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Claude Opus 4.7 vs GPT-6.1 Terra

Full profiles: Claude Opus 4.7 (Anthropic) and GPT-6.1 Terra (OpenAI): every benchmark, price and rank.

Updated · Data from 9 sources

As of 27 Oct 2026, Claude Opus 4.7 and GPT-6.1 Terra (max) are neck and neck on quality (63.2 vs 65.9 on the BenchLeader Index, inside the ±7.5 margin of error), and GPT-6.1 Terra (max) costs 5.0× less, which makes it the better buy for most workloads. GPT-6.1 Terra leads in every category we measure both in.

Claude Opus 4.7 benchmarks Anthropic GPT-6.1 Terra benchmarks max OpenAIOpen in the comparison tool

Which to pick for…

The pick's numbers come first. Category scores on the BenchLeader scale (50 is the average model, 15 points one standard deviation). High confidence means a lead of half a standard deviation or more; under one point is called either.

Use casePickWhyConfidence
ReasoningGPT-6.1 Terra86 vs 65 category scoreHigh
KnowledgeGPT-6.1 Terra75 vs 62 category scoreHigh
Images and other mediaEither67 vs 68 category scoreEither
Chat quality (human preference)–Only Claude Opus 4.7 has an Arena rating (68 category score)Not comparable
Lowest costGPT-6.1 Terra$2.00 vs $10.00 per 1M blended; agentic coding session $0.080 vs $0.400–
Fastest first answerClaude Opus 4.71.2 s (Claude Opus 4.7) vs 131.8 s (GPT-6.1 Terra (max)) to the first answer token–
High volume on a budgetNeither flagshipA smaller model is usually the better fit: Cheapest good LLMs, Fastest LLMs–

Key differences

  • Claude Opus 4.7 and GPT-6.1 Terra (max) are neck and neck on quality (63.2 vs 65.9, within the ±7.5 margin).
  • Claude Opus 4.7 is stronger in agents & tools, coding, human preference, maths.
  • GPT-6.1 Terra (max) is stronger in knowledge, multimodal, reasoning, composite, long context.
  • GPT-6.1 Terra (max) is 5.0× cheaper ($2.00 vs $10.00 per 1M blended).
  • GPT-6.1 Terra (max) streams 1.5× faster (91 vs 60 tokens per second).

At a glance

SpecClaude Opus 4.7GPT-6.1 Terra
DeveloperAnthropicOpenAI
Released16 Apr 202626 Oct 2026
Context window (tokens)1,000,0001,050,000
Compared atClaude Opus 4.7GPT-6.1 Terra (max)
Open weightsNoNo
Price in / out / cached input per 1M$5.00 / $25.00 / $0.500$1.00 / $5.00 / $0.050

Category by category

Category scores on the BenchLeader scale: 50 is the average evaluated model, 15 points is one standard deviation. A gap under one point is called level.

CategoryClaude Opus 4.7GPT-6.1 Terra (max)Leader
Reasoning64.886.2GPT-6.1 Terra
Coding61.4–not comparable
Agents & tools65.4–not comparable
Maths66.7–not comparable
Knowledge62.374.5GPT-6.1 Terra
Human preference67.5–not comparable
Multimodal66.867.5level
Long context–66.3not comparable
Composite–82.8not comparable

Where sources disagree

Across the 0 benchmarks both have, Claude Opus 4.7 leads 0, GPT-6.1 Terra leads 0. Every overall index that rates both puts GPT-6.1 Terra ahead.

Overall indexClaude Opus 4.7GPT-6.1 Terra (max)Leader
BenchLeader Index63.265.9neck and neck
AA Intelligence Index v4.3.230.9†48.1GPT-6.1 Terra

Each publisher runs its own tasks, harnesses and effort settings, so their overall verdicts can differ; BenchLeader reconciles them on one scale. † measured at another effort setting of the same model (the publisher did not test the one compared here).

Price, speed and latency

List prices blended at three input tokens per output token. Per-task prices use the token counts on the methodology page; suite cost is what Artificial Analysis paid to run its whole Intelligence Index.

MeasureClaude Opus 4.7GPT-6.1 Terra (max)
Blended price per 1M tokens$10.00$2.00
Input / output per 1M$5.00 / $25.00$1.00 / $5.00
Cached input per 1M$0.500$0.050
Price per task: chat reply$0.0095$0.0019
Price per task: summarize a 30-page report$0.075$0.015
Price per task: code edit$0.068$0.013
Price per task: agentic coding session$0.400$0.080
Price per task: structured extraction$0.015$0.0030
Cost to run a full benchmark suite–$0.403
Output speed60 tok/s91 tok/s
Time to first answer1.2 s131.8 s
Time to a complete answer–137.3 s

Each model is compared at its best-ranked reasoning-effort setting. Claude Opus 4.7 scores 63.2 and takes 1 s to its first answer token; Claude Opus 4.7 (max) scores 62.2 in 27 s, Claude Opus 4.7 (high) scores 61.0 in 1 s. GPT-6.1 Terra (max) scores 65.9 and takes 132 s to its first answer token; GPT-6.1 Terra (xhigh) scores 65.0 in 18 s, GPT-6.1 Terra (high) scores 64.7 in 7 s. Higher effort means more thinking before the answer: better scores, longer waits and more tokens billed. How reasoning effort changes scores and cost.

Every benchmark

121 benchmarks, grouped by category; each name links to the board and its leaders. † measured at another effort setting of the same model, shown for reference and not counted as a win.

MetricPublisherClaude Opus 4.7GPT-6.1 Terra (max)
BenchLeader IndexBenchLeader63.265.9
Agents & tools scoreBenchLeader65.4–
Coding scoreBenchLeader61.4–
Human preference scoreBenchLeader67.5–
Knowledge scoreBenchLeader62.374.5
Maths scoreBenchLeader66.7–
Multimodal scoreBenchLeader66.867.5
Reasoning scoreBenchLeader64.886.2
Composite scoreBenchLeader–82.8
Long context scoreBenchLeader–66.3
Blended price $/MBenchLeader$10.00$2.00
Output speedBenchLeader60 tok/s91 tok/s
Time to first answerBenchLeader1.2 s131.8 s
Context windowBenchLeader1,000,000 tokens1,050,000 tokens
Reasoning
GPQA DiamondEpoch AI Benchmarking Hub86.4%†–
Humanity's Last ExamScale AI / CAIS36.2%–
SimpleBenchSimpleBench61.7%–
Arena Hard PromptsArena (LMArena)1519–
LiveBench 2.0 ReasoningLiveBench–88.7%†
LiveBench 1.0 ReasoningLiveBench87.2%†–
GPQA Diamond (AA)Artificial Analysis91.4%†–
Humanity's Last Exam (AA)Artificial Analysis42.3%†46.3%
GPQA Diamond (Vals)Vals AI90.2%–
Kagi LLM BenchmarkKagi LLM Benchmark73.3%–
ARC-AGI-3ARC Prize0.2%†–
CritPtArtificial Analysis12.0%†28.0%
Chess PuzzlesEpoch AI Benchmarking Hub41.0%†–
Mystery Game PuzzlesEpoch AI Benchmarking Hub13.0%–
LMCAEpoch AI Benchmarking Hub52.2%†–
DTBenchEpoch AI Benchmarking Hub94.7%†–
ForecastBenchForecastBench63.1%–
Coding
SWE-bench Verified (Epoch)Epoch AI Benchmarking Hub83.5%†–
SciCodeSciCode54.5%†–
WeirdMLWeirdML76.4%–
FrontierCodeCognition38.5%–
GSO-BenchGSO-Bench44.1%–
Arena CodingArena (LMArena)1546–
Arena WebDevArena (LMArena)1555–
LiveBench 2.0 CodingLiveBench–90.8%†
Show all 121 benchmarks
MetricPublisherClaude Opus 4.7GPT-6.1 Terra (max)
Coding
LiveBench 1.0 CodingLiveBench82.1%†–
SciCode (AA)Artificial Analysis–55.1%
LiveCodeBenchVals AI85.1%–
SWE-bench (Vals)Vals AI82.0%–
Code MigrationVals AI43.9%–
ProgramBenchVals AI0.0%–
Vibe Code Bench v1.1Vals AI71.0%–
SWE Atlas: Codebase QnAScale AI SEAL40.3%–
SWE Atlas: RefactoringScale AI SEAL48.6%–
SWE Atlas: Test WritingScale AI SEAL38.5%–
MirrorCodeEpoch AI Benchmarking Hub31.1%†–
ALE-BenchEpoch AI Benchmarking Hub1323.0–
Terminal-Bench 4.0 (AA)Artificial Analysis–55.6%
Terminal-Bench 2.1 (AA)Artificial Analysis83.2%†–
FrontierCode 1.1 (AA)Artificial Analysis–36.0%
Agents & tools
Terminal-BenchTerminal-Bench80.2%–
OSWorld-Verified 2.0OSWorld18.2%†–
APEX-AgentsMercor49.2%†–
LiveBench Long AgenticLiveBench–48.6%†
LiveBench BrowserLiveBench–93.1%†
LiveBench Agentic CodingLiveBench50.7%†–
Terminal-Bench HardArtificial Analysis51.5%†–
τ²-Bench Telecom (AA)Artificial Analysis88.6%†–
Terminal-Bench 2.1 (Vals)Vals AI68.5%–
MCP AtlasScale AI SEAL79.1%†–
HiL-BenchScale AI SEAL41.7%–
τ³-bench Bankingτ²-bench40.2%†–
τ³-Banking (AA)Artificial Analysis34.6%†–
ITBench SRE (AA)Artificial Analysis46.7%†–
Analyst Agent (AA)Artificial Analysis43.8%†–
Finance Agent v2Vals AI51.5%–
Harvey's Legal Agent Benchmark v1Vals AI6.7%–
Legal Research BenchVals AI38.5%–
Tax Agent BenchVals AI23.7%–
Terminal-Bench 2.0 (Vals)Vals AI68.5%–
Arena SearchArena (LMArena)1210–
PostTrainBenchEpoch AI Benchmarking Hub28.6%†–
ExploitBenchEpoch AI Benchmarking Hub26.5%–
GBAEvalEpoch AI Benchmarking Hub43.8%–
Vending-Bench 2Andon Labs10936.8–
AutomationBenchArtificial Analysis–60.2%
GDP.pdf (AA)Artificial Analysis–28.6%
AA-Briefcase v1.1Artificial Analysis–1402
Terminal-Bench-Science 0.1 (AA)Artificial Analysis–57.6%
Maths
FrontierMath Tiers 1–3Epoch AI Benchmarking Hub70.2%†–
FrontierMath Tier 4Epoch AI Benchmarking Hub31.7%†–
OTIS Mock AIMEEpoch AI Benchmarking Hub86.7%†–
ProofBenchVals AI54.0%†–
LiveBench MathematicsLiveBench92.8%†–
AIME (Vals)Vals AI96.3%–
AIME 2026MathArena95.8%†–
HMMT February 2026MathArena93.9%†–
MathArena ApexMathArena40.6%†–
Arena MathsArena (LMArena)1491–
Knowledge
SimpleQA VerifiedEpoch AI Benchmarking Hub51.7%†–
LiveBench 2.0 Data AnalysisLiveBench–82.3%†
LiveBench ResearchLiveBench–73.9%†
LiveBench 1.0 Data AnalysisLiveBench78.3%†–
AA-OmniscienceArtificial Analysis27.3†29.8
MMLU-ProVals AI89.9%–
LegalBenchVals AI85.3%–
CorpFinVals AI66.1%–
TaxEvalVals AI75.3%–
CaseLaw v2Vals AI68.4%–
Excel Modeling BenchmarkVals AI63.7%–
MedCodeVals AI54.9%–
MedScribeVals AI83.0%–
EBR-benchEpoch AI Benchmarking Hub19.1%†–
GDP.pdfEpoch AI Benchmarking Hub21.0%†–
AA-Omniscience: accuracyArtificial Analysis48.9%†49.1%
AA-Omniscience: non-hallucinationArtificial Analysis57.7%†62.0%
Instruction following
LiveBench LanguageLiveBench77.9%†–
LiveBench Instruction FollowingLiveBench66.7%†–
IFBenchArtificial Analysis58.6%†–
Arena Instruction FollowingArena (LMArena)1493–
Human preference
Arena TextArena (LMArena)1494–
EQ-Bench 4EQ-Bench1294–
Arena Creative WritingArena (LMArena)1482–
Arena Multi-turnArena (LMArena)1514–
Arena Longer QueriesArena (LMArena)1508–
Multimodal
Arena VisionArena (LMArena)1317–
LiveBench DesignLiveBench–82.3%†
MMMU-ProArtificial Analysis78.8%†84.7%
MMMU-Pro (Vals)Vals AI85.5%–
MortgageTaxVals AI70.3%–
SAGEVals AI56.1%–
Blueprint-Bench 2Epoch AI Benchmarking Hub24.5%–
Long context
AA-LCRArtificial Analysis78.7%†82.7%
Arena DocumentArena (LMArena)1494–
Composite
Epoch Capabilities IndexEpoch AI Benchmarking Hub156.8–
LiveBench 2.0LiveBench–77.2%†
LiveBench 1.0LiveBench76.5%†–
AA Intelligence Index v4.3.2Artificial Analysis40.7†48.1
Vals RSI IndexVals AI19.1%†–
Vals Multimodal IndexVals AI67.4%–
Safety & honesty
FORTRESSScale AI SEAL–18.2%†

Data as of 2026-10-27. Best configuration of each model; every score links to its source on the model pages.

What changed

  • Claude Opus 4.7: 1 score revision.
  • Claude Opus 4.7: 1 new result (Harvey's Legal Agent Benchmark v1).
  • Claude Opus 4.7: 1 score revision.
  • Claude Opus 4.7: 1 score revision.
  • Claude Opus 4.7: 1 score revision.
  • Claude Opus 4.7: BenchLeader Index 64.0 → 63.5.
  • Claude Opus 4.7: 1 new result (SWE Atlas: Codebase QnA).
  • Claude Opus 4.7: BenchLeader Index 64.5 → 64.0.
  • Claude Opus 4.7: 1 new result (SWE Atlas: Test Writing).
  • Claude Opus 4.7: 2 new results (Tax Agent Bench, SWE Atlas: Codebase QnA).

Rankings: Best LLM overall · Best LLM for agents · Best cheap LLMs

Claude Opus 4.7 vs GPT-6.1 Terra: questions

Is Claude Opus 4.7 better than GPT-6.1 Terra?
Claude Opus 4.7 and GPT-6.1 Terra (max) are neck and neck on quality (63.2 vs 65.9, within the ±7.5 margin). The BenchLeader Index combines every independent quality benchmark; the two are within the margin of error as of 2026-10-27, so check the category scores for your use.
Which is cheaper, Claude Opus 4.7 or GPT-6.1 Terra, and will that change?
GPT-6.1 Terra is cheaper today: $2.00 against $10.00 per million tokens, blended at three input tokens per output token. No price change has been announced for either.
Which is better for long documents?
Both take about 1.1M tokens: Claude Opus 4.7 1,000,000, GPT-6.1 Terra 1,050,000.
Which has lower latency?
Claude Opus 4.7 answers first: 1.2 s against 131.8 s to the first answer token, as Artificial Analysis measures it. Reasoning effort drives most of this: a higher setting thinks longer before it answers.
Which is faster, Claude Opus 4.7 or GPT-6.1 Terra?
GPT-6.1 Terra streams faster: 91 against 60 output tokens per second.
When was GPT-6.1 Terra released?
GPT-6.1 Terra was released on 26 Oct 2026.