Cohere Command R+
AI104B-parameter LLM optimized for long-context conversational tasks, RAG, grounded generation, and multi-step tool use (open research release).
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Cohere Command R+
AI
104B-parameter LLM optimized for long-context conversational tasks, RAG, grounded generation, and multi-step tool use (open research release).
-(0 Reviews)
Free Available
Starting from Free
Premium plans available
About Cohere Command R+
Command R+ is a large language model from Cohere optimized for conversational interaction, long-context inputs (up to ~128K tokens), retrieval-augmented generation (RAG), and both single-step and multi-step tool orchestration. The model supports grounded generation modes with citation spans, a "fast" citation mode for fewer tokens, and prompt templates that include optional system preambles to control task, context, and output style. CohereLabs released an open-weights research checkpoint (c4ai-command-r-plus-08-2024) that enables experimentation with tool use, multilingual capabilities, and RAG workflows.
Key Features
Large-Scale Model: 104 billion parameter research checkpoint offering high-capacity reasoning and generation for complex tasks.
Very Long Context: Supports extremely long context windows (~128K tokens / 131,072) to handle long documents, multi-turn conversations, and extended RAG inputs.
Retrieval-Augmented Generation: Built-in support for RAG workflows — takes conversation plus retrieved document snippets and generates grounded, citation-aware responses.
Grounded Generation Modes: Multiple answer modes including a "fast" citation mode that emits answers with grounding spans to reduce token usage while trading off some grounding accuracy.
Single- and Multi-Step Tool Use: Native prompt templates and workflow support for both single-step tool calls and multi-step tool orchestration, enabling complex pipelines that combine multiple tools across steps.
Multilingual Training and Evaluation: Trained on 23 languages and explicitly evaluated across 10 languages (e.g., English, French, Spanish, German, Portuguese, Japanese, Korean, Arabic, Chinese).
Open Research Release & Integrations: Available as an open-weights research release on Hugging Face (c4ai-command-r-plus-08-2024) with hosted demo spaces and guidance for use with transformers and prompt templates.
Large-parameter family (research release includes a 104B-parameter variant)
Long context windows (documented up to 128K tokens / 131072 in some listings)
Optimized for conversational templates and chat-style prompts
Retrieval-Augmented Generation (RAG) with grounding spans and citation modes
Single-step and multi-step tool use templates for orchestrating external tools/APIs
Multilingual generation (trained on 23 languages; evaluated in 10 languages)
Open-weights research release available on Hugging Face (Cohere Labs) with Transformer integration
Hostable via Cohere hosted Chat API (reference: https://docs.cohere.com/reference/chat)
Examples and integration guidance for use with FAISS, Hugging Face transformers (>=4.39.1), and local inference
Use Cases
Document Q&A with RAG: Build systems that retrieve relevant document snippets (e.g., from a vector store) and produce citation-aware answers over long documents or corpora.
Conversational Agents that Call Tools: Implement chat agents that call external APIs, databases, or tools in single-step or multi-step workflows to complete tasks like booking, data retrieval, or automation.
Multi-step Automation Pipelines: Orchestrate sequences of tool invocations (e.g., search → extract → transform → submit) where the model plans and executes multiple steps to complete complex operations.
Long-form Summarization and Analysis: Summarize, synthesize, or analyze very long documents, meeting transcripts, or multi-document corpora using the extended context window.
Multilingual Support and Cross-Language Tasks: Provide question answering, summarization, or conversational support across numerous languages with evaluated performance in major languages.
Research and Experimentation: Use the open-weights release to experiment with grounded generation techniques, citation modes, prompt templates, and custom tool-use strategies in research or prototype builds.
Long-form document question-answering and summarization using RAG over large corpora
Multi-step automation and agents that call and combine external tools/APIs
Conversational assistants requiring grounded answers with source citations
Document analysis pipelines (PDFs, knowledge bases) combined with semantic search like FAISS
Multilingual customer support and knowledge retrieval across large contexts
Screenshots
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