# Command R+ Command R+ is a large language model developed by Cohere, released in April 2024 as a larger version of Command R, designed for enterprise applications with advanced retrieval-augmented generation and multilingual support. Command R+ is a [large language model](https://www.wikiprompt.org/wiki/large-language-model) developed by the Canadian AI company Cohere. Released in April 2024, it is the larger sibling of the earlier Command R model, offering increased parameter count and enhanced capabilities. The model is positioned for enterprise use cases, emphasizing accuracy, efficiency, and multilingual performance. It is designed to be deployed in production environments, with a focus on tasks such as [generative AI](https://www.wikiprompt.org/wiki/generative-ai) applications, document analysis, and conversational agents. The model builds on the architectural foundations common to modern large language models, including the [transformer](https://www.wikiprompt.org/wiki/transformer) architecture. It is trained using techniques from [deep-learning](https://www.wikiprompt.org/wiki/deep-learning) and [machine-learning](https://www.wikiprompt.org/wiki/machine-learning), with a particular emphasis on [retrieval-augmented generation](https://www.wikiprompt.org/wiki/retrieval-augmented-generation) (RAG). Command R+ is notable for its ability to ground responses in external knowledge sources, reducing hallucinations and improving factual accuracy. It supports multiple languages and is optimized for cost-efficient inference, making it attractive for businesses seeking to integrate AI into their workflows. ## Architecture and Training Command R+ employs a transformer-based architecture, similar to other state-of-the-art models. While Cohere has not publicly disclosed the exact parameter count, it is confirmed to be larger than Command R, which had 35 billion parameters. The model uses techniques such as [multi-head-attention](https://www.wikiprompt.org/wiki/multi-head-attention) and [positional-encoding](https://www.wikiprompt.org/wiki/positional-encoding) to process sequences effectively. Training involved a diverse dataset of text from the web, books, and other sources, with a focus on multilingual coverage. The training process likely incorporated [adam-optimizer](https://www.wikiprompt.org/wiki/adam-optimizer) and [learning-rate-schedule](https://www.wikiprompt.org/wiki/learning-rate-schedule) methods, common in large-scale model training. Cohere also emphasizes alignment with human preferences, possibly using [reinforcement learning from AI feedback](https://www.wikiprompt.org/wiki/rlaif) or similar techniques, although specific details are not fully public. The model is designed to be efficient, with optimizations for inference speed and memory usage. ## Capabilities and Features Command R+ excels in retrieval-augmented generation, allowing it to access and cite external documents during generation. This makes it particularly useful for enterprise applications such as customer support, legal document analysis, and knowledge management. It supports a wide range of languages, with strong performance in English, French, Spanish, German, Italian, Portuguese, Japanese, Korean, and Chinese, among others. The model also features a tool-use capability, enabling it to interact with external APIs and databases. This allows it to perform tasks like querying databases, calling web services, and executing code. Command R+ is available through Cohere's API and can be deployed on various cloud platforms, including [amazon-web-services](https://www.wikiprompt.org/wiki/amazon-web-services), [azure](https://www.wikiprompt.org/wiki/azure), and [google-cloud](https://www.wikiprompt.org/wiki/google-cloud). ## Performance and Benchmarks In internal evaluations, Command R+ demonstrated competitive performance against other large models, such as those from [openai](https://www.wikiprompt.org/wiki/openai) and [anthropic](https://www.wikiprompt.org/wiki/anthropic). It showed particular strength in multilingual tasks and RAG-based question answering. Cohere reported that Command R+ achieved high scores on benchmarks like MMLU (Massive Multitask Language Understanding) and HellaSwag, though exact figures are not publicly detailed. The model is also designed to be cost-effective, with lower inference costs compared to similarly sized models. This is achieved through architectural optimizations and efficient serving infrastructure. As of 2024, Command R+ is considered one of the leading enterprise-focused language models. ## Deployment and Availability Command R+ is available through Cohere's API, with pricing based on usage. It can also be deployed on cloud platforms such as [AWS Trainium](https://www.wikiprompt.org/wiki/aws-trainium)-based instances, [oracle-cloud](https://www.wikiprompt.org/wiki/oracle-cloud), and [azure](https://www.wikiprompt.org/wiki/azure). Cohere offers both managed and self-hosted options, allowing enterprises to choose based on their data privacy and compliance needs. The model is part of Cohere's broader product suite, which includes the smaller Command R and other specialized models. Cohere positions Command R+ as a solution for businesses that require high accuracy and reliability in AI-powered applications. As of 2025, the model continues to be updated, with Cohere releasing new versions and improvements. ## Reception and Impact Command R+ has been well received in the enterprise AI community, praised for its strong RAG capabilities and multilingual support. It has been adopted by various companies for tasks such as customer service automation, document processing, and knowledge management. The model's emphasis on grounding and citation has been highlighted as a key differentiator in an industry often criticized for hallucination issues. Cohere's focus on enterprise needs, rather than consumer chatbots, has positioned Command R+ as a serious contender in the AI market. It competes with models from [google-deepmind](https://www.wikiprompt.org/wiki/google-deepmind), [ai21-labs](https://www.wikiprompt.org/wiki/ai21-labs), and [inflection-ai](https://www.wikiprompt.org/wiki/inflection-ai), among others. As of 2025, Command R+ remains a relevant and widely used model in the enterprise sector. --- Source: https://www.wikiprompt.org/wiki/command-r-plus License: CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/) Last updated: 2026-09-12T22:20:10.509352+00:00