On Friday, Anthropic launched its Opus 5 model, the newest version of its long-standing heavyweight model. While smaller than Fable 5, the model will be both cheaper and less restrictive than Fable, likely making it preferable in most use cases. <\/p>\n
Notably, Opus 5 actually outperforms Fable 5 on a number of benchmarks included in the announcement.<\/p>\n
Opus 5 is launching only two months after Opus 4.8, which became available on May 28. Mythos 5, Fable 5, and Sonnet 5 all launched in June, leaving only the lightweight Haiku model still waiting for an upgrade to the 5 series.<\/p>\n
In a post announcing the new model<\/a>, Anthropic emphasized that Opus 5 was \u201cmuch stronger at verifying its work and iterating carefully until it succeeds,\u201d citing benchmark testing, in which Opus 5 wrote its own computer vision pipeline in response to an incomplete prompt, among other examples.<\/p>\n Crucially, Opus 5 is also free from many of the restrictions that have dogged Fable since its release. Like its predecessor, Opus 5 is not subject to the 30-day data retention policy<\/a> that covers Fable and Mythos, which had raised concerns among some privacy-conscious users.<\/p>\n There are still meaningful safeguards on Opus, particularly around cybersecurity tasks like exploit generation and penetration testing. For instance, Opus 5 safeguards prevent it from being used to scan for vulnerabilities in a software binary, although it is permitted to search for vulnerabilities in source code, since the latter task is more likely to be used for defensive purposes.<\/p>\n Broadly, Anthropic expects these classifiers to engage 85% less often for Opus 5 than they will for Fable 5, a reflection of the lighter touch given to the less capable model.<\/p>\n Anthropic is also rolling out a new tool to make the safeguards less disruptive<\/a> when they do engage. Users can now opt in to a beta feature called Automatic Fallbacks, which will automatically route requests to a less powerful model when a prompt triggers the safety classifier. The result is that API users with the setting engaged will get a functional response instead of an error message.<\/p>\n