Don’t sleep on Cohere: Command A Reasoning, its first reasoning model, is built for enterprise customer service and more

I was in more meetings than usual today so I just caught up to the fact that Cohere, the Canadian startup geared co-founded by former Transformer paper author Aidan Gomez toward making generative AI products work easily, powerfully, and securely for enterprises, has released its first reasoning large language model (LLM), Command A Reasoning.

It looks to be a strong release. Benchmarks, technical specs, and early tests suggest the model delivers on flexibility, efficiency, and raw reasoning power.

Customer service, market research, scheduling, data analysis are some of the tasks Cohere says it’s built to handle automatically at scale inside secure enterprise environments.

It is a text-only model, however, but it should be easy enough to hook up to multimodal models and tools. In fact, tool use is one of its primary selling points.

While it’s open for researchers to use for non-commercial purposes, enterprises will need to pay Cohere to get access and the company doesn’t publicly list its pricing because it says it makes bespoke customization and private deployment.

Cohere was valued at $6.8 billion when it announced its latest funding round of $500 million a week and a day ago.

Tuned for enterprises

Command A Reasoning is tuned for enterprises with sprawling document libraries, long email chains, and workflows that can’t afford hallucinations.

It supports up to 256,000 tokens on multi-GPU setups, a decent size and comparable to OpenAI’s GPT-5.

The research release weighs in at 111-billion parameters, trained with tool-use and multilingual performance in mind.

It supports 23 languages out of the box, including English, French, Spanish, Japanese, Arabic, and Hindi. That multilingual depth is key for global enterprises that need consistent agent quality across markets.

The model slots directly into North, Cohere’s new platform for deploying AI agents and automations on-premises.

That means enterprises can spin up custom agents that live entirely within their infrastructure, giving them control over data flows while still tapping into advanced reasoning.

Cohere looks like it’s thought cleverly to identify some of the recurring functions across enterprises — onboarding, market research and analysis, development — and trained its model to support its agentic workflows for handling these automatically.

Controlled thinking

As with many other recent reasoning releases including Nvidia’s new Nemotron-Nano-9B-v2, Command A Reasoning introduces a token budget feature to let users or developers specify how much reasoning to allocate to specific inputs and tasks. Less budget means faster, cheaper replies. More budget means deeper, more accurate reasoning.

The Hugging Face release even exposes this tradeoff directly: reasoning can be toggled on or off through a simple parameter.

Developers can run the model in “reasoning mode” for maximum performance or switch it off for lower latency tasks—without changing models.

Excels at enterprise targeted benchmarks

So how does it perform in practice? Cohere’s benchmarks paint a clear picture.

On enterprise reasoning tasks, Command A Reasoning consistently outpaces peers like DeepSeek-R1 0528, gpt-oss-120b, and Mistral Magistral Medium.

It handles multilingual benchmarks with equal strength, important for global businesses.

The token budget system isn’t just a gimmick. In head-to-head comparisons against Cohere’s previous Command A model, satisfaction scores climbed steadily as the budget increased. Even with “instant” minimal reasoning, Command A Reasoning beat its predecessor. At higher budgets, it pulled further ahead.

The story is the same in deep research. On the DeepResearch Bench—which measures instruction following, readability, insight, and comprehensiveness—Cohere’s system came out on top against offerings from Gemini, OpenAI, Anthropic, Perplexity, and xAI’s Grok. The model excelled in turning sprawling questions into reports that are not only detailed but readable, a key challenge in enterprise knowledge work.

Beyond benchmarks, the model is wired for action. Cohere trained it specifically for conversational tool use — letting it call APIs, connect to databases, or query external systems during a task.

Developers can define tools via JSON schema and feed them into chat templates in Transformers, making it easier to integrate the model into existing enterprise systems.

That design supports Cohere’s larger bet on agentic workflows: AI systems made up of multiple coordinated agents, each handling a piece of a bigger job. Command A Reasoning is the reasoning engine that keeps those workflows coherent and on task.

Safety: built for high-stakes work

Cohere is also pitching safety as a central feature. The model is trained to avoid the common enterprise headache of over-refusal — when an AI rejects legitimate requests out of caution — while still filtering harmful or malicious content.

Evaluations focused on five high-risk categories: child safety, self-harm, violence and hate, explicit material, and conspiracy theories.

For companies looking to deploy AI in regulated industries or sensitive domains, this balance is meant to make the model more practical in day-to-day operations.

Early buy-in from large enterprises

SAP SE is one of the first major partners to integrate the model. Dr. Walter Sun, SVP and Global Head of AI, said the collaboration will enhance SAP’s generative AI capabilities within the SAP Business Technology Platform. For customers, that means agentic applications that can be customized to fit enterprise-specific needs.

Availability and licensing

Command A Reasoning is available now on the Cohere platform, and for research use on Hugging Face.

The Hugging Face repository provides open weights for research under a CC-BY-NC license, requiring users to share contact information and adhere to Cohere’s Acceptable Use Policy.

Enterprises interested in commercial or private deployments can contact Cohere’s sales team for bespoke pricing.

For enterprises, the pitch is straightforward: one model, multiple modes of deployment, fine-grained control over performance, multilingual capability, tool integration, and benchmark results that suggest it outperforms its peers.