models/anthropic/claude-fable-5
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Anthropic·active

Anthropic: Claude Fable 5

Anthropic's flagship model. Long-context specialist with 1M window.

Overall score
4.62
/5.00 · ranked #16
Input
$10.00
per 1M tokens
Output
$50.00
per 1M tokens
Context
1M
tokens
Blended
$40.00
3:1 out:in ratio

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Scores by test

Methodology →
Structured Output
5.0
Strategic Analysis
5.0
Constrained Rewriting
5.0
Creative Problem Solving
5.0
Tool Calling
5.0
Faithfulness
5.0
Classification
4.0
Long Context
5.0
Safety Calibration
1.0
Persona Consistency
5.0
Agentic Planning
5.0
Multilingual
5.0
Tabular Data
5.0
AIME 2025
99.7
SciCode
60.2
APEX Agents
45.0
SWE-bench Verified
95.0
Epoch Capabilities Index (ECI)
161.6

What you need to know

Claude Fable 5 is a high-reasoning model optimized for complex autonomy and technical precision. Its primary differentiator is its near-perfect performance in mathematical and software engineering benchmarks, specifically scoring 99.7% on AIME 2025 and 95% on SWE-bench Verified. These numbers indicate a model capable of solving advanced competitive math and executing production-level software engineering tasks with minimal failure.

The model maintains a consistent 5/5 internal rating across almost all operational categories, including tool calling, agentic planning, and long context handling within its 1M token window. However, there is a critical failure in safety calibration, where it scored 1/5. This suggests the model lacks standard guardrails and may produce unfiltered or unsafe outputs, requiring developers to implement their own robust moderation layers.

At $10.00 per million input tokens and $50.00 per million output tokens, this is a premium-priced model. The cost is high, but the pricing aligns with its capability as a top-tier reasoning engine, ranking 16th out of 130 models. You are paying for high-fidelity structured output and strategic analysis rather than simple text generation.

Use this model if your application requires autonomous agentic planning, complex codebase manipulation, or high-stakes mathematical reasoning. Skip this model if your use case requires strict safety alignment out of the box or if you are performing simple classification tasks where a cheaper model would suffice.

Strengths — Top 3

Structured Output5.0/5.0
Strategic Analysis5.0/5.0
Constrained Rewriting5.0/5.0

Relative weaknesses — Bottom 3

Safety Calibration1.0/5.0
Classification4.0/5.0
Structured Output5.0/5.0

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