Anthropic has replaced Claude Sonnet 4.6 with Sonnet 5 as its default medium-sized model for free and Pro users, emphasizing stronger agentic performance at lower cost than larger models. The update narrows the gap to Opus 4.8 while introducing introductory pricing through August 31, 2026, and expanded rate limits.
According to Anthropic, the model can formulate plans, operate tools including browsers and terminals, and function independently in ways that reportedly demanded bigger, costlier systems only months earlier.
Fable 5 represents a more cautious alternative that appeared for customers only briefly before U.S. government intervention halted distribution; Anthropic states it is pursuing renewed availability going forward.
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Anthropic launched Fable 5.1 and Mythos 5.1 on September 1, 2026, claiming stronger performance at 25 percent lower typical cost and up to 45 percent savings on agentic work. The release also brings refined safeguards, enterprise data-retention improvements rolling out this fall, and expanded but still limited use for software vulnerability identification.
Anthropic released Claude Haiku 5.5, its cheapest and fastest small model, at $0.10 per million input tokens and $0.50 per million output tokens for prompts up to 100,000 tokens. Anthropic says it costs around 75% less to run than Haiku 4.5 on average and scores far higher on its benchmarks.
Anthropic's Claude service is down worldwide due to authentication system failures affecting multiple products. More than 1,500 reports appeared on Downdetector within minutes on September 29.
Anthropic introduced Claude Sonnet 5.5 at unchanged mid-tier rates of $2 per million input tokens and $10 per million output tokens. The model exceeded Opus 5.5 on Terminal-Bench 4.0 yet consumed more tokens at peak effort settings.
Anthropic launched Claude Opus 5.5 on September 22, 2026 as the first model in its Claude 5.5 family, claiming it matches Claude Fable 5.1 performance while running 40% cheaper and 30% faster than Opus 5. The release highlights continued pressure on AI developers to improve efficiency without sacrificing quality.
