Claude Opus 4.7 vs GPT-6.1 Terra
GPT-6.1 Terra leads Claude Opus 4.7 on coding, 77.4 to 67.5 on the Vibe Coding Index, and GPT-6.1 Terra costs 5.0x less per output token ($5.00 vs $25.00 per million). Updated October 29, 2026; 17 benchmarks have a result for both.
Claude Opus 4.7 or GPT-6.1 Terra: which to pick
Pick GPT-6.1 Terra for coding: it scores 77.4 on the Vibe Coding Index against 67.5 for Claude Opus 4.7.
Pick Claude Opus 4.7 for work measured by Tax Agent Bench (Vals AI) or Legal Research Bench (Vals AI), where it leads.
- Shared results
- 17
- Claude Opus 4.7 only
- 90
- GPT-6.1 Terra only
- 19
- Like-for-like categories
- 6 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-6.1 Terra
GPT-6.1 Terra scores 77.4 against 67.5.
4 benchmarks behind each score
Intelligence
AA Intelligence Index
No call
Only GPT-6.1 Terra has a AA Intelligence Index result, 51.0.
Directional only
Agentic work
AA Agentic Index
No call
Only Claude Opus 4.7 has a AA Agentic Index result, 38.6.
Directional only
Reasoning
GPQA Diamond
No call
Only Claude Opus 4.7 has a GPQA Diamond result, 90.2%.
Directional only
Math
AIME
No call
Only Claude Opus 4.7 has a AIME result, 96.3%.
Directional only
Knowledge
MMLU Pro
No call
Only Claude Opus 4.7 has a MMLU Pro result, 89.9%.
Directional only
Web design
Design Arena: websites
No call
Only GPT-6.1 Terra has a Design Arena: websites result, 1283.
Directional only
General
Vals Index
No call
Only GPT-6.1 Terra has a Vals Index result, 60.5%.
Directional only
Long documents
Listed context window
GPT-6.1 Terra
GPT-6.1 Terra takes 1.05M tokens against 1M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-6.1 Terra
Claude Opus 4.7 $0.018, GPT-6.1 Terra $0.0035 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Claude Opus 4.7 has the number, $2.95.
Directional only
At a glance
- Coding index
- Claude Opus 4.7 67.5 from 4 benchmarks, GPT-6.1 Terra 77.4 from 4 benchmarks.
- Widest category
- Reasoning, 28.3 points apart on average over 4 shared benchmarks.
- Widest single gap
- FrontierMath, tier 4: Claude Opus 4.7 31.7% against GPT-6.1 Terra 92.7%, 61.0 points apart on a 0 to 100 scale.
- Output price
- Claude Opus 4.7 $25.00 against GPT-6.1 Terra $5.00 per million output tokens, 5.0x apart.
- Context window
- Claude Opus 4.7 takes 1M tokens, GPT-6.1 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 4.7 | Bars | GPT-6.1 Terra | Ties |
|---|---|---|---|---|
| Coding | 0 | 4 | ||
| Agentic work | 2 | 1 | ||
| Reasoning | 0 | 4 | ||
| Math | 0 | 3 | ||
| Knowledge | 0 | 1 | ||
| General | 0 | 2 | ||
| All shared boards | 2 | 15 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
| Benchmark | Claude Opus 4.7 | GPT-6.1 Terra | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 94.3% | Only GPT-6.1 Terra measured |
| LMArena WebDev | 1558 | 1673 | GPT-6.1 Terra |
| AA Coding Index | 73.6 | Not measured | Only Claude Opus 4.7 measured |
| LiveCodeBench (Vals AI) | 85.1% | Not measured | Only Claude Opus 4.7 measured |
| SWE-bench Verified (Vals AI) | 82.0% | Not measured | Only Claude Opus 4.7 measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 49.5% | Only GPT-6.1 Terra measured |
| Vibe Code Bench (Vals AI) | 71.0% | 84.0% | GPT-6.1 Terra |
| Code migration (Vals AI) | 43.9% | Not measured | Only Claude Opus 4.7 measured |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only Claude Opus 4.7 measured |
| Terminal-Bench 2.1 (Vals AI) | 68.5% | Not measured | Only Claude Opus 4.7 measured |
| SciCode | Not measured | 55.1% | Only GPT-6.1 Terra measured |
| Terminal-Bench Hard (Artificial Analysis run) | 54.5% | Not measured | Only Claude Opus 4.7 measured |
| Terminal-Bench | 80.2% | Not measured | Only Claude Opus 4.7 measured |
| FrontierCode | 38.5% | Not measured | Only Claude Opus 4.7 measured |
| GSO-Bench | 44.1% | Not measured | Only Claude Opus 4.7 measured |
| WeirdML | 76.4% | Not measured | Only Claude Opus 4.7 measured |
| MirrorCode | 31.1% | Not measured | Only Claude Opus 4.7 measured |
| ALE-Bench | Not measured | 2608.8 | Only GPT-6.1 Terra measured |
| LMArena Text: coding | 1519 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text: software and IT | 1516 | Not measured | Only Claude Opus 4.7 measured |
| LMArena WebDev: React | 1560 | 1657 | GPT-6.1 Terra |
| LMArena WebDev: HTML | 1554 | 1719 | GPT-6.1 Terra |
| LMArena WebDev: from an image | 1577 | Not measured | Only Claude Opus 4.7 measured |
| SWE Atlas: Codebase QnA | 40.3% | Not measured | Only Claude Opus 4.7 measured |
| SWE Atlas: Refactoring | 48.6% | Not measured | Only Claude Opus 4.7 measured |
| SWE Atlas: Test Writing | 38.5% | Not measured | Only Claude Opus 4.7 measured |
| Neither model has a result on the other 17 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Claude Opus 4.7 | GPT-6.1 Terra | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 51.0 | Only GPT-6.1 Terra measured |
| Epoch Capabilities Index | 156.8 | Not measured | Only Claude Opus 4.7 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index21
| Benchmark | Claude Opus 4.7 | GPT-6.1 Terra | Leader |
|---|---|---|---|
| AA Agentic Index | 38.6 | Not measured | Only Claude Opus 4.7 measured |
| LiveBench research | Not measured | 73.9% | Only GPT-6.1 Terra measured |
| LiveBench browser | Not measured | 93.1% | Only GPT-6.1 Terra measured |
| LiveBench long agentic | Not measured | 48.6% | Only GPT-6.1 Terra measured |
| Finance Agent (Vals AI) | 51.5% | Not measured | Only Claude Opus 4.7 measured |
| Tax Agent Bench (Vals AI) | 23.7% | 15.2% | Claude Opus 4.7 |
| Legal Research Bench (Vals AI) | 38.5% | 26.0% | Claude Opus 4.7 |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 1.7% | Only GPT-6.1 Terra measured |
| Excel Modeling Benchmark (Vals AI) | 63.7% | Not measured | Only Claude Opus 4.7 measured |
| τ²-Bench Telecom | 88.6% | Not measured | Only Claude Opus 4.7 measured |
| APEX-Agents | 49.2% | 68.3% | GPT-6.1 Terra |
| OSWorld 2.0 | 18.2% | Not measured | Only Claude Opus 4.7 measured |
| PostTrainBench | 28.6% | Not measured | Only Claude Opus 4.7 measured |
| Vending-Bench 2 | $10,937 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Search | 1230 | Not measured | Only Claude Opus 4.7 measured |
| MCP Atlas | 79.1% | Not measured | Only Claude Opus 4.7 measured |
| Neither model has a result on the other 26 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Claude Opus 4.7 | GPT-6.1 Terra | Leader |
|---|---|---|---|
| GPQA Diamond | 90.2% | Not measured | Only Claude Opus 4.7 measured |
| LiveBench reasoning | Not measured | 88.7% | Only GPT-6.1 Terra measured |
| MMMU Pro (Vals AI) | 85.5% | Not measured | Only Claude Opus 4.7 measured |
| GPQA Diamond (Artificial Analysis run) | 91.4% | Not measured | Only Claude Opus 4.7 measured |
| Humanity's Last Exam (Artificial Analysis run) | 42.3% | 46.2% | GPT-6.1 Terra |
| GPQA Diamond (Epoch AI run) | 90.2% | 94.3% | GPT-6.1 Terra |
| ARC-AGI | 93.5% | Not measured | Only Claude Opus 4.7 measured |
| ARC-AGI-2 | 75.8% | Not measured | Only Claude Opus 4.7 measured |
| SimpleBench | 61.7% | Not measured | Only Claude Opus 4.7 measured |
| Chess puzzles | 41.0% | Not measured | Only Claude Opus 4.7 measured |
| Mystery game puzzles | 28.0% | 74.0% | GPT-6.1 Terra |
| EBR-bench | 19.0% | 78.1% | GPT-6.1 Terra |
| LMArena Text: hard prompts | 1506 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text: expert | 1520 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text: hard prompts in English | 1508 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Vision: diagrams | 1336 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Vision: homework | 1337 | Not measured | Only Claude Opus 4.7 measured |
| Kagi LLM Benchmark | 80.7% | Not measured | Only Claude Opus 4.7 measured |
| Humanity's Last Exam (Scale run) | Not measured | 35.7% | Only GPT-6.1 Terra measured |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Claude Opus 4.7 | GPT-6.1 Terra | Leader |
|---|---|---|---|
| AIME | 96.3% | Not measured | Only Claude Opus 4.7 measured |
| OTIS Mock AIME | 97.8% | 100.0% | GPT-6.1 Terra |
| FrontierMath, tiers 1 to 3 | 70.2% | 90.9% | GPT-6.1 Terra |
| FrontierMath, tier 4 | 31.7% | 92.7% | GPT-6.1 Terra |
| ProofBench | 54.0% | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text: math | 1499 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text: mathematical work | 1510 | Not measured | Only Claude Opus 4.7 measured |
| MathArena | 20.1% | Not measured | Only Claude Opus 4.7 measured |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Claude Opus 4.7 | GPT-6.1 Terra | Leader |
|---|---|---|---|
| MMLU Pro | 89.9% | Not measured | Only Claude Opus 4.7 measured |
| LegalBench (Vals AI) | 85.3% | Not measured | Only Claude Opus 4.7 measured |
| CaseLaw (Vals AI) | 68.4% | Not measured | Only Claude Opus 4.7 measured |
| CorpFin (Vals AI) | 66.1% | Not measured | Only Claude Opus 4.7 measured |
| TaxEval (Vals AI) | 75.3% | Not measured | Only Claude Opus 4.7 measured |
| MortgageTax (Vals AI) | 70.3% | Not measured | Only Claude Opus 4.7 measured |
| MedCode (Vals AI) | 54.9% | Not measured | Only Claude Opus 4.7 measured |
| SimpleQA Verified | 51.7% | 57.3% | GPT-6.1 Terra |
| LMArena Text: science | 1508 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text: medicine and healthcare | 1495 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text: legal and government | 1504 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text: business and finance | 1490 | Not measured | Only Claude Opus 4.7 measured |
| FACTS Benchmark Suite | 35.8% | Not measured | Only Claude Opus 4.7 measured |
| Neither model has a result on the other 7 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Claude Opus 4.7 | GPT-6.1 Terra | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1283 | Only GPT-6.1 Terra measured |
| Design Arena: UI components | Not measured | 1277 | Only GPT-6.1 Terra measured |
| Design Arena: game dev | Not measured | 1348 | Only GPT-6.1 Terra measured |
| Design Arena: all code categories | Not measured | 1295 | Only GPT-6.1 Terra measured |
| LiveBench design | Not measured | 82.3% | Only GPT-6.1 Terra measured |
| Neither model has a result on the other 4 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Claude Opus 4.7 | GPT-6.1 Terra | Leader |
|---|---|---|---|
| Vals Index | Not measured | 60.5% | Only GPT-6.1 Terra measured |
| LiveBench data analysis | Not measured | 82.3% | Only GPT-6.1 Terra measured |
| MedScribe (Vals AI) | 83.0% | Not measured | Only Claude Opus 4.7 measured |
| Vals Multimodal Index | 67.4% | Not measured | Only Claude Opus 4.7 measured |
| IFBench | 58.6% | Not measured | Only Claude Opus 4.7 measured |
| Long context reasoning (AA-LCR) | 78.7% | 82.7% | GPT-6.1 Terra |
| DTBench | 94.7% | Not measured | Only Claude Opus 4.7 measured |
| LMCA | 52.2% | Not measured | Only Claude Opus 4.7 measured |
| Furniture assembly | 33.3% | 79.2% | GPT-6.1 Terra |
| ExploitBench | 26.5% | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text | 1490 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text: instruction following | 1498 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text: creative writing | 1486 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Vision | 1317 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text: English | 1496 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text: non-English | 1480 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text: Chinese | 1531 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text: German | 1497 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text: Japanese | 1473 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text: Korean | 1464 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text: French | 1503 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text: Spanish | 1494 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text: Russian | 1494 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text: Polish | 1490 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text: multi-turn | 1505 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text: longer queries | 1505 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text: writing and language | 1492 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text: entertainment and media | 1474 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Vision: English | 1322 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Vision: Chinese | 1371 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Vision: reading text | 1327 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Vision: humor | 1309 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Vision: recognizing things | 1278 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Vision: creative writing | 1326 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Document | 1495 | Not measured | Only Claude Opus 4.7 measured |
| MultiChallenge | Not measured | 74.2% | Only GPT-6.1 Terra measured |
| EQ-Bench 4 | 1293 | Not measured | Only Claude Opus 4.7 measured |
| 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 Opus 4.7 | GPT-6.1 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
GPT-6.1 Terra costs less
Repository review
50K input + 3K output tokens
GPT-6.1 Terra costs less
Agent loop
200K input + 10K output tokens
GPT-6.1 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 4.7 | OpenAI GPT-6.1 Terra |
|---|---|---|
| Maker | Anthropic | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | April 16, 2026 | October 26, 2026 |
| Context window | 1M | 1.05M |
| Input $/Mtok | $5.00 | $1.00 |
| Output $/Mtok | $25.00 | $5.00 |
| Coding index | 67.5 | 77.4 |
| Independent measurements behind it | 4 | 4 |
| Cost per solved task | $2.95 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Claude Opus 4.7 better than GPT-6.1 Terra for coding?
Pick GPT-6.1 Terra for coding: it scores 77.4 on the Vibe Coding Index against 67.5 for Claude Opus 4.7. Pick Claude Opus 4.7 for work measured by Tax Agent Bench (Vals AI) or Legal Research Bench (Vals AI), where it leads.
Which is cheaper, Claude Opus 4.7 or GPT-6.1 Terra?
Claude Opus 4.7 costs $5.00 per million input tokens and $25.00 per million output tokens; GPT-6.1 Terra costs $1.00 and $5.00. Cost per solved task is known for Claude Opus 4.7 only.
Which has the bigger context window, Claude Opus 4.7 or GPT-6.1 Terra?
GPT-6.1 Terra, with 1.05M tokens against 1M.
How many benchmarks is the Claude Opus 4.7 vs GPT-6.1 Terra comparison based on?
17 benchmarks have a result for both models as of October 29, 2026. Claude Opus 4.7's coding index rests on 4 independent benchmarks and GPT-6.1 Terra's on 4. Where only one model has a result, the page says so instead of naming a winner.