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Claude Opus 4.8 Thinking

anthropic/claude-opus-4.8:thinking
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Claude Opus 4.8 Thinking

anthropic/claude-opus-4.8:thinking
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Anthropic Claude Opus 4.8 with thinking enabled.

Added May 28, 2026

Context Window

1M

Max Output

128K

Input Price (Auto)

$5.00/1M

Output Price (Auto)

$25.00/1M

Cache Read (Auto)

$0.50/1M

Capabilities

Benchmarks

Performance metrics and benchmarks

Sourced from Artificial Analysis.

Intelligence Index

41.8

Better than 93% of models compared

Coding Index

74.3

Better than 89% of models compared

Agentic Index

41.9

Better than 83% of models compared

Agentic work

AutomationBench-AA

Workflow automation with guardrail penalties

45.6%

Better than 64% of models compared

Harvey LAB-AA

Legal agentic work criterion pass rate

91.1%

Better than 73% of models compared

AA-Briefcase

Agentic knowledge work (Elo)

1321 Elo

Better than 75% of models compared

GDPval-AA v2

Economically valuable tasks (Elo)

1438 Elo

Better than 82% of models compared

Document reasoning

GDP.pdf

Professional PDF reasoning: all-pass rate

22.8%

Better than 81% of models compared

AA-LCR v1.1

Long context reasoning with updated grading

77.7%

Better than 80% of models compared

MLCR-AA

Medical long-context reasoning

45.6%

Better than 87% of models compared

Reasoning

HLE

Humanity's Last Exam

48.7%

Better than 96% of models compared

IFBench

Instruction-following benchmark

62.2%

Better than 73% of models compared

CritPt

Research-level physics reasoning

20.9%

Coding

Terminal-Bench v4.0

Practical coding and terminal tasks

21.7%

Better than 79% of models compared

SciCode

Python programming for scientific computing

54.4%

Better than 75% of models compared

Knowledge

AA-Omniscience Accuracy

Proportion of correctly answered questions

48.8%

AA-Omniscience Hallucination Rate

Rate of incorrect answers among non-correct responses

39.3%

Legacy benchmarks

GPQA Diamond (legacy)

Graduate-level scientific reasoning

92.0%

Better than 93% of models compared

Terminal-Bench Hard (legacy)

Agentic coding and terminal use

58.3%

Better than 98% of models compared

T²-Bench Telecom (legacy)

Conversational AI agents in dual-control scenarios

94.4%

Better than 93% of models compared

AA-LCR (unversioned / legacy)

Long context reasoning evaluation

77.7%

Better than 80% of models compared

GDPval-AA (unversioned / legacy)

Economically valuable tasks

46.9%

Last updated Sep 27, 2026

Artificial Analysis

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