Anthropic’s latest hybrid AI model can independently perform tasks for extended periods.

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While Claude Opus 4 will be available exclusively to paying Anthropic customers, the second model, Claude Sonnet 4, will cater to both paid and free-tier users. Opus 4 is positioned as a robust, large model designed for complex challenges, whereas Sonnet 4 is characterized as a smart, efficient model for everyday tasks.

Both new models are hybrid, capable of providing either a quick reply or a more detailed, reasoned response based on the nature of the request. While formulating a response, both models can utilize web searches or other tools to enhance their output.

AI companies are currently engaged in a competition to develop genuinely useful AI agents that can plan, reason, and execute complex tasks reliably and independently, says Stefano Albrecht, director of AI at the startup DeepFlow and coauthor of Multi-Agent Reinforcement Learning: Foundations and Modern Approaches. This often involves the autonomous use of the internet or other tools. However, there are still safety and security challenges to address. AI agents powered by large language models can behave unpredictably and perform unintended actions, which can become even more problematic when trusted to operate without human oversight.

“The more agents are able to proceed autonomously over extended periods, the more beneficial they will be, especially if I have to intervene less frequently,” he explains. “The new models’ ability to use tools simultaneously is intriguing—that could save time, making them more effective.”

As an illustration of the safety issues AI companies are working to resolve, agents sometimes take unexpected shortcuts or exploit loopholes to achieve their objectives. For example, they might reserve every seat on a plane to guarantee a user’s ticket or employ creative strategies to win a chess game. Anthropic states it has reduced this behavior, known as reward hacking, in both new models by 65% compared to Claude Sonnet 3.7. This was accomplished by closely monitoring problematic behaviors during training and improving both the AI’s training environment and evaluation methods.

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