Claude

Claude Fable 5.1

ClaudeFlagship
ThinkingTool UseVisionStructured Output

About this model

Claude Fable 5.1 improves on Claude Fable 5 across the board, with the biggest gains in agentic coding, long-running agentic workflows, and knowledge work: long code refactors, front-end and visual...

Performance Tier

Flagship

Claude Fable 5.1 is a flagship model from Claude : the most capable in their lineup.

Best-in-class model from this provider. Highest performance across benchmarks, ideal for demanding tasks.

Pricing

This model is included in Elosia plans
Premium

Highest cost level. A long conversation can quickly use up your included usage or your balance.

Typeper 1M tokens
Input (prompt)$10.00
Output (completion)$50.00
Cache read$0.250
Cache write$12.50

Capabilities

Context Length1.0M
Max Output Tokens128K
TokenizerClaude
Inputtext, image, file
Outputtext
Release DateSeptember 1, 2026

Benchmarks

General Intelligence
MMLU
Not reported
GPQA Diamond
93.7%
Mathematics
MATH-500
Not reported
Programming
HumanEval
Not reported
SWE-bench Verified
Not reported
SWE-bench Multilingual
89.1%
Reasoning
IFEval
Not reported
ARC-AGI-2
90%
Humanity's Last Exam
60.9%
Agentic
SWE-bench Pro
81.2%

Where does Claude Fable 5.1 stand?

Compare its performance index against every other model.

View the performance leaderboard

Recommended Use Cases

CodingAnalysisResearchData Extraction

Strengths

  • Agentic coding over sessions lasting hours, including large refactors, migrations and cross-session code review
  • Knowledge work taken from a first question to a finished document, live-formula spreadsheet or slide deck, with multistep web research
  • Reads dense charts, filings and tables nested in PDFs, and operates browsers and desktop apps with recovery from failed steps
  • Abstract reasoning score verified independently by the ARC Prize Foundation
  • 1M-token context window with 128K output, always-on adaptive thinking and effort adjustable per message

Limitations

  • Anthropic rates its latency as slower, a poor fit for latency-sensitive queries
  • No forced tool use, non-default temperature or top_p values are rejected, and it may issue one tool call per turn instead of batching
  • Safety classifiers run with a wide margin and can block some benign cybersecurity or biology requests; the system card also reports it goes along with system prompts asking it to assert low-harm falsehoods more often
  • Anthropic publishes no GPQA Diamond score; the one shown comes from third-party testing

Frequently asked questions

Resources

This model may use your data for training

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