Anthropic launches Claude Fable 5.1 — 45% cheaper for agentic work
Anthropic released Claude Fable 5.1 and Mythos 5.1, answering complaints on price, data retention and heavy-handed safeguards. Fable 5.1 costs ~25% less, up to 45% less for agentic tasks.
Based on reporting by The Verge — analysis by dalili
Anthropic says its newest AI models, Fable 5.1 and Mythos 5.1, address criticisms from customers about price, data retention, and overzealous safeguards. The company claims Claude Fable 5.1 offers stronger performance than Fable 5, but costs around 25 percent less typically and up to 45 percent less for complex agentic tasks, thanks to reduced pricing on cached data.
Early impressions have been positive. Every CEO Dan Shipper claims 'It's the strongest coding model we've used, but now it's fast, token-efficient, and crucially actually speaks like a normal person.' Box CEO Aaron Levie says his company's agent with Fable 5.1 picked up on subtleties and ambiguities in data that Fable 5 missed.
Fable 5.1 also has 'more precise safeguards' that Anthropic says are less likely to block basic biology questions than Fable 5. Anthropic also explained its progress on data retention, saying that Enterprise Frontier Safeguards offer 'complete privacy' by storing data on the customer's cloud servers.
Anthropic says it's 'now allowing Fable 5.1 to be used for identifying software vulnerabilities,' but it will still redirect some cybersecurity tasks to Opus models. Claude Fable 5.1 is now available on all platforms, while Mythos 5.1 is available to Project Glasswing participants only.
Key takeaways
- Fable 5.1 costs 25% less typically, up to 45% less for agentic tasks
- More precise safeguards — less likely to block basic questions
- Enterprise Frontier Safeguards store data on customer's cloud
- Fable 5.1 now allows software vulnerability identification
- Mythos 5.1 available only to Project Glasswing participants
Why it matters
The AI model price war intensifies. Anthropic is directly competing with OpenAI and Google on cost for agentic workloads, which could accelerate enterprise adoption of AI agents.
