Anthropic has launched a new subscription plan for its Claude AI chatbot, priced at $200 per month. Announced on April 9, this Max plan targets users who heavily rely on Claude for their work, providing greater access to key tasks. It offers priority access to the platform’s latest features and models, addressing feedback from active users who sought more extensive access to Claude. With the Max plan, users benefit from up to 20 times higher usage limits, enabling them to maintain workflow efficiency on demanding projects.
The subscription includes two tiers: “Expanded Usage” at $100 monthly for regular users and “Maximum Flexibility” at $200 for those needing daily access. According to Anthropic, increased usage allows for deeper collaboration with Claude, whether for professional purposes or personal projects. This flexibility is particularly valuable for tasks such as writing, software development, and data analysis.
Illustrating the competitive landscape, OpenAI previously introduced a similar $200 “research grade” ChatGPT plan in December. Furthermore, Anthropic recently launched “Claude for Education,” which aids universities in integrating AI technologies into their teaching and administrative systems. This shift towards educational applications marks Anthropic’s focus on developing versatile AI solutions primarily for enterprise needs rather than consumer electronics or entertainment.
At a recent HumanX conference, Chief Product Officer Mike Krieger emphasized the company’s dedication to merging innovative research with effective product development. He stated that their mission is to enhance productivity across various tasks in both small businesses and larger corporations.
In a related discussion, experts examined Meta’s recent Llama AI model, which boasts a remarkable 10 million token context window, significantly surpassing that of Google’s Gemini 2.5. Ilia Badeev, a data science leader, highlighted Llama 4’s efficiency and expanded context capabilities, suggesting potential advancements in performance that could outshine current leading models in specific applications.
