sale/claude-fable-5-1

The discounted version is powered by a hybrid cloud infrastructure combining AWS, official providers, Azure, and Vertex AI. It uses clean, native APIs with no reverse-engineered interfaces.

Claude Fable 5.1 is Anthropic’s flagship large model, specially designed for long-duration autonomous agents, cutting-edge scientific research, and large-scale system reengineering. As Anthropic’s most advanced autonomous task-execution model to date, Fable 5.1 is tailored for complex tasks that require continuous operation for hours or even days. The model features an exceptional “self-correcting and anti-laziness” mechanism: in long-chain code refactoring or system debugging, it can pinpoint underlying bugs. Coupled with significantly reduced cache read rates, Fable 5.1 serves as the ultimate industrial-grade foundation for building fully automated coding agents, deep knowledge work pipelines, and research-assisting agents.

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Core Capabilities

Breakthroughs in Cutting-Edge Science and Code Agents: Achieved 52.6% on the Terminal-Bench-Science 0.1 scientific research benchmark—doubling the performance of the previous generation (24.7%). In the Terminal-Bench 4.0 complex agentic coding test, it scored 55.8%, comprehensively breaking industry records.

Long-Term Continuous Reasoning and “No-Lazy” Self-Checking: Fable 5.1 excels at maintaining clear reasoning logs during unattended tasks lasting several hours—such as continuous ML training and debugging lasting up to 38 hours—adjusting its priorities autonomously and rarely resorting to baseless assumptions or perfunctory behavior.

Precise False Positive Control: The next-generation cybersecurity and biology guardrails have enhanced accuracy, reducing false rejection rates caused by safety policies by approximately 60%.

Multilevel Effort Cost Optimization: Fable 5.1 natively supports dynamic switching between Low/Medium/High inference effort levels. In Low-Effort mode, it achieves an IQ surpassing that of the previous flagship model, dramatically reducing the actual computational costs for routine tasks.