Command R

Generates coherent, contextually-aware text responses using a transformer-based architecture optimized for retrieval-augmented generation workloads. The model processes up to 128K tokens of input context (documents, retrieved passages, conversation history) in a single forward pass, enabling it to synthesize information from large document collections without requiring intermediate summarization or context truncation. This architecture allows the model to maintain coherence across extended retrieval results while keeping latency and cost lower than larger alternatives.

Automatically generates citations that map generated text back to specific source documents or passages provided in the input context. The model learns during training to identify which retrieved passages support each claim in its response, embedding citation markers directly into the output text. This capability eliminates the need for post-hoc citation extraction or external attribution systems, enabling developers to immediately surface source documents to end-users without additional processing.

Generates dense vector embeddings for text using the Embed 4 model, which can be used for semantic search, similarity comparison, and clustering. Embeddings are generated through a separate API endpoint and can be stored in vector databases for retrieval-augmented generation pipelines. This capability enables the full RAG stack (retrieval + ranking + generation) within the Cohere ecosystem.

Ranks and scores retrieved documents based on semantic relevance to a query using Cohere's Rerank 3.5 or Rerank 4 models. This capability improves retrieval quality by re-ranking initial search results (from keyword search, BM25, or embedding similarity) based on semantic understanding. Reranking is typically applied after initial retrieval but before passing documents to the generation model, improving the quality of context available to Command R.

Processes multiple requests in a single batch operation, optimizing throughput for high-volume workloads where latency is less critical than cost and efficiency. Batch requests are queued and processed during off-peak hours, typically at lower cost than real-time API calls. This capability is ideal for overnight processing, periodic report generation, or bulk document analysis.

Generates fluent, contextually appropriate text in 10 supported languages using a single unified model trained on multilingual data. The model automatically detects input language and generates responses in the same language without requiring language-specific model variants or explicit language tags. This capability enables developers to build single-model applications serving global audiences without maintaining separate language-specific inference pipelines.

Enables the model to invoke external tools, APIs, or functions by generating structured function calls within its response. The model learns to recognize when a user request requires external action (e.g., database lookup, API call, calculation) and outputs a machine-readable function call specification that developers can parse and execute. This capability allows Command R to act as the reasoning engine in multi-step agentic workflows where the model decides what actions to take and the application layer executes those actions.

Analyzes and summarizes long documents (up to 128K tokens) while preserving key information, structure, and context. The model can extract key points, answer specific questions about document content, and generate summaries at various levels of detail without losing critical information. This capability leverages the 128K context window to process entire documents in a single pass rather than requiring chunking or hierarchical summarization.

Provides flexible API-based access to Command R through two deployment tiers: free trial keys (rate-limited, non-production) and production pay-as-you-go billing. Developers can prototype and test applications using trial keys without upfront costs, then scale to production by upgrading to a paid account with per-token or per-request billing. This model eliminates infrastructure management overhead and allows cost scaling based on actual usage.

Provides fully-managed, dedicated inference infrastructure through Cohere's Model Vault service, offering isolated instances without multi-tenancy. Organizations can deploy Command R on dedicated hardware with fixed or flexible pricing, choosing between hourly billing (for variable workloads) and monthly billing (for predictable loads). This deployment option eliminates shared-resource contention and provides SLA guarantees for enterprise customers.

Enables deployment of Command R within customer-controlled VPCs on major cloud providers (AWS, Azure, GCP) or on-premises infrastructure. This deployment option maintains data isolation and compliance with regulations requiring data residency or network isolation. Cohere manages the model and infrastructure while the customer controls network access, security policies, and data flow.

Generates responses in a streaming fashion, returning tokens incrementally as they are produced rather than waiting for the complete response. This capability enables real-time user experiences where text appears character-by-character in the UI, reducing perceived latency and improving responsiveness. The streaming API maintains the same context and citation capabilities as batch generation.

Manages multi-turn conversations by accepting message arrays with role-based formatting (user, assistant, system) that maintain conversation context across multiple API calls. The model uses this conversation history to understand context, maintain coherence, and avoid repeating information. This capability simplifies chatbot development by eliminating the need for manual context concatenation or custom conversation state management.