Anthropic Claude Fable 5 for long-running coding, research, and knowledge-work tasks. Anthropic retains prompts and outputs for 30 days under its Covered Models policy; Zero Data Retention is not available.
Added Jun 9, 2026
Context Window
1.0M
Max Output
128.0K
Input Price (Auto)
$10.00/1M
Output Price (Auto)
$50.00/1M
Cache Read (Auto)
$1.00/1M
Capabilities
Benchmarks
Benchmarks
Performance metrics and benchmarks
Sourced from Artificial Analysis.
Intelligence Index
49.6
Coding Index
76.5
Agentic Index
51.0
Agentic work
AutomationBench-AA
Workflow automation with guardrail penalties
54.1%
Better than 80% of models compared
AutomationBench-AA Tasks Completed
Fully completed workflows without guardrail violations
24.7%
Better than 39% of models compared
Harvey LAB-AA
Legal agentic work criterion pass rate
93.6%
Better than 93% of models compared
AA-Briefcase
Agentic knowledge work (Elo)
1544 Elo
Better than 93% of models compared
GDPval-AA v2
Economically valuable tasks (Elo)
1595 Elo
Better than 92% of models compared
Document reasoning
GDP.pdf
Professional PDF reasoning: all-pass rate
24.0%
Better than 88% of models compared
AA-LCR v1.1
Long context reasoning with updated grading
82.3%
Better than 95% of models compared
MLCR-AA
Medical long-context reasoning
64.4%
Better than 98% of models compared
Reasoning
HLE
Humanity's Last Exam
55.5%
Better than 99% of models compared
IFBench
Instruction-following benchmark
63.5%
Better than 74% of models compared
CritPt
Research-level physics reasoning
28.6%
Coding
Terminal-Bench v4.0
Practical coding and terminal tasks
42.4%
Better than 93% of models compared
SciCode
Python programming for scientific computing
61.0%
Better than 99% of models compared
Knowledge
AA-Omniscience Accuracy
Proportion of correctly answered questions
65.3%
AA-Omniscience Hallucination Rate
Rate of incorrect answers among non-correct responses
63.6%
Legacy benchmarks
GPQA Diamond (legacy)
Graduate-level scientific reasoning
92.6%
Better than 94% of models compared
Terminal-Bench Hard (legacy)
Agentic coding and terminal use
62.9%
Better than 99% of models compared
T²-Bench Telecom (legacy)
Conversational AI agents in dual-control scenarios
98.5%
Better than 99% of models compared
AA-LCR (unversioned / legacy)
Long context reasoning evaluation
82.3%
Better than 95% of models compared
GDPval-AA (unversioned / legacy)
Economically valuable tasks
56.6%
Last updated Sep 20, 2026
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