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Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.

  1. Claude Fable 5.2AnthropicRemove
  2. Claude Fable 5AnthropicRemove
claude-fable-5.2 vs claude-fable-5
AttributeClaude Fable 5.2claude-fable-5.2Claude Fable 5claude-fable-5
Pricing
Input$8.00 / 1M$10.00 / 1M
Output$40.00 / 1M$50.00 / 1M
Cache Write (5m)$10.00 / 1M$12.50 / 1M
Cache Write (1h)$16.00 / 1M$20.00 / 1M
Cache Read$0.20 / 1M$1.00 / 1M
Web Search$0 / 1M$0 / 1M
Context
Max context1M1M
Max outputN/AN/A
Capabilities
VisionYesYes
Function CallingYesYes
JSON ModeYesYes
StreamingYesYes
Catalogue
ProviderAnthropicAnthropic
Categorychatchat
Charge typePay As You GoPay As You Go
Released——
Description
SummaryClaude Fable 5.2 is Anthropic's most capable model for advanced technical work and open-ended problem solving, delivering significant improvements in coding, reasoning, and vision over Fable 5.1. It excels at exceptionally complex tasks, including large-scale software migrations, electronic system design, and advanced scientific and mathematical research. With up to 30% faster output generation, clearer communication, and always-on adaptive extended thinking, Fable 5.2 supports configurable reasoning effort from low to max (default high), with thinking preserved across turns. Designed for frontier engineering, scientific discovery, and ambitious long-horizon agentic workflows, it complements Opus 5.5 as a specialized option for the most demanding technical challenges.Claude Fable 5 is Anthropic's Mythos-class model, designed for autonomous knowledge work, coding, and long-running agentic workflows. It supports text, image, and file inputs with text output, includes reasoning capabilities, and features a 1M-token context window for handling complex, high-context tasks. Optimized for asynchronous and long-horizon execution, Claude Fable 5 excels at end-to-end tasks that would typically require hours, days, or weeks of human effort. It combines strong reasoning, autonomous verification and self-correction loops, and robust safeguards, making it well suited for complex research, software engineering, and large-scale knowledge work.