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Home » Anthropic launches Opus 5 | TechCrunch
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Anthropic launches Opus 5 | TechCrunch

IQ TIMES MEDIABy IQ TIMES MEDIAJuly 24, 2026No Comments2 Mins Read
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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.

Notably, Opus 5 actually outperforms Fable 5 on a number of benchmarks included in the announcement.

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.

In a post announcing the new model, Anthropic emphasized that Opus 5 was “much stronger at verifying its work and iterating carefully until it succeeds,” citing benchmark testing, in which Opus 5 wrote its own computer vision pipeline in response to an incomplete prompt, among other examples.

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 that covers Fable and Mythos, which had raised concerns among some privacy-conscious users.

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.

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.

Anthropic is also rolling out a new tool to make the safeguards less disruptive 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.

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