Claude Opus 4.1 vs GPT-5.6 Terra
GPT-5.6 Terra leads Claude Opus 4.1 on the Artificial Analysis Intelligence Index, 42.1 to 22.8, and GPT-5.6 Terra costs 6.3x less per output token ($12.00 vs $75.00 per million). Updated October 22, 2026; 61 benchmarks have a result for both.
vs boards won of 61 shared
Claude Opus 4.1 or GPT-5.6 Terra: which to pick
Only GPT-5.6 Terra has a coding score, 63.9 on the Vibe Coding Index; Claude Opus 4.1 has none, so the two cannot be ranked on coding.
Pick Claude Opus 4.1 for work measured by SimpleBench or MMLU Pro, where it leads.
Model AClaude Opus 4.1AnthropicNot benchmarked1 independent benchmarkNo cost per solved taskModel BGPT-5.6 TerraOpenAI63.9/1006 independent benchmarks$0.42 per solved task
- Shared results
- 61
- Claude Opus 4.1 only
- 15
- GPT-5.6 Terra only
- 68
- Like-for-like categories
- 7 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
No call
Only GPT-5.6 Terra has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GPT-5.6 Terra
GPT-5.6 Terra leads, 42.1 against 22.8.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GPT-5.6 Terra has a AA Agentic Index result, 43.2.
Directional only
Reasoning
GPQA Diamond
GPT-5.6 Terra
GPT-5.6 Terra leads, 90.9% against 76.3%.
Same board, both measured
Math
AIME
No call
Only Claude Opus 4.1 has a AIME result, 78.2%.
Directional only
Knowledge
MMLU Pro
Claude Opus 4.1
Claude Opus 4.1 leads, 87.9% against 86.7%.
Same board, both measured
Web design
Design Arena: websites
GPT-5.6 Terra
GPT-5.6 Terra leads, 1268 against 1176.
Same board, both measured
Long documents
Listed context window
GPT-5.6 Terra
GPT-5.6 Terra takes 1.05M tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-5.6 Terra
Claude Opus 4.1 $0.052, GPT-5.6 Terra $0.0080 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.6 Terra has the number, $0.42.
Directional only
At a glance
- Coding index
- Claude Opus 4.1 not benchmarked, GPT-5.6 Terra 63.9 from 6 benchmarks.
- Widest category
- Math, 37.1 points apart on average over 5 shared benchmarks.
- Widest single gap
- FrontierMath, tiers 1 to 3: Claude Opus 4.1 12.6% against GPT-5.6 Terra 86.0%, 73.3 points apart on a 0 to 100 scale.
- Output price
- Claude Opus 4.1 $75.00 against GPT-5.6 Terra $12.00 per million output tokens, 6.3x apart.
- Context window
- Claude Opus 4.1 takes 200K 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 Opus 4.1 | Bars | GPT-5.6 Terra | Ties |
|---|---|---|---|---|
| Coding | 0 | 7 | ||
| Intelligence | 0 | 2 | ||
| Reasoning | 1 | 7 | ||
| Math | 0 | 5 | ||
| Knowledge | 2 | 8 | ||
| Web design | 1 | 7 | ||
| General | 1 | 20 | ||
| All shared boards | 5 | 56 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index07
| Benchmark | Claude Opus 4.1 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 86.2% | Only GPT-5.6 Terra measured |
| LMArena WebDev | 1390 | 1522 | GPT-5.6 Terra |
| AA Coding Index | Not measured | 76.7 | Only GPT-5.6 Terra measured |
| LiveCodeBench (Vals AI) | 66.5% | 85.9% | GPT-5.6 Terra |
| SWE-bench Verified (Vals AI) | Not measured | 95.4% | Only GPT-5.6 Terra measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 22.7% | Only GPT-5.6 Terra measured |
| Vibe Code Bench (Vals AI) | Not measured | 74.6% | Only GPT-5.6 Terra measured |
| Code migration (Vals AI) | Not measured | 47.8% | Only GPT-5.6 Terra measured |
| ProgramBench (Vals AI) | Not measured | 0.5% | Only GPT-5.6 Terra measured |
| IOI (Vals AI) | Not measured | 87.6% | Only GPT-5.6 Terra measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 77.5% | Only GPT-5.6 Terra measured |
| Vibe Code Bench 1-100 (Vals AI) | Not measured | 14.8% | Only GPT-5.6 Terra measured |
| SkillsBench (Vals AI) | Not measured | 58.9% | Only GPT-5.6 Terra measured |
| CyberBench (Vals AI) | Not measured | 72.1% | Only GPT-5.6 Terra measured |
| SciCode | Not measured | 55.0% | Only GPT-5.6 Terra measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 62.9% | Only GPT-5.6 Terra measured |
| Terminal-Bench | 38.0% | Not measured | Only Claude Opus 4.1 measured |
| FrontierCode | Not measured | 41.3% | Only GPT-5.6 Terra measured |
| WeirdML | 45.9% | 78.3% | GPT-5.6 Terra |
| Cybench | 42.0% | Not measured | Only Claude Opus 4.1 measured |
| ALE-Bench | Not measured | 1951.4 | Only GPT-5.6 Terra measured |
| LMArena Text: coding | 1479 | 1485 | GPT-5.6 Terra |
| LMArena Text: software and IT | 1456 | 1482 | GPT-5.6 Terra |
| LMArena WebDev: React | Not measured | 1513 | Only GPT-5.6 Terra measured |
| LMArena WebDev: HTML | 1399 | 1526 | GPT-5.6 Terra |
| LMArena WebDev: from an image | Not measured | 1541 | Only GPT-5.6 Terra measured |
| SciCode | 4.6% | 10.8% | GPT-5.6 Terra |
| SWE-Bench Pro V2 | Not measured | 92.4% | Only GPT-5.6 Terra measured |
| Neither model has a result on the other 15 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Claude Opus 4.1 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| AA Intelligence Index | 22.8 | 42.1 | GPT-5.6 Terra |
| Epoch Capabilities Index | 144.1 | 160.1 | GPT-5.6 Terra |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index24 boards, none shared
| Benchmark | Claude Opus 4.1 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 43.2 | Only GPT-5.6 Terra measured |
| Design Arena: agents building web apps | Not measured | 1120 | Only GPT-5.6 Terra measured |
| Design Arena: agents building full-stack apps | Not measured | 1061 | Only GPT-5.6 Terra measured |
| Design Arena: agents building mobile apps | Not measured | 1110 | Only GPT-5.6 Terra measured |
| Design Arena: agents building Android apps | Not measured | 1060 | Only GPT-5.6 Terra measured |
| Design Arena: agents building games | Not measured | 1083 | Only GPT-5.6 Terra measured |
| Design Arena: HTML slides | Not measured | 1047 | Only GPT-5.6 Terra measured |
| Design Arena: PowerPoint slides | Not measured | 1104 | Only GPT-5.6 Terra measured |
| LiveBench research | Not measured | 60.4% | Only GPT-5.6 Terra measured |
| LiveBench browser | Not measured | 84.0% | Only GPT-5.6 Terra measured |
| LiveBench long agentic | Not measured | 48.4% | Only GPT-5.6 Terra measured |
| SREBench (Vals AI) | Not measured | 9.5% | Only GPT-5.6 Terra measured |
| Finance Agent (Vals AI) | Not measured | 54.4% | Only GPT-5.6 Terra measured |
| Tax Agent Bench (Vals AI) | Not measured | 30.0% | Only GPT-5.6 Terra measured |
| Legal Research Bench (Vals AI) | Not measured | 41.4% | Only GPT-5.6 Terra measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 66.2% | Only GPT-5.6 Terra measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 10.0% | Only GPT-5.6 Terra measured |
| τ²-Bench Telecom | Not measured | 86.3% | Only GPT-5.6 Terra measured |
| APEX-Agents | Not measured | 58.2% | Only GPT-5.6 Terra measured |
| METR time horizons | 66.8% | Not measured | Only Claude Opus 4.1 measured |
| GDPval | 43.6% | Not measured | Only Claude Opus 4.1 measured |
| BALROG | Not measured | 53.2% | Only GPT-5.6 Terra measured |
| DeepResearch Bench | 48.3% | Not measured | Only Claude Opus 4.1 measured |
| Vending-Bench 2 | Not measured | $7,343 | Only GPT-5.6 Terra measured |
| Neither model has a result on the other 18 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index17
| Benchmark | Claude Opus 4.1 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| GPQA Diamond | 76.3% | 90.9% | GPT-5.6 Terra |
| LiveBench reasoning | Not measured | 90.6% | Only GPT-5.6 Terra measured |
| MMMU Pro (Vals AI) | 77.5% | 86.5% | GPT-5.6 Terra |
| GPQA Diamond (Artificial Analysis run) | Not measured | 92.5% | Only GPT-5.6 Terra measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 42.9% | Only GPT-5.6 Terra measured |
| GPQA Diamond (Epoch AI run) | 77.3% | 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 | 60.0% | 48.9% | Claude Opus 4.1 |
| Chess puzzles | Not measured | 47.0% | Only GPT-5.6 Terra measured |
| Mystery game puzzles | 21.0% | 35.0% | GPT-5.6 Terra |
| EBR-bench | 7.9% | Not measured | Only Claude Opus 4.1 measured |
| VPCT | 35.0% | Not measured | Only Claude Opus 4.1 measured |
| LMArena Text: hard prompts | 1442 | 1469 | GPT-5.6 Terra |
| LMArena Text: expert | 1438 | 1496 | GPT-5.6 Terra |
| LMArena Text: hard prompts in English | 1451 | 1465 | GPT-5.6 Terra |
| LMArena Vision: diagrams | Not measured | 1295 | Only GPT-5.6 Terra measured |
| LMArena Vision: homework | Not measured | 1312 | Only GPT-5.6 Terra measured |
| Kagi LLM Benchmark | Not measured | 51.3% | Only GPT-5.6 Terra measured |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | Claude Opus 4.1 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| AIME | 78.2% | Not measured | Only Claude Opus 4.1 measured |
| MATH 500 (Vals AI) | 95.4% | Not measured | Only Claude Opus 4.1 measured |
| MGSM (Vals AI) | 94.4% | Not measured | Only Claude Opus 4.1 measured |
| OTIS Mock AIME | 68.9% | 99.7% | GPT-5.6 Terra |
| FrontierMath, tiers 1 to 3 | 12.6% | 86.0% | GPT-5.6 Terra |
| FrontierMath, tier 4 | 2.4% | 70.7% | GPT-5.6 Terra |
| ProofBench | Not measured | 74.0% | Only GPT-5.6 Terra measured |
| LMArena Text: math | 1431 | 1465 | GPT-5.6 Terra |
| LMArena Text: mathematical work | 1434 | 1478 | GPT-5.6 Terra |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index28
| Benchmark | Claude Opus 4.1 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| MMLU Pro | 87.9% | 86.7% | Claude Opus 4.1 |
| LegalBench (Vals AI) | 83.5% | 85.1% | GPT-5.6 Terra |
| CorpFin (Vals AI) | Not measured | 65.3% | Only GPT-5.6 Terra measured |
| TaxEval (Vals AI) | 73.7% | 76.2% | GPT-5.6 Terra |
| MortgageTax (Vals AI) | 61.1% | 67.3% | GPT-5.6 Terra |
| MedQA (Vals AI) | 93.6% | Not measured | Only Claude Opus 4.1 measured |
| MedCode (Vals AI) | 47.2% | 43.4% | Claude Opus 4.1 |
| SimpleQA Verified | Not measured | 43.2% | Only GPT-5.6 Terra measured |
| LMArena Text: science | 1422 | 1452 | GPT-5.6 Terra |
| LMArena Text: medicine and healthcare | 1416 | 1430 | GPT-5.6 Terra |
| LMArena Text: legal and government | 1421 | 1461 | GPT-5.6 Terra |
| LMArena Text: business and finance | 1406 | 1448 | GPT-5.6 Terra |
| FACTS Benchmark Suite | 40.4% | 52.0% | GPT-5.6 Terra |
| PRBench Finance | 35.2% | Not measured | Only Claude Opus 4.1 measured |
| PRBench Legal | 34.0% | Not measured | Only Claude Opus 4.1 measured |
| Neither model has a result on the other 5 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index17
| Benchmark | Claude Opus 4.1 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| Design Arena: websites | 1176 | 1268 | GPT-5.6 Terra |
| Design Arena: UI components | 1162 | 1256 | GPT-5.6 Terra |
| Design Arena: game dev | 1171 | 1229 | GPT-5.6 Terra |
| Design Arena: data viz | 1166 | 1240 | GPT-5.6 Terra |
| Design Arena: 3D | 1152 | 1134 | Claude Opus 4.1 |
| Design Arena: all code categories | 1170 | 1240 | GPT-5.6 Terra |
| Design Arena: SVG | 1151 | 1219 | GPT-5.6 Terra |
| Design Arena: ASCII art | 1167 | 1178 | GPT-5.6 Terra |
| LiveBench design | Not measured | 76.0% | Only GPT-5.6 Terra measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index120
| Benchmark | Claude Opus 4.1 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 79.3% | Only GPT-5.6 Terra measured |
| Public Benefits Bench (Vals AI) | Not measured | 62.4% | Only GPT-5.6 Terra measured |
| SAGE (Vals AI) | 31.4% | 47.0% | GPT-5.6 Terra |
| MedScribe (Vals AI) | 73.9% | 82.9% | GPT-5.6 Terra |
| Vals Multimodal Index | Not measured | 65.1% | Only GPT-5.6 Terra measured |
| IFBench | Not measured | 71.2% | Only GPT-5.6 Terra measured |
| Long context reasoning (AA-LCR) | Not measured | 83.0% | Only GPT-5.6 Terra measured |
| DTBench | 80.0% | 93.3% | GPT-5.6 Terra |
| LMCA | 37.1% | 54.9% | GPT-5.6 Terra |
| Creative writing (Lech Mazur) | 84.7% | Not measured | Only Claude Opus 4.1 measured |
| Surface Evolver Bench | Not measured | 83.8% | Only GPT-5.6 Terra measured |
| Furniture assembly | Not measured | 54.2% | Only GPT-5.6 Terra measured |
| LMArena Text | 1419 | 1448 | GPT-5.6 Terra |
| LMArena Text: instruction following | 1435 | 1456 | GPT-5.6 Terra |
| LMArena Text: creative writing | 1412 | 1413 | GPT-5.6 Terra |
| LMArena Vision | Not measured | 1271 | Only GPT-5.6 Terra measured |
| LMArena Text: English | 1433 | 1449 | GPT-5.6 Terra |
| LMArena Text: non-English | 1405 | 1441 | GPT-5.6 Terra |
| LMArena Text: Chinese | 1427 | 1514 | GPT-5.6 Terra |
| LMArena Text: German | 1413 | 1460 | GPT-5.6 Terra |
| LMArena Text: Japanese | 1377 | 1454 | GPT-5.6 Terra |
| LMArena Text: Korean | 1381 | 1427 | GPT-5.6 Terra |
| LMArena Text: French | 1433 | 1466 | GPT-5.6 Terra |
| LMArena Text: Spanish | 1448 | 1451 | GPT-5.6 Terra |
| LMArena Text: Russian | 1422 | 1452 | GPT-5.6 Terra |
| LMArena Text: Polish | 1413 | 1446 | GPT-5.6 Terra |
| LMArena Text: multi-turn | 1444 | 1453 | GPT-5.6 Terra |
| LMArena Text: longer queries | 1455 | 1454 | Claude Opus 4.1 |
| LMArena Text: writing and language | 1418 | 1431 | GPT-5.6 Terra |
| LMArena Text: entertainment and media | 1398 | 1418 | GPT-5.6 Terra |
| LMArena Vision: English | Not measured | 1272 | Only GPT-5.6 Terra measured |
| LMArena Vision: Chinese | Not measured | 1337 | Only GPT-5.6 Terra measured |
| LMArena Vision: reading text | Not measured | 1278 | Only GPT-5.6 Terra measured |
| LMArena Vision: humor | Not measured | 1236 | Only GPT-5.6 Terra measured |
| LMArena Vision: creative writing | Not measured | 1248 | Only GPT-5.6 Terra measured |
| LMArena Document | Not measured | 1472 | Only GPT-5.6 Terra measured |
| MultiChallenge | 57.2% | Not measured | Only Claude Opus 4.1 measured |
| EQ-Bench 4 | Not measured | 1234 | Only GPT-5.6 Terra measured |
| Neither model has a result on the other 10 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Claude Opus 4.1 | 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 Opus 4.1$0.052Fits in one request
GPT-5.6 Terra$0.0080Fits in one request
GPT-5.6 Terra costs less
Repository review
50K input + 3K output tokens
Claude Opus 4.1$0.975Fits in one request
GPT-5.6 Terra$0.136Fits in one request
GPT-5.6 Terra costs less
Agent loop
200K input + 10K output tokens
Claude Opus 4.1$3.75Exceeds the context window
GPT-5.6 Terra$0.520Fits in one request
GPT-5.6 Terra 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 Opus 4.1 | OpenAI GPT-5.6 Terra |
|---|---|---|
| Maker | Anthropic | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | August 5, 2025 | July 9, 2026 |
| Context window | 200K | 1.05M |
| Input $/Mtok | $15.00 | $2.00 |
| Output $/Mtok | $75.00 | $12.00 |
| Coding index | Not benchmarked | 63.9 |
| Independent measurements behind it | 1 | 6 |
| Cost per solved task | Not computable | $0.42 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Claude Opus 4.1 better than GPT-5.6 Terra for coding?
Only GPT-5.6 Terra has a coding score, 63.9 on the Vibe Coding Index; Claude Opus 4.1 has none, so the two cannot be ranked on coding. Pick Claude Opus 4.1 for work measured by SimpleBench or MMLU Pro, where it leads.
Which is cheaper, Claude Opus 4.1 or GPT-5.6 Terra?
Claude Opus 4.1 costs $15.00 per million input tokens and $75.00 per million output tokens; GPT-5.6 Terra costs $2.00 and $12.00. Cost per solved task is known for GPT-5.6 Terra only.
Which has the bigger context window, Claude Opus 4.1 or GPT-5.6 Terra?
GPT-5.6 Terra, with 1.05M tokens against 200K.
How many benchmarks is the Claude Opus 4.1 vs GPT-5.6 Terra comparison based on?
61 benchmarks have a result for both models as of October 22, 2026. Claude Opus 4.1's coding index rests on 1 independent benchmark and GPT-5.6 Terra's on 6. Where only one model has a result, the page says so instead of naming a winner.