Anthropic’s newest model, Opus 5.5, was released on Tuesday, setting a new state-of-the-art in coding and knowledge work performance, according to the company. Notably, the company says, the release outpaces the larger Fable model in many benchmarks, and succeeded in a number of informal tasks that Fable failed to complete.
The new model is also significantly cheaper than its predecessor. Output tokens will be charged at $20 per million tokens for Opus 5.5, compared to $25 for the previous model. Other metrics have similar price drops. The model is also faster to run, reflecting an overall drop in the compute required to serve it.
The new version also makes significant changes to how Opus communicates, with the Opus 5.5 less likely to use jargon and more likely to put important information at the start of its messages.
The launch comes just two months after the release of Opus 5 on July 24th. According to the announcement, Sonnet 5.5 and Haiku 5.5 will be released “in the coming weeks,” with similar performance improvements.
Anthropic says that Opus 5.5 is comparable to Mythos in its biology and cybersecurity capabilities, so its release is subject to the same safeguards as the company’s Fable model. Those safeguards limit how much the models can be used to discover exploits in compiled programs or developing recognizable biological weapons, among other tasks.
Opus 5.5 is Anthropic’s first model release since CEO Dario Amodei embraced calls to pace the frontier, deliberately slowing down progress on AI capabilities to match the rate of progress on alignment.
“I have become convinced that fully addressing the risks requires even more prudence,” Amodei wrote in a post earlier this month, “not just investing in risk prevention, but pacing the rate of capabilities advancement so that risk prevention has time to keep up.”
Opus 5.5’s safety training was broadly similar to its predecessors, with alignment testing and pre-release evaluation by outside organizations like METR and Frontier Design. But Anthropic emphasized that more advanced training and evaluation systems were already being prepared for future models, including improved security and monitoring systems.
“As AI becomes more capable, public policy should play a larger role in making sure the systems people rely on are safe. That capacity takes time to build, and we’ve started to put the infrastructure in place to support it,” the blog post reads. “We expect to share more details on these efforts soon.”