Claude Sonnet 5 vs GPT-5.6 Terra
GPT-5.6 Terra leads Claude Sonnet 5 on coding, 63.9 to 57.7 on the Vibe Coding Index, and Claude Sonnet 5 costs 1.2x less per output token ($10.00 vs $12.00 per million). Updated October 22, 2026; 118 benchmarks have a result for both.
Claude Sonnet 5 or GPT-5.6 Terra: which to pick
Pick GPT-5.6 Terra for coding: it scores 63.9 on the Vibe Coding Index against 57.7 for Claude Sonnet 5.
Pick Claude Sonnet 5 for output tokens 1.2x cheaper ($10.00 vs $12.00 per million) and work measured by LMArena WebDev or Vibe Code Bench (Vals AI), where it leads.
- Shared results
- 118
- Claude Sonnet 5 only
- 3
- GPT-5.6 Terra only
- 9
- 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 57.7.
6 benchmarks behind each score
Intelligence
AA Intelligence Index
GPT-5.6 Terra
GPT-5.6 Terra leads, 42.1 against 38.2.
Same board, both measured
Agentic work
AA Agentic Index
Claude Sonnet 5
Claude Sonnet 5 leads, 43.6 against 43.2.
Same board, both measured
Reasoning
GPQA Diamond
GPT-5.6 Terra
GPT-5.6 Terra leads, 90.9% against 88.9%.
Same board, both measured
Knowledge
MMLU Pro
Claude Sonnet 5
Claude Sonnet 5 leads, 87.5% against 86.7%.
Same board, both measured
Web design
Design Arena: websites
Claude Sonnet 5
Claude Sonnet 5 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
Claude Sonnet 5
Claude Sonnet 5 $0.0070, GPT-5.6 Terra $0.0080 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5.6 Terra
Claude Sonnet 5 $1.87, GPT-5.6 Terra $0.42.
Published attempt cost and pass rate
At a glance
- Coding index
- Claude Sonnet 5 57.7 from 6 benchmarks, GPT-5.6 Terra 63.9 from 6 benchmarks.
- Widest category
- Math, 11.8 points apart on average over 6 shared benchmarks.
- Widest single gap
- IOI (Vals AI): Claude Sonnet 5 45.0% against GPT-5.6 Terra 87.6%, 42.6 points apart on a 0 to 100 scale.
- Output price
- Claude Sonnet 5 $10.00 against GPT-5.6 Terra $12.00 per million output tokens, 1.2x apart.
- Context window
- Claude Sonnet 5 takes 1M tokens, GPT-5.6 Terra 1.05M.
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 | Claude Sonnet 5 | Bars | GPT-5.6 Terra | Ties |
|---|---|---|---|---|
| Coding | 5 | 19 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 11 | 8 | ||
| Reasoning | 1 | 12 | 1 tie | |
| Math | 3 | 3 | ||
| Knowledge | 6 | 5 | ||
| Web design | 8 | 1 | ||
| General | 12 | 21 | ||
| All shared boards | 46 | 71 | 1 tie |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index519
| Benchmark | Claude Sonnet 5 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| LiveBench coding | 74.7% | 86.2% | GPT-5.6 Terra |
| LMArena WebDev | 1540 | 1522 | Claude Sonnet 5 |
| AA Coding Index | 71.5 | 76.7 | GPT-5.6 Terra |
| LiveCodeBench (Vals AI) | 82.4% | 85.9% | GPT-5.6 Terra |
| SWE-bench Verified (Vals AI) | 79.6% | 95.4% | GPT-5.6 Terra |
| Terminal-Bench 4 (Vals AI) | 10.6% | 22.7% | GPT-5.6 Terra |
| Vibe Code Bench (Vals AI) | 81.3% | 74.6% | Claude Sonnet 5 |
| Code migration (Vals AI) | 44.4% | 47.8% | GPT-5.6 Terra |
| ProgramBench (Vals AI) | 0.0% | 0.5% | GPT-5.6 Terra |
| IOI (Vals AI) | 45.0% | 87.6% | GPT-5.6 Terra |
| Terminal-Bench 2.1 (Vals AI) | 74.5% | 77.5% | GPT-5.6 Terra |
| Vibe Code Bench 1-100 (Vals AI) | 13.8% | 14.8% | GPT-5.6 Terra |
| SkillsBench (Vals AI) | 46.5% | 58.9% | GPT-5.6 Terra |
| CyberBench (Vals AI) | 70.3% | 72.1% | GPT-5.6 Terra |
| SciCode | 54.3% | 55.0% | GPT-5.6 Terra |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 62.9% | Only GPT-5.6 Terra measured |
| FrontierCode | 42.7% | 41.3% | Claude Sonnet 5 |
| GSO-Bench | 37.3% | Not measured | Only Claude Sonnet 5 measured |
| WeirdML | 68.8% | 78.3% | GPT-5.6 Terra |
| ALE-Bench | 1463.1 | 1951.4 | GPT-5.6 Terra |
| LMArena Text: coding | 1483 | 1485 | GPT-5.6 Terra |
| LMArena Text: software and IT | 1474 | 1482 | GPT-5.6 Terra |
| LMArena WebDev: React | 1541 | 1513 | Claude Sonnet 5 |
| LMArena WebDev: HTML | 1527 | 1526 | Claude Sonnet 5 |
| LMArena WebDev: from an image | 1540 | 1541 | GPT-5.6 Terra |
| SciCode | 10.7% | 10.8% | GPT-5.6 Terra |
| Neither model has a result on the other 13 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Claude Sonnet 5 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| AA Intelligence Index | 38.2 | 42.1 | GPT-5.6 Terra |
| Epoch Capabilities Index | 156.9 | 160.1 | GPT-5.6 Terra |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index118
| Benchmark | Claude Sonnet 5 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| AA Agentic Index | 43.6 | 43.2 | Claude Sonnet 5 |
| Design Arena: agents building web apps | 1245 | 1120 | Claude Sonnet 5 |
| Design Arena: agents building full-stack apps | 1249 | 1061 | Claude Sonnet 5 |
| Design Arena: agents building mobile apps | 1215 | 1111 | Claude Sonnet 5 |
| Design Arena: agents building Android apps | 1227 | 1062 | Claude Sonnet 5 |
| Design Arena: agents building Godot games | 1225 | Not measured | Only Claude Sonnet 5 measured |
| Design Arena: agents building games | 1227 | 1083 | Claude Sonnet 5 |
| Design Arena: HTML slides | 1168 | 1058 | Claude Sonnet 5 |
| Design Arena: PowerPoint slides | 1191 | 1105 | Claude Sonnet 5 |
| Design Arena: spreadsheets | 1035 | Not measured | Only Claude Sonnet 5 measured |
| LiveBench research | 82.8% | 60.4% | Claude Sonnet 5 |
| LiveBench browser | 72.0% | 84.0% | GPT-5.6 Terra |
| LiveBench long agentic | 39.0% | 48.4% | GPT-5.6 Terra |
| SREBench (Vals AI) | 0.4% | 9.5% | GPT-5.6 Terra |
| Finance Agent (Vals AI) | 53.9% | 54.4% | GPT-5.6 Terra |
| Tax Agent Bench (Vals AI) | 29.4% | 30.0% | GPT-5.6 Terra |
| Legal Research Bench (Vals AI) | 41.8% | 41.4% | Claude Sonnet 5 |
| Excel Modeling Benchmark (Vals AI) | 66.3% | 66.2% | Claude Sonnet 5 |
| Terminal-Bench-Science (Vals AI) | 5.7% | 10.0% | GPT-5.6 Terra |
| τ²-Bench Telecom | Not measured | 86.3% | Only GPT-5.6 Terra measured |
| APEX-Agents | 54.5% | 58.2% | GPT-5.6 Terra |
| BALROG | Not measured | 53.2% | Only GPT-5.6 Terra measured |
| Vending-Bench 2 | $6,378 | $7,343 | GPT-5.6 Terra |
| Neither model has a result on the other 18 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index112
| Benchmark | Claude Sonnet 5 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| GPQA Diamond | 88.9% | 90.9% | GPT-5.6 Terra |
| LiveBench reasoning | 88.7% | 90.6% | GPT-5.6 Terra |
| MMMU Pro (Vals AI) | 83.0% | 86.5% | GPT-5.6 Terra |
| GPQA Diamond (Artificial Analysis run) | 91.1% | 92.5% | GPT-5.6 Terra |
| Humanity's Last Exam (Artificial Analysis run) | 41.3% | 42.9% | GPT-5.6 Terra |
| GPQA Diamond (Epoch AI run) | 90.5% | 93.3% | GPT-5.6 Terra |
| ARC-AGI | Not measured | 96.5% | Only GPT-5.6 Terra measured |
| ARC-AGI-2 | Not measured | 83.9% | Only GPT-5.6 Terra measured |
| SimpleBench | 60.6% | 48.9% | Claude Sonnet 5 |
| Chess puzzles | 45.0% | 47.0% | GPT-5.6 Terra |
| Mystery game puzzles | 35.0% | 35.0% | Tie |
| LMArena Text: hard prompts | 1461 | 1469 | GPT-5.6 Terra |
| LMArena Text: expert | 1489 | 1496 | GPT-5.6 Terra |
| LMArena Text: hard prompts in English | 1464 | 1465 | GPT-5.6 Terra |
| LMArena Vision: diagrams | 1293 | 1295 | GPT-5.6 Terra |
| LMArena Vision: homework | 1311 | 1312 | GPT-5.6 Terra |
| Kagi LLM Benchmark | Not measured | 51.3% | 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 index33
| Benchmark | Claude Sonnet 5 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| OTIS Mock AIME | 94.7% | 99.7% | GPT-5.6 Terra |
| FrontierMath, tiers 1 to 3 | 65.6% | 86.0% | GPT-5.6 Terra |
| FrontierMath, tier 4 | 29.3% | 70.7% | GPT-5.6 Terra |
| ProofBench | 77.0% | 74.0% | Claude Sonnet 5 |
| LMArena Text: math | 1467 | 1465 | Claude Sonnet 5 |
| LMArena Text: mathematical work | 1481 | 1478 | Claude Sonnet 5 |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index65
| Benchmark | Claude Sonnet 5 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| MMLU Pro | 87.5% | 86.7% | Claude Sonnet 5 |
| LegalBench (Vals AI) | 83.9% | 85.1% | GPT-5.6 Terra |
| CorpFin (Vals AI) | 67.0% | 65.3% | Claude Sonnet 5 |
| TaxEval (Vals AI) | 75.6% | 76.2% | GPT-5.6 Terra |
| MortgageTax (Vals AI) | 70.0% | 67.3% | Claude Sonnet 5 |
| MedCode (Vals AI) | 47.5% | 43.4% | Claude Sonnet 5 |
| SimpleQA Verified | 33.7% | 43.2% | GPT-5.6 Terra |
| LMArena Text: science | 1460 | 1452 | Claude Sonnet 5 |
| LMArena Text: medicine and healthcare | 1442 | 1430 | Claude Sonnet 5 |
| LMArena Text: legal and government | 1454 | 1461 | GPT-5.6 Terra |
| LMArena Text: business and finance | 1436 | 1448 | GPT-5.6 Terra |
| FACTS Benchmark Suite | Not measured | 52.0% | Only GPT-5.6 Terra measured |
| Neither model has a result on the other 6 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index81
| Benchmark | Claude Sonnet 5 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| Design Arena: websites | 1274 | 1268 | Claude Sonnet 5 |
| Design Arena: UI components | 1277 | 1256 | Claude Sonnet 5 |
| Design Arena: game dev | 1277 | 1229 | Claude Sonnet 5 |
| Design Arena: data viz | 1251 | 1240 | Claude Sonnet 5 |
| Design Arena: 3D | 1261 | 1134 | Claude Sonnet 5 |
| Design Arena: all code categories | 1275 | 1241 | Claude Sonnet 5 |
| Design Arena: SVG | 1192 | 1219 | GPT-5.6 Terra |
| Design Arena: ASCII art | 1207 | 1177 | Claude Sonnet 5 |
| LiveBench design | 82.0% | 76.0% | Claude Sonnet 5 |
GeneralLike-for-likePublic boards, shown as published, kept out of the index1221
| Benchmark | Claude Sonnet 5 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| LiveBench data analysis | 71.7% | 79.3% | GPT-5.6 Terra |
| Public Benefits Bench (Vals AI) | 66.0% | 62.4% | Claude Sonnet 5 |
| SAGE (Vals AI) | 48.9% | 47.0% | Claude Sonnet 5 |
| MedScribe (Vals AI) | 76.0% | 82.9% | GPT-5.6 Terra |
| Vals Multimodal Index | 68.8% | 65.1% | Claude Sonnet 5 |
| IFBench | Not measured | 71.2% | Only GPT-5.6 Terra measured |
| Long context reasoning (AA-LCR) | 82.0% | 83.0% | GPT-5.6 Terra |
| DTBench | 92.5% | 93.3% | GPT-5.6 Terra |
| LMCA | 50.0% | 54.9% | GPT-5.6 Terra |
| Surface Evolver Bench | 60.0% | 83.8% | GPT-5.6 Terra |
| Furniture assembly | Not measured | 54.2% | Only GPT-5.6 Terra measured |
| LMArena Text | 1442 | 1448 | GPT-5.6 Terra |
| LMArena Text: instruction following | 1452 | 1456 | GPT-5.6 Terra |
| LMArena Text: creative writing | 1416 | 1413 | Claude Sonnet 5 |
| LMArena Vision | 1273 | 1271 | Claude Sonnet 5 |
| LMArena Text: English | 1450 | 1449 | Claude Sonnet 5 |
| LMArena Text: non-English | 1431 | 1441 | GPT-5.6 Terra |
| LMArena Text: Chinese | 1478 | 1514 | GPT-5.6 Terra |
| LMArena Text: German | 1442 | 1460 | GPT-5.6 Terra |
| LMArena Text: Japanese | 1422 | 1454 | GPT-5.6 Terra |
| LMArena Text: Korean | 1412 | 1427 | GPT-5.6 Terra |
| LMArena Text: French | 1463 | 1466 | GPT-5.6 Terra |
| LMArena Text: Spanish | 1438 | 1451 | GPT-5.6 Terra |
| LMArena Text: Russian | 1450 | 1452 | GPT-5.6 Terra |
| LMArena Text: Polish | 1425 | 1446 | GPT-5.6 Terra |
| LMArena Text: multi-turn | 1453 | 1453 | Claude Sonnet 5 |
| LMArena Text: longer queries | 1462 | 1454 | Claude Sonnet 5 |
| LMArena Text: writing and language | 1436 | 1431 | Claude Sonnet 5 |
| LMArena Text: entertainment and media | 1411 | 1418 | GPT-5.6 Terra |
| LMArena Vision: English | 1275 | 1272 | Claude Sonnet 5 |
| LMArena Vision: Chinese | 1331 | 1337 | GPT-5.6 Terra |
| LMArena Vision: reading text | 1288 | 1278 | Claude Sonnet 5 |
| LMArena Vision: humor | 1261 | 1236 | Claude Sonnet 5 |
| LMArena Vision: creative writing | 1246 | 1248 | GPT-5.6 Terra |
| LMArena Document | 1466 | 1472 | GPT-5.6 Terra |
| Neither model has a result on the other 11 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Claude Sonnet 5 | GPT-5.6 Terra | 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
Claude Sonnet 5 costs less
Repository review
50K input + 3K output tokens
Claude Sonnet 5 costs less
Agent loop
200K input + 10K output tokens
Claude Sonnet 5 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 | Anthropic Claude Sonnet 5 | OpenAI GPT-5.6 Terra |
|---|---|---|
| Maker | Anthropic | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | June 30, 2026 | July 9, 2026 |
| Context window | 1M | 1.05M |
| Input $/Mtok | $2.00 | $2.00 |
| Output $/Mtok | $10.00 | $12.00 |
| Coding index | 57.7 | 63.9 |
| Independent measurements behind it | 6 | 6 |
| Cost per solved task | $1.87 | $0.42 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Claude Sonnet 5 better than GPT-5.6 Terra for coding?
Pick GPT-5.6 Terra for coding: it scores 63.9 on the Vibe Coding Index against 57.7 for Claude Sonnet 5. Pick Claude Sonnet 5 for output tokens 1.2x cheaper ($10.00 vs $12.00 per million) and work measured by LMArena WebDev or Vibe Code Bench (Vals AI), where it leads.
Which is cheaper, Claude Sonnet 5 or GPT-5.6 Terra?
Claude Sonnet 5 costs $2.00 per million input tokens and $10.00 per million output tokens; GPT-5.6 Terra costs $2.00 and $12.00. Per solved SWE-bench Verified task, Claude Sonnet 5 costs $1.87 and GPT-5.6 Terra $0.42.
Which has the bigger context window, Claude Sonnet 5 or GPT-5.6 Terra?
GPT-5.6 Terra, with 1.05M tokens against 1M.
How many benchmarks is the Claude Sonnet 5 vs GPT-5.6 Terra comparison based on?
118 benchmarks have a result for both models as of October 22, 2026. Claude Sonnet 5's coding index rests on 6 independent benchmarks and GPT-5.6 Terra's on 6. Where only one model has a result, the page says so instead of naming a winner.