Re. what ever happened to Cohere’s Command-A series of models?

Reddit r/LocalLLaMA Models

Summary

Cohere launches Command A+, its first Mixture-of-Experts model, released under Apache 2.0 with efficient quantization for 1-2 GPU deployment, prioritizing practicality and open access for developers.

Hey everyone, Nick Frosst here from Cohere. A few months ago Aidan (my cofounder) [left a comment](https://www.reddit.com/r/LocalLLaMA/comments/1rf8nou/comment/o8rkdrf/) in here about our Command series and how we were working on some more powerful, open-weights models behind the scenes. We just launched Command A+ and we wanted to share it with you guys. TLDR is we built a really efficient model. It’s our first MoE model, which is exciting. There’s obvs work to do on top-line performance but it’s easily looking like one of the fastest and most responsive models in our category. We also pulled off some incredible quantization work so it runs really well on even 1 or 2 GPUs. Like with R7B, we really prioritized making the model practical, so smaller teams and devs could realistically use it to build the kind of agents we ship for our platform customers. That’s also why it’s under Apache 2.0. Just total, near unfettered access to a pretty awesome model. We’re enterprise-first but honestly, we get so much out of our open-source community that makes us more innovative and creative. The feedback you give will almost certainly influence how we think about models and product going forward…... as it already has here from getting called out the last time haha. So, don’t hold back. Share your thoughts, your projects, whatever. You can see the full details here [https://cohere.com/blog/command-a-plus](https://cohere.com/blog/command-a-plus) We appreciate you :)
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Command A+

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Cohere has released Command A+, an open enterprise AI model designed for business applications.

CohereLabs/command-a-plus-05-2026-bf16 · Hugging Face

Reddit r/LocalLLaMA

Cohere releases Command A+, an open-source model with 25B active parameters (218B total) optimized for agentic, multilingual, and reasoning-heavy tasks, supporting vision inputs and 128K context under Apache 2.0.