Command R

Command R generates text with native citation capabilities designed specifically for retrieval-augmented generation workflows. The model architecture is optimized to identify and attribute information to source documents, automatically generating inline citations that map generated text back to retrieved context. This eliminates the need for post-processing citation extraction and enables production RAG pipelines to deliver verifiable, source-attributed responses without additional orchestration layers.

Command R supports a 128K token context window, enabling processing of entire documents, long conversation histories, and large retrieved context sets in a single API call. This architectural choice allows the model to maintain coherence across extended sequences without requiring document chunking or context windowing strategies, making it suitable for tasks requiring full-document understanding and multi-turn conversations with deep context retention.

Command R integrates with Cohere's embedding and reranking models through the same API ecosystem, enabling end-to-end RAG pipelines without external dependencies. The `/embed` endpoint generates embeddings for documents and queries, while the `/rerank` endpoint reorders retrieved results for improved relevance. This integration allows teams to build complete RAG systems using Cohere's models exclusively, with consistent API design and unified billing, reducing complexity of managing multiple vendors or models.

Command R can generate structured outputs following specified schemas or formats, enabling extraction of information into JSON, CSV, or other structured formats. The model learns to follow format constraints and produce valid structured data, reducing the need for post-processing parsing or validation. This capability is useful for data extraction, entity recognition, and API response generation where structured output is required.

Command R generates coherent, high-quality text across 10 languages with strong cross-lingual performance. The model handles language-specific nuances, grammar, and cultural context without requiring language-specific fine-tuning or separate model instances. This capability is built into the base model architecture, enabling single-model deployment for global applications without language-specific routing or model selection logic.

Command R supports tool use and function calling through Cohere's Tool Use API, enabling the model to invoke external functions, APIs, and integrations as part of agentic reasoning workflows. The model learns to recognize when a tool is needed, format function calls with appropriate parameters, and incorporate tool results back into generation. This enables multi-step reasoning where the model can decompose tasks, call external systems, and synthesize results without requiring external orchestration frameworks.

Command R is positioned as a lower-cost alternative to Command R+ while maintaining strong performance on core tasks like RAG and document analysis. The model achieves cost efficiency through architectural choices (likely reduced parameter count, optimized inference, or pruning) that trade off marginal performance on frontier tasks for significant cost reduction. This enables high-volume production deployments where throughput and cost matter more than maximal capability, making it economical for chatbots, RAG pipelines, and document analysis at scale.

Command R supports conversational chat through the `/chat` API endpoint, enabling multi-turn dialogue with automatic context management across conversation turns. The model maintains coherence across extended conversations by processing full conversation history (up to 128K tokens) in each request, enabling stateless API design where the client manages conversation state. This allows building chatbots and conversational agents without server-side session management or context persistence.

Command R can analyze and summarize documents by processing full document text within its 128K context window, extracting key information, generating summaries, and answering questions about document content. The model performs this analysis in a single pass without requiring document chunking or multi-step processing, maintaining full document context for accurate extraction and synthesis. This capability is optimized for enterprise document workflows including research synthesis, contract analysis, and report generation.

Command R is deployed as a managed API service on Cohere's cloud infrastructure, providing production-grade availability, scaling, and monitoring without requiring client-side infrastructure management. The API uses standard REST endpoints (`/chat`, `/embed`, `/rerank`) with authentication via API keys, enabling easy integration into existing applications. Cohere manages model serving, load balancing, and infrastructure scaling, allowing teams to focus on application logic rather than model deployment and operations.

Command R can be deployed in private VPC environments for organizations requiring data residency, compliance, or network isolation. Cohere offers dedicated private deployment options where the model runs in customer-controlled infrastructure or isolated cloud environments, ensuring data never leaves the customer's network. This enables enterprises to use Command R while meeting regulatory requirements (HIPAA, GDPR, SOC 2) and security policies that prohibit sending data to shared cloud APIs.

Command R supports batch processing through Cohere's batch API, enabling organizations to submit large volumes of requests asynchronously and receive results at lower cost than real-time API calls. Batch processing trades latency for cost reduction, allowing teams to process thousands or millions of requests (documents, queries, analyses) at significantly reduced per-request pricing. This is ideal for offline workflows like document analysis, content generation, and data processing where real-time response is not required.