Arena: PowerPoint slides | 1104 | 1169 | Qwen3.7 Max |
| LiveBench research | 60.4% | Not measured | Only GPT-5.6 Terra measured |
| LiveBench browser | 84.0% | Not measured | Only GPT-5.6 Terra measured |
| LiveBench long agentic | 48.4% | Not measured | Only GPT-5.6 Terra measured |
| SREBench (Vals AI) | 9.5% | Not measured | Only GPT-5.6 Terra measured |
| Finance Agent (Vals AI) | 54.4% | 47.8% | GPT-5.6 Terra |
| Tax Agent Bench (Vals AI) | 30.0% | 15.8% | GPT-5.6 Terra |
| Legal Research Bench (Vals AI) | 41.4% | 25.5% | GPT-5.6 Terra |
| Excel Modeling Benchmark (Vals AI) | 66.2% | 57.0% | GPT-5.6 Terra |
| Terminal-Bench-Science (Vals AI) | 10.0% | Not measured | Only GPT-5.6 Terra measured |
| τ²-Bench Telecom | 86.3% | 94.7% | Qwen3.7 Max |
| APEX-Agents | 58.2% | Not measured | Only GPT-5.6 Terra measured |
| BALROG | 53.2% | Not measured | Only GPT-5.6 Terra measured |
| Vending-Bench 2 | $7,343 | Not measured | Only GPT-5.6 Terra measured |
| Neither model has a result on the other 20 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index61
| Benchmark | GPT-5.6 Terra | Qwen3.7 Max | Leader |
|---|---|---|---|
| GPQA Diamond | 90.9% | 90.2% | GPT-5.6 Terra |
| LiveBench reasoning | 90.6% | Not measured | Only GPT-5.6 Terra measured |
| MMMU Pro (Vals AI) | 86.5% | Not measured | Only GPT-5.6 Terra measured |
| GPQA Diamond (Artificial Analysis run) | 92.5% | 92.3% | GPT-5.6 Terra |
| Humanity's Last Exam (Artificial Analysis run) | 42.9% | 40.5% | GPT-5.6 Terra |
| GPQA Diamond (Epoch AI run) | 93.3% | 90.9% | GPT-5.6 Terra |
| ARC-AGI | 96.5% | Not measured | Only GPT-5.6 Terra measured |
| ARC-AGI-2 | 83.9% | Not measured | Only GPT-5.6 Terra measured |
| SimpleBench | 48.9% | 70.4% | Qwen3.7 Max |
| Chess puzzles | 47.0% | 26.0% | GPT-5.6 Terra |
| Mystery game puzzles | 35.0% | 32.0% | GPT-5.6 Terra |
| EBR-bench | Not measured | 9.5% | Only Qwen3.7 Max measured |
| LMArena Text: hard prompts | 1469 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: expert | 1496 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: hard prompts in English | 1465 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Vision: diagrams | 1295 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Vision: homework | 1312 | Not measured | Only GPT-5.6 Terra measured |
| Kagi LLM Benchmark | 51.3% | Not measured | Only GPT-5.6 Terra measured |
| Neither model has a result on the other 5 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GPT-5.6 Terra | Qwen3.7 Max | Leader |
|---|---|---|---|
| OTIS Mock AIME | 99.7% | 95.6% | GPT-5.6 Terra |
| FrontierMath, tiers 1 to 3 | 86.0% | 64.6% | GPT-5.6 Terra |
| FrontierMath, tier 4 | 70.7% | 34.1% | GPT-5.6 Terra |
| ProofBench | 74.0% | 26.0% | GPT-5.6 Terra |
| LMArena Text: math | 1465 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: mathematical work | 1478 | Not measured | Only GPT-5.6 Terra measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index42
| Benchmark | GPT-5.6 Terra | Qwen3.7 Max | Leader |
|---|---|---|---|
| MMLU Pro | 86.7% | 89.3% | Qwen3.7 Max |
| LegalBench (Vals AI) | 85.1% | 84.9% | GPT-5.6 Terra |
| CorpFin (Vals AI) | 65.3% | 63.7% | GPT-5.6 Terra |
| TaxEval (Vals AI) | 76.2% | 75.3% | GPT-5.6 Terra |
| MortgageTax (Vals AI) | 67.3% | Not measured | Only GPT-5.6 Terra measured |
| MedCode (Vals AI) | 43.4% | 38.8% | GPT-5.6 Terra |
| SimpleQA Verified | 43.2% | 55.8% | Qwen3.7 Max |
| LMArena Text: science | 1452 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: medicine and healthcare | 1430 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: legal and government | 1461 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: business and finance | 1448 | Not measured | Only GPT-5.6 Terra measured |
| FACTS Benchmark Suite | 52.0% | Not measured | Only GPT-5.6 Terra measured |
| Neither model has a result on the other 8 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index17
| Benchmark | GPT-5.6 Terra | Qwen3.7 Max | Leader |
|---|---|---|---|
| Design Arena: websites | 1268 | 1274 | Qwen3.7 Max |
| Design Arena: UI components | 1256 | 1267 | Qwen3.7 Max |
| Design Arena: game dev | 1229 | 1265 | Qwen3.7 Max |
| Design Arena: data viz | 1240 | 1276 | Qwen3.7 Max |
| Design Arena: 3D | 1134 | 1265 | Qwen3.7 Max |
| Design Arena: all code categories | 1240 | 1275 | Qwen3.7 Max |
| Design Arena: SVG | 1219 | 1216 | GPT-5.6 Terra |
| Design Arena: ASCII art | 1178 | 1211 | Qwen3.7 Max |
| LiveBench design | 76.0% | Not measured | Only GPT-5.6 Terra measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index51
| Benchmark | GPT-5.6 Terra | Qwen3.7 Max | Leader |
|---|---|---|---|
| LiveBench data analysis | 79.3% | Not measured | Only GPT-5.6 Terra measured |
| Public Benefits Bench (Vals AI) | 62.4% | Not measured | Only GPT-5.6 Terra measured |
| SAGE (Vals AI) | 47.0% | Not measured | Only GPT-5.6 Terra measured |
| MedScribe (Vals AI) | 82.9% | 79.4% | GPT-5.6 Terra |
| Vals Multimodal Index | 65.1% | Not measured | Only GPT-5.6 Terra measured |
| IFBench | 71.2% | 80.5% | Qwen3.7 Max |
| Long context reasoning (AA-LCR) | 83.0% | 79.0% | GPT-5.6 Terra |
| DTBench | 93.3% | 92.3% | GPT-5.6 Terra |
| LMCA | 54.9% | 44.0% | GPT-5.6 Terra |
| Surface Evolver Bench | 83.8% | Not measured | Only GPT-5.6 Terra measured |
| Furniture assembly | 54.2% | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text | 1448 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: instruction following | 1456 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: creative writing | 1413 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Vision | 1271 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: English | 1449 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: non-English | 1441 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: Chinese | 1514 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: German | 1460 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: Japanese | 1454 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: Korean | 1427 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: French | 1466 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: Spanish | 1451 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: Russian | 1452 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: Polish | 1446 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: multi-turn | 1453 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: longer queries | 1454 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: writing and language | 1431 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: entertainment and media | 1418 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Vision: English | 1272 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Vision: Chinese | 1337 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Vision: reading text | 1278 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Vision: humor | 1236 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Vision: creative writing | 1248 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Document | 1472 | Not measured | Only GPT-5.6 Terra measured |
| EQ-Bench 4 | 1234 | 1110 | GPT-5.6 Terra |
| 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 | GPT-5.6 Terra | 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 GPT-5.6 Terra | Qwen3.7 Max |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | No public weights listed |
| Released | July 9, 2026 | May 21, 2026 |
| Context window | 1.05M | 1M |
| Input $/Mtok | $2.00 | $1.475 |
| Output $/Mtok | $12.00 | $4.425 |
| Coding index | 63.9 | 54.1 |
| Independent measurements behind it | 6 | 3 |
| Cost per solved task | $0.42 | $4.69 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.6 Terra better than Qwen3.7 Max for coding?
Pick GPT-5.6 Terra for coding: it scores 63.9 on the Vibe Coding Index against 54.1 for Qwen3.7 Max. Pick Qwen3.7 Max for output tokens 2.7x cheaper ($4.425 vs $12.00 per million) and work measured by LiveCodeBench (Vals AI) or LMArena WebDev: React, where it leads.
Which is cheaper, GPT-5.6 Terra or Qwen3.7 Max?
GPT-5.6 Terra costs $2.00 per million input tokens and $12.00 per million output tokens; Qwen3.7 Max costs $1.475 and $4.425. Per solved SWE-bench Verified task, GPT-5.6 Terra costs $0.42 and Qwen3.7 Max $4.69.
Which has the bigger context window, GPT-5.6 Terra or Qwen3.7 Max?
GPT-5.6 Terra, with 1.05M tokens against 1M.
How many benchmarks is the GPT-5.6 Terra vs Qwen3.7 Max comparison based on?
58 benchmarks have a result for both models as of October 22, 2026. GPT-5.6 Terra's coding index rests on 6 independent benchmarks and Qwen3.7 Max's on 3. Where only one model has a result, the page says so instead of naming a winner.