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Cohere Launches Command A+ with 25 Billion Active Parameters

The model is available through standard API endpoints and features agentic applications. It marks the final release in the Command A family.

Published 20 September 2026 · ID 2026-09-20-cohere-launches-command-a-with-25-billion-active-parameters

Cohere has introduced Command A+, the latest and final model in its Command A series. This model integrates vision input support, reasoning, translation, and agentic task capabilities into a single framework. It represents a significant advancement in Cohere's model architecture, offering enhanced functionality for developers and businesses.

Command A+ is built as a Mixture of Experts (MoE) model, featuring 25 billion active parameters and 218 billion total parameters. This configuration allows for greater scalability and efficiency, enabling more complex tasks to be handled with improved performance. The model's design reflects Cohere's ongoing efforts to push the boundaries of AI capabilities.

The release of Command A+ follows a series of updates and improvements to Cohere's model lineup. With its MoE architecture, the model is capable of handling a broader range of tasks while maintaining high performance. This development underscores Cohere's commitment to innovation and its focus on delivering cutting-edge AI solutions to its users.

The introduction of Command A+ may influence market dynamics by setting new benchmarks for AI model capabilities. Businesses adopting this model may experience changes in operational costs, integration complexity, and vendor lock-in. The model's availability through standard API endpoints ensures broad accessibility, but users should consider governance and compliance implications as they implement it.

Cohere's release of Command A+ marks a pivotal moment in its product evolution. By combining multiple functionalities into a single model, Cohere is addressing the growing demand for versatile AI tools. As the model becomes widely adopted, its impact on industry standards and user expectations is likely to be significant.

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