{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"command-r","slug":"command-r","name":"Command R","type":"model","url":"https://cohere.com/command","page_url":"https://unfragile.ai/command-r","categories":["model-training","rag-knowledge","deployment-infra","documentation","testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"command-r__cap_0","uri":"capability://text.generation.language.rag.optimized.text.generation.with.128k.context.window","name":"rag-optimized text generation with 128k context window","description":"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.","intents":["I need to build a production chatbot that answers questions grounded in large document collections without hallucinating","I want to process 50+ pages of retrieved documents and synthesize them into a coherent answer in a single API call","I need to reduce inference costs while maintaining RAG quality for high-volume enterprise applications"],"best_for":["Enterprise teams building production RAG pipelines with high throughput requirements","Developers optimizing for cost-per-inference in document-heavy applications","Teams migrating from larger models (GPT-4, Claude) to reduce operational expenses"],"limitations":["128K token context window is fixed; documents larger than this require external chunking/summarization before submission","No quantitative benchmark data published comparing RAG quality vs Command R+ or other models","Inference latency and throughput metrics not disclosed; actual performance at context limits unknown","No local inference option; all processing occurs on Cohere-managed infrastructure with network latency"],"requires":["Cohere API key (free trial or production pay-as-you-go account)","HTTP/REST client library or Cohere SDK (Python, Node.js, Go, Java supported)","Pre-retrieval pipeline to fetch and rank relevant documents before submission"],"input_types":["text (conversation history, system prompts, user queries)","structured context (retrieved document passages with metadata)","conversation arrays with role-based message formatting"],"output_types":["text (generated response)","citations (document references with source attribution)","structured metadata (finish_reason, token counts, model version)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"command-r__cap_1","uri":"capability://text.generation.language.built.in.citation.generation.with.source.attribution","name":"built-in citation generation with source attribution","description":"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.","intents":["I need to show users exactly which documents my chatbot is citing when it answers their questions","I want to build compliance-friendly applications where every factual claim is traceable to a source document","I need to reduce hallucination by forcing the model to cite sources rather than generating unsupported claims"],"best_for":["Legal/compliance teams building document-grounded applications where source attribution is mandatory","Customer support platforms requiring transparent, auditable responses","Research and knowledge management systems where citation accuracy is critical"],"limitations":["Citation accuracy depends on quality of retrieved documents; irrelevant or contradictory sources may produce incorrect citations","No mechanism to handle conflicting information across sources; model may cite contradictory passages without flagging the conflict","Citation format and granularity not customizable; output follows Cohere's fixed citation schema","No published evaluation metrics for citation accuracy or false-positive rates"],"requires":["Cohere API key with access to Command R model","Retrieved documents or passages provided in the request context with unique identifiers","Client-side parsing logic to extract and render citations from the response"],"input_types":["text (user query)","structured documents (array of passages with IDs, titles, or metadata)"],"output_types":["text with embedded citation markers","citation metadata (source document ID, passage index, confidence scores if available)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"command-r__cap_10","uri":"capability://data.processing.analysis.embedding.generation.via.embed.4.model.integration","name":"embedding generation via embed 4 model integration","description":"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.","intents":["I need to convert documents into embeddings for semantic search in my RAG pipeline","I want to find similar documents or passages based on semantic meaning rather than keyword matching","I need to build a vector database of documents for efficient retrieval"],"best_for":["Developers building end-to-end RAG pipelines using Cohere models","Teams implementing semantic search on document collections","Applications requiring similarity-based clustering or recommendation"],"limitations":["Embed 4 is a separate model with separate API endpoint and pricing ($4-5/hour in Model Vault)","Embedding dimension and vector format not documented","No built-in vector database; embeddings must be stored in external systems (Pinecone, Weaviate, etc.)","No batch embedding API documented; may require multiple API calls for large document collections"],"requires":["Cohere API key","Embed 4 model access (separate from Command R)","Vector database or storage system for embedding persistence"],"input_types":["text (documents or passages to embed)"],"output_types":["dense vectors (embeddings)","metadata (embedding dimension, model version)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"command-r__cap_11","uri":"capability://search.retrieval.semantic.ranking.and.relevance.scoring.via.rerank.models","name":"semantic ranking and relevance scoring via rerank models","description":"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.","intents":["I need to improve retrieval quality by re-ranking search results based on semantic relevance","I want to reduce noise in my RAG pipeline by filtering out irrelevant documents before generation","I need to score document relevance to a query without training a custom ranking model"],"best_for":["Developers building production RAG pipelines with strict relevance requirements","Teams optimizing retrieval quality to reduce hallucination in generation","Applications where retrieval precision is critical (legal, medical, financial)"],"limitations":["Rerank models are separate from Command R with separate API endpoints and pricing ($5-10/hour in Model Vault)","Ranking algorithm and training approach not documented","No published benchmarks comparing Rerank 3.5 vs Rerank 4 or vs competitors","Relevance scores are relative; no absolute threshold for determining relevance"],"requires":["Cohere API key","Rerank model access (Rerank 3.5 or Rerank 4)","Retrieved documents with query for ranking"],"input_types":["query (text)","documents (array of text passages or structured documents)"],"output_types":["ranked documents (reordered by relevance)","relevance scores (numeric scores for each document)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"command-r__cap_12","uri":"capability://automation.workflow.batch.processing.api.for.high.volume.inference","name":"batch processing api for high-volume inference","description":"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.","intents":["I need to process 100,000 documents overnight at lower cost than real-time API calls","I want to analyze a large dataset of customer feedback without paying premium rates for immediate processing","I need to generate summaries or analyses for a batch of documents on a schedule"],"best_for":["Batch processing and ETL pipelines","Overnight or scheduled analysis jobs","Cost-sensitive applications where latency is not critical"],"limitations":["Batch API details not documented; exact submission format, processing time, and cost savings unknown","No SLA on batch processing time; may take hours or days to complete","No real-time feedback on batch job progress; results are delivered asynchronously","Batch API may not support all features (streaming, citations, etc.); feature parity unknown"],"requires":["Cohere API key","Batch submission format (JSON or CSV, exact format unknown)"],"input_types":["batch of requests (format unknown)"],"output_types":["batch of responses (asynchronous delivery)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"command-r__cap_2","uri":"capability://text.generation.language.multilingual.text.generation.across.10.languages","name":"multilingual text generation across 10 languages","description":"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.","intents":["I need to build a chatbot that serves customers in multiple countries without deploying separate models per language","I want to reduce infrastructure complexity by using one model for 10 languages instead of 10 separate models","I need to support code-switching or mixed-language inputs without degrading response quality"],"best_for":["Global enterprises building multilingual customer support or knowledge management systems","SaaS platforms serving international markets with limited infrastructure budgets","Teams prioritizing operational simplicity over language-specific fine-tuning"],"limitations":["Specific languages supported not enumerated in documentation; only stated as '10 languages'","No published benchmarks comparing multilingual quality vs language-specific models or competitors","Performance may vary significantly across languages; no per-language quality metrics disclosed","No explicit support for low-resource languages; multilingual coverage likely skewed toward high-resource languages"],"requires":["Cohere API key","Input text in one of the 10 supported languages (language detection is automatic)"],"input_types":["text in any of the 10 supported languages","mixed-language inputs (code-switching)"],"output_types":["text in the detected input language"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"command-r__cap_3","uri":"capability://tool.use.integration.tool.use.and.function.calling.for.agentic.workflows","name":"tool use and function calling for agentic workflows","description":"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.","intents":["I need to build an AI agent that can decide when to call external APIs and generate properly-formatted function calls","I want the model to reason about which tools to use for a given task without hardcoding tool selection logic","I need to create a chatbot that can perform actions (send emails, update databases, fetch real-time data) based on user requests"],"best_for":["Developers building autonomous agents that orchestrate multiple external services","Teams implementing ReAct or similar agentic patterns with LLM-driven tool selection","Applications requiring dynamic tool invocation based on user intent"],"limitations":["Function calling format and schema not explicitly documented; exact specification unknown","No built-in tool execution layer; developers must parse function calls and execute them separately","No automatic error handling or retry logic if function calls fail; application must implement recovery","Tool selection quality depends on model's understanding of tool descriptions; poorly-written tool specs may cause incorrect calls"],"requires":["Cohere API key","Tool/function definitions provided in the request (schema format unknown but likely JSON-based)","Application-layer code to parse function calls and execute external tools"],"input_types":["text (user query)","tool definitions (schema describing available functions)"],"output_types":["text response with embedded function call specifications","structured function call metadata (function name, parameters, argument values)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"command-r__cap_4","uri":"capability://text.generation.language.document.analysis.and.summarization.with.context.preservation","name":"document analysis and summarization with context preservation","description":"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.","intents":["I need to summarize a 50-page legal document and extract key obligations and dates","I want to analyze a research paper and answer specific questions about methodology and findings","I need to process a batch of customer feedback documents and identify common themes"],"best_for":["Legal and compliance teams analyzing contracts and regulatory documents","Research organizations processing academic papers and technical reports","Customer insights teams analyzing feedback, reviews, and support tickets at scale"],"limitations":["Summarization quality not benchmarked against alternatives; no published ROUGE or similar metrics","No control over summary length or detail level; output length depends on model's learned behavior","May lose nuance or context when summarizing highly technical or domain-specific documents","No built-in support for multi-document summarization or cross-document analysis"],"requires":["Cohere API key","Document text (plain text, markdown, or extracted from PDF/Word)"],"input_types":["text (document content, up to 128K tokens)","text (user query or summarization instructions)"],"output_types":["text (summary or analysis)","structured data (extracted key points, metadata)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"command-r__cap_5","uri":"capability://tool.use.integration.pay.as.you.go.api.inference.with.trial.and.production.tiers","name":"pay-as-you-go api inference with trial and production tiers","description":"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.","intents":["I want to prototype a RAG application without committing to infrastructure or upfront costs","I need to scale inference from 100 requests/day to 1M requests/day without re-architecting","I want to avoid managing GPU infrastructure and let a vendor handle model serving"],"best_for":["Startups and small teams with variable or unpredictable inference loads","Enterprises seeking to avoid CapEx on GPU infrastructure","Developers prototyping multiple applications and wanting to share a single API quota"],"limitations":["Trial API keys explicitly prohibited from production/commercial use; requires upgrade to production key","Rate limits on trial keys not specified; exact throughput limits unknown","Pricing for Command R not publicly listed; custom enterprise pricing requires sales contact","No SLA or uptime guarantees documented; dependent on Cohere infrastructure availability","No local inference option; all processing occurs on Cohere servers with network latency"],"requires":["Cohere account (free or paid)","API key (trial or production)","HTTP client library or Cohere SDK"],"input_types":["API requests (JSON-formatted)"],"output_types":["API responses (JSON-formatted with generated text, citations, metadata)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"command-r__cap_6","uri":"capability://automation.workflow.managed.model.vault.deployment.with.dedicated.instances","name":"managed model vault deployment with dedicated instances","description":"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.","intents":["I need guaranteed latency and throughput for mission-critical applications without sharing infrastructure with other customers","I want to deploy a model in a VPC or private network for compliance/security reasons","I need predictable costs with monthly billing rather than per-token variable pricing"],"best_for":["Enterprise organizations with strict latency or availability requirements","Regulated industries (finance, healthcare) requiring isolated infrastructure","Teams with predictable, high-volume inference loads justifying dedicated capacity"],"limitations":["Pricing for Command R Model Vault not publicly disclosed; requires custom enterprise quote","Minimum commitment or capacity requirements unknown","Setup and provisioning time not documented; likely requires weeks for enterprise deployments","No auto-scaling within a dedicated instance; capacity planning must be done upfront","Integration with North platform required; limited flexibility for custom deployment architectures"],"requires":["Enterprise Cohere contract","Custom pricing negotiation","VPC or private network infrastructure (for private deployments)"],"input_types":["API requests (same format as cloud API)"],"output_types":["API responses (same format as cloud API)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"command-r__cap_7","uri":"capability://automation.workflow.private.deployment.with.hyperscaler.vpc.integration","name":"private deployment with hyperscaler vpc integration","description":"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.","intents":["I need to deploy a model in my VPC for data residency compliance (GDPR, HIPAA, etc.)","I want to avoid sending sensitive data to Cohere's shared cloud infrastructure","I need to integrate the model with internal systems without exposing data to external networks"],"best_for":["Regulated enterprises (healthcare, finance, government) with strict data residency requirements","Organizations processing sensitive customer data requiring network isolation","Teams with existing VPC infrastructure and security policies"],"limitations":["Pricing for private deployments not publicly listed; custom enterprise pricing required","Deployment timeline and complexity unknown; likely requires 4-12 weeks for enterprise deployments","Limited documentation on VPC integration patterns; requires coordination with Cohere sales/engineering","Ongoing maintenance and updates managed by Cohere; limited customer control over deployment details","No option for fully self-hosted inference; Cohere retains control over model updates and infrastructure"],"requires":["Enterprise Cohere contract","VPC infrastructure on AWS, Azure, or GCP (or on-premises equivalent)","Network connectivity and security policies configured for private deployment"],"input_types":["API requests (same format as cloud API, but routed through VPC)"],"output_types":["API responses (same format as cloud API)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"command-r__cap_8","uri":"capability://text.generation.language.streaming.response.generation.for.real.time.applications","name":"streaming response generation for real-time applications","description":"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.","intents":["I want to build a chatbot UI that shows text appearing in real-time rather than waiting for the full response","I need to reduce time-to-first-token latency for interactive applications","I want to enable users to interrupt or stop generation mid-response"],"best_for":["Interactive chatbot and conversational AI applications","Real-time customer support interfaces","Web and mobile applications where perceived latency matters"],"limitations":["Streaming format and token delivery mechanism not documented; exact API contract unknown","Citations may be incomplete or unavailable until end of stream; citation handling in streaming mode unclear","No built-in support for interrupting generation mid-stream; client must handle connection termination","Streaming adds complexity to error handling and retry logic compared to batch requests"],"requires":["Cohere API key","HTTP client supporting streaming responses (e.g., fetch with ReadableStream, axios with responseType: 'stream')","Client-side logic to parse and render streamed tokens"],"input_types":["API requests with streaming flag enabled"],"output_types":["streamed tokens (text chunks delivered incrementally)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"command-r__cap_9","uri":"capability://text.generation.language.conversation.history.management.with.role.based.message.formatting","name":"conversation history management with role-based message formatting","description":"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.","intents":["I need to build a multi-turn chatbot where the model remembers previous messages in the conversation","I want to maintain conversation state without manually concatenating all previous messages","I need to inject system instructions or context that persist across multiple user messages"],"best_for":["Chatbot and conversational AI developers","Customer support applications requiring multi-turn interactions","Teams building dialogue systems with persistent context"],"limitations":["Conversation history counts toward the 128K token limit; long conversations may exceed context window","No built-in conversation persistence; developers must store and retrieve conversation history from external storage","No automatic conversation pruning or summarization; developers must manually manage history length","Message formatting schema not explicitly documented; exact role and field requirements unknown"],"requires":["Cohere API key","Message array with role-based formatting (user, assistant, system roles)"],"input_types":["message arrays with role, content, and optional metadata fields"],"output_types":["text response (assistant message)","conversation metadata (token counts, finish reason)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"command-r__headline","uri":"capability://model.training.rag.optimized.language.model.for.enterprise","name":"rag-optimized language model for enterprise","description":"Cohere's Command R is a powerful, cost-effective language model designed for high-volume enterprise applications, featuring RAG architecture and multilingual support, ideal for chatbots and document analysis.","intents":["best RAG model for enterprise","language model for document analysis","efficient AI model for chatbots","cost-effective RAG solution","multilingual language model for business"],"best_for":["high-volume workloads","enterprise applications"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["model-training","rag-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Cohere API key (free trial or production pay-as-you-go account)","HTTP/REST client library or Cohere SDK (Python, Node.js, Go, Java supported)","Pre-retrieval pipeline to fetch and rank relevant documents before submission","Cohere API key with access to Command R model","Retrieved documents or passages provided in the request context with unique identifiers","Client-side parsing logic to extract and render citations from the response","Cohere API key","Embed 4 model access (separate from Command R)","Vector database or storage system for embedding persistence","Rerank model access (Rerank 3.5 or Rerank 4)"],"failure_modes":["128K token context window is fixed; documents larger than this require external chunking/summarization before submission","No quantitative benchmark data published comparing RAG quality vs Command R+ or other models","Inference latency and throughput metrics not disclosed; actual performance at context limits unknown","No local inference option; all processing occurs on Cohere-managed infrastructure with network latency","Citation accuracy depends on quality of retrieved documents; irrelevant or contradictory sources may produce incorrect citations","No mechanism to handle conflicting information across sources; model may cite contradictory passages without flagging the conflict","Citation format and granularity not customizable; output follows Cohere's fixed citation schema","No published evaluation metrics for citation accuracy or false-positive rates","Embed 4 is a separate model with separate API endpoint and pricing ($4-5/hour in Model Vault)","Embedding dimension and vector format not documented","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.35,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.548Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=command-r","compare_url":"https://unfragile.ai/compare?artifact=command-r"}},"signature":"ILmCHZSKk5+EDnDFosDtr2SKWuk+QMWExZ2thD6hYHfQH2pPI8FiICzSZBJ1tZpgNQ1fqSF10Ml3uZb2fYCLBA==","signedAt":"2026-06-20T10:08:06.039Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/command-r","artifact":"https://unfragile.ai/command-r","verify":"https://unfragile.ai/api/v1/verify?slug=command-r","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}