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Anthropic just introduced Claude Fable 5 and Claude Mythos 5 🚀

The interesting part:
Fable 5 and Mythos 5 are basically the same underlying model, but with different access and safety layers.

✅ Claude Fable 5 is the version available for general users. Anthropic says it is their most capable generally available model so far, especially for:

• Software Engineering
• Long-running agentic tasks
• Vision-based work
• Knowledge work
• Scientific reasoning
• Long-context workflows

One example they shared is pretty wild:
Stripe tested it on a 50M-line Ruby codebase, and it completed a migration in one day that would normally take a team more than two months. 🤯

But because the model is much stronger, Anthropic is adding stricter safeguards.

For risky areas like cybersecurity, biology/chemistry, and model distillation, some requests will automatically fall back to Claude Opus 4.8 instead of Fable 5. ⚠️

Claude Mythos 5 is the restricted version, mainly for trusted Cyber defenders, infrastructure providers, and later selected biology researchers. It has some safeguards lifted depending on the access program.

Pricing is 💸:
• $10 / 1M input tokens
•
$50 / 1M output tokens


[ Read More ] : https://www.anthropic.com/news/claude-fable-5-mythos-5

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#AI #Claude #LLM
@ProgrammingTip
Claude Opus 5.5 is now on the Claude Platform 🚀

Anthropic introduced Claude Opus 5.5, a lower-cost frontier model available in Claude Code and through the Claude Platform.

That matters if your agent workload has been split between a high-end model for hard tasks and cheaper models for everything else. A lower-cost Opus tier can change where that handoff happens.

What to check:

• Run your existing eval set, not a few cherry-picked prompts.
• Measure tool-call accuracy and recovery after a failed call.
• Compare total agent cost: tokens, retries, and human review time.

For Claude Code users, model choice is now a practical repo-level config decision, not just a benchmark chart.

[ Blog ] :
https://www.anthropic.com/claude-opus-5-5

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#AI #LLM #Claude #ClaudeCode
@ProgrammingTip
Claude Opus 5.5 is available in the API 🚀

Anthropic released Claude Opus 5.5, and it is available through the Claude API.

The practical pitch is simple: lower typical token costs than Opus 5, plus a faster mode when response time matters more than squeezing out the last bit of reasoning.

What to check in your evals:

• Run the same tool-use and coding tasks against Opus 5.
• Measure latency separately for normal and faster mode.
• Track input and output tokens, not just the model's listed price.

A cheaper high-end model changes agent architecture decisions. Some workflows that needed routing to a smaller model may now fit under one stronger default.

[ Read More ] :
https://www.anthropic.com/claude/opus

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#AI #LLM #Claude #API
@ProgrammingTip
Claude Sonnet 5.5 is built for coding agents 🚀

Anthropic released Claude Sonnet 5.5 for the Claude Platform. It targets the work developers actually hand to coding agents: navigating a repo, making changes across files, using tools, and checking the result.

What changed:

• Faster agentic work: aimed at shorter tool loops and less idle time while an agent investigates a codebase.
• Lower-cost option: positioned for teams that need to run coding tasks repeatedly, not just ask one-off questions.
• Production focus: Anthropic calls out software engineering and multi-step agent workflows directly.

If your agent spends more time calling tools than writing code, model latency and per-task cost matter as much as benchmark scores. Test it on a real issue queue, with your actual tool permissions.

[ Read More ] :
https://www.anthropic.com/claude-sonnet-5-5

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#AI #LLM #Claude #CodingAgents
@ProgrammingTip
Claude Haiku 5.5 is available on the Claude Platform 🚀

Anthropic released Claude Haiku 5.5, its lower-cost small model, on the Claude Platform.

The model ID is claude-haiku-5-5. This is the one to benchmark when your workload has lots of short agent turns: classification, extraction, routing, simple tool calls, and coding tasks where latency matters more than maximum reasoning depth.

What to test first:

• Replace your fast-path model in staging.
• Measure tool-call accuracy, not just chat quality.
• Set a harder-model fallback for failed evaluations.

Small models pay off when they handle the boring 80 percent cleanly. Keep the expensive model for the weird tickets.

[ Read More ] :
https://www.anthropic.com/claude-haiku-5-5

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#AI #LLM #Claude #API
@ProgrammingTip