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Today, we’re releasing Command A+ open-source. A mixture-of-experts (MoE) model, Command A+ is an efficient, versatile, and privately deployable LLM built for high-performance agentic tasks with minimal compute overhead. Born from a year of deploying North with our customers, it surpasses every previous generation in the Command series and unifies their capabilities into a single scalable model. Now freely available under an Apache 2.0 license, Command A+ advances Cohere’s mission to make sovereign AI a technological reality — giving developers direct access to enterprise-grade agentic capabilities across experimentation, deployment, and production workflows. Visit Hugging Face to download the weights - available in several near lossless quantizations - and read our implementation guides. For a dedicated, managed inference environment, deploy Command A+ in Model Vault today. Snapshot Northwards For the past year, North — Cohere’s integrated enterprise workspace for building and deploying agentic AI — has been the driving force behind much of our innovation. Through that work, we set out to build a unified model for customers that simplifies deployment, can run locally, and synthesizes capabilities from across the Command family. The work is already paying off. Read how our customers have been using North to transform their operations. However, sovereign AI is much bigger than Cohere. Empowering engineers with models that they can run, control, and adapt themselves is the most acute challenge facing this generation of AI. We’ve optimized Command A+ for practical, developer-focused use, including support for low-bit quantization, efficient inference, and integration across open inference frameworks. AI independence for all. We can’t wait to see what the community builds. Command, consolidated Command A+ outperforms previous Command A models in key dimensions of enterprise workloads, including multimodal understanding, retrieval, long-horizon, and complex reasoning. Image 2: comparing the capabilities of Command A+ with other models in the Command A family. Compared with Command A Reasoning, 𝜏²-Bench Telecom scores improved from 37% to 85%, with agentic coding performance on Terminal-Bench Hard reaching 25% from 3%. Gains were also achieved on non-agentic reasoning, instruction following, and other code generation tasks. Image 3: Performance for Command A+ and Command A Reasoning on a range of popular open-source benchmarks. See footnote for further details. 1 Command A+ performs strongly within North applications, reflecting its original design goals. Agentic Question Answering accuracy and spreadsheet analysis quality improved by 20% and 32% over Command A Reasoning, respectively. Memory performance — testing North’s skill in reasoning across conversations and stored data — scored 54% with Command A+ compared to 39% with Command A Reasoning. For multimodal understanding and reasoning, Command A+ achieved 63% on MMMU Pro and 75.1% on MMMU, (compared with 65.3% for Command A Vision for the latter). MathVista scores increased from 73.5% to 80.6%, and CharXiv reasoning improved from 46.9% to 52.7%, reflecting broad gains across document understanding tasks. Command A+ significantly expands multilingual capability, broadening language coverage from 23 to 48 languages and recording gains in machine translation and multilingual reasoning. Image 6: comparison of multilingual performance for Command A+ and…