{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-cohere-command-r-08-2024","slug":"cohere-command-r-08-2024","name":"Cohere: Command R (08-2024)","type":"model","url":"https://openrouter.ai/models/cohere~command-r-08-2024","page_url":"https://unfragile.ai/cohere-command-r-08-2024","categories":["rag-knowledge"],"tags":["cohere","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$1.50e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-cohere-command-r-08-2024__cap_0","uri":"capability://memory.knowledge.multilingual.retrieval.augmented.generation.rag.with.context.grounding","name":"multilingual retrieval-augmented generation (rag) with context grounding","description":"Implements RAG by accepting external document context and grounding responses in retrieved passages across 100+ languages. The model architecture includes a retrieval-aware attention mechanism that weights retrieved documents during generation, enabling factual accuracy and citation-aware outputs. Supports both in-context document injection and integration with external vector databases via tool-use APIs.","intents":["Ground LLM responses in proprietary documents without fine-tuning","Build multilingual knowledge bases that serve customers in their native language","Reduce hallucinations by forcing the model to cite source documents","Query documents in languages different from the source language"],"best_for":["Teams building enterprise search and Q&A systems across multiple markets","Organizations with multilingual document corpora needing unified retrieval","Developers reducing hallucination risk in production LLM applications"],"limitations":["RAG quality depends on retrieval quality — poor document ranking upstream degrades output","Context window limits (typically 4K-8K tokens) constrain document volume per query","No built-in re-ranking or relevance filtering — requires external ranking pipeline for large document sets","Multilingual performance varies by language pair; low-resource languages may show degradation"],"requires":["API key for Cohere or OpenRouter access","External retrieval system (vector DB, BM25 index, or custom ranking) to supply documents","Document preprocessing pipeline to chunk and embed content"],"input_types":["text (query)","text (retrieved document passages)","structured metadata (document source, relevance scores)"],"output_types":["text (grounded response with citations)","structured data (response + source references)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-cohere-command-r-08-2024__cap_1","uri":"capability://tool.use.integration.tool.use.and.function.calling.with.schema.based.dispatch","name":"tool-use and function calling with schema-based dispatch","description":"Implements function calling via a JSON schema registry where developers define tool signatures (name, description, parameters) and the model outputs structured tool calls that can be dispatched to external APIs or local functions. The model learns to invoke tools based on task requirements, supporting multi-turn tool use where outputs from one tool feed into subsequent calls. Integration points include OpenRouter's tool-calling API, native Cohere API, and custom orchestration layers.","intents":["Enable LLM agents to call APIs, databases, or custom functions without manual prompt engineering","Build multi-step workflows where tool outputs inform subsequent tool selection","Reduce latency by avoiding round-trips to the LLM for every tool invocation decision"],"best_for":["Developers building autonomous agents that interact with external systems","Teams implementing agentic workflows with deterministic tool dispatch","Builders reducing prompt engineering overhead by using structured tool definitions"],"limitations":["Tool selection quality depends on schema clarity — ambiguous descriptions lead to incorrect tool calls","No built-in error handling or retry logic — requires wrapper code to handle tool failures","Limited to tools defined in the schema; dynamic tool discovery not supported","Tool call latency adds ~100-200ms per invocation due to API round-trip"],"requires":["API key for Cohere or OpenRouter","Tool definitions in JSON schema format (name, description, parameters with types)","Orchestration layer to execute tool calls and feed results back to the model"],"input_types":["text (user query)","structured data (tool schema definitions in JSON)"],"output_types":["structured data (tool name + parameters)","text (model reasoning about tool selection)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-cohere-command-r-08-2024__cap_2","uri":"capability://code.generation.editing.code.generation.and.mathematical.reasoning.with.structured.output","name":"code generation and mathematical reasoning with structured output","description":"Generates code across multiple programming languages and solves mathematical problems by breaking down reasoning into intermediate steps. The model uses chain-of-thought patterns internally, producing both executable code and step-by-step mathematical derivations. Supports code completion, bug fixing, and algorithm explanation. The 08-2024 update improves performance on complex math and multi-language code generation through enhanced training on mathematical datasets and code repositories.","intents":["Generate working code snippets from natural language descriptions","Solve mathematical problems with step-by-step reasoning","Debug or refactor existing code with explanations","Explain algorithms and mathematical concepts"],"best_for":["Developers using LLM-assisted coding in IDEs or chat interfaces","Educational platforms teaching math and computer science","Teams automating code generation for boilerplate or routine tasks"],"limitations":["Code generation quality varies by language; better for Python/JavaScript than niche languages","No real-time compilation or execution — generated code must be tested separately","Mathematical reasoning limited to problems solvable in ~2000 tokens; very complex proofs may be incomplete","No awareness of project-specific libraries or custom frameworks — requires additional context"],"requires":["API key for Cohere or OpenRouter","Clear problem description or code context for best results","Testing infrastructure to validate generated code"],"input_types":["text (natural language problem description)","code (existing code for refactoring or debugging)","structured data (mathematical problem specification)"],"output_types":["code (executable or pseudo-code)","text (step-by-step reasoning)","structured data (mathematical derivations)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-cohere-command-r-08-2024__cap_3","uri":"capability://text.generation.language.conversational.chat.with.multi.turn.context.management","name":"conversational chat with multi-turn context management","description":"Maintains conversation state across multiple turns, tracking user intent and context without explicit memory management. The model processes the full conversation history (within token limits) to generate contextually appropriate responses. Supports persona customization through system prompts and handles topic switching, clarification requests, and context recovery. Integration via chat completion APIs that accept message arrays with role-based formatting (user/assistant/system).","intents":["Build chatbots that remember context across conversation turns","Create conversational interfaces for customer support or Q&A","Implement multi-turn dialogue with natural topic transitions"],"best_for":["Teams building customer-facing chatbots and conversational interfaces","Developers prototyping dialogue systems quickly","Applications requiring stateless API calls with full conversation history"],"limitations":["Context window limits (~4K-8K tokens) constrain conversation length before truncation","No persistent memory across sessions — each conversation starts fresh","Token usage grows linearly with conversation length, increasing API costs","No built-in conversation summarization — long conversations require manual truncation"],"requires":["API key for Cohere or OpenRouter","Message history management in application code","Token counting to avoid exceeding context limits"],"input_types":["text (user message)","structured data (message history with roles)"],"output_types":["text (assistant response)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-cohere-command-r-08-2024__cap_4","uri":"capability://search.retrieval.semantic.search.and.relevance.ranking.with.embedding.aware.retrieval","name":"semantic search and relevance ranking with embedding-aware retrieval","description":"Supports semantic search by accepting query text and returning ranked results based on semantic similarity rather than keyword matching. The model can be used as a reranker in retrieval pipelines, taking candidate documents and a query, then scoring relevance. Integrates with vector databases and BM25 indices through API calls. The 08-2024 update improves multilingual search by handling cross-lingual queries where the search language differs from document language.","intents":["Rerank search results from a vector database or BM25 index","Find semantically similar documents without explicit embeddings","Implement cross-lingual search where queries and documents are in different languages"],"best_for":["Teams building search-heavy applications (e-commerce, documentation, knowledge bases)","Developers improving retrieval quality in RAG pipelines through reranking","Organizations with multilingual content needing unified search"],"limitations":["Reranking latency adds ~50-200ms per query depending on candidate set size","Requires pre-computed candidate set from a faster retriever (vector DB or BM25)","No built-in caching — repeated queries incur full API cost","Ranking quality depends on query clarity; ambiguous queries produce inconsistent results"],"requires":["API key for Cohere or OpenRouter","Pre-existing retrieval system (vector DB, BM25, or full-text search) to supply candidates","Candidate documents with text content for ranking"],"input_types":["text (query)","text (candidate documents)"],"output_types":["structured data (ranked document IDs with relevance scores)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-cohere-command-r-08-2024__cap_5","uri":"capability://text.generation.language.instruction.following.with.system.prompt.customization","name":"instruction-following with system prompt customization","description":"Accepts system prompts to customize model behavior, tone, and constraints without fine-tuning. The model interprets system instructions and applies them consistently across the conversation. Supports complex instructions like role-playing, output format specifications, and behavioral constraints. Implementation uses instruction-tuning from training, where the model learned to follow diverse instructions through supervised fine-tuning on instruction-following datasets.","intents":["Customize model behavior for specific use cases (e.g., technical writer, customer support agent)","Enforce output format requirements (JSON, XML, markdown) through instructions","Implement safety constraints or domain-specific rules without model retraining"],"best_for":["Teams building specialized chatbots with consistent personas","Developers needing structured output without custom parsing","Applications requiring behavioral customization across different user segments"],"limitations":["Instruction following quality degrades with very complex or conflicting instructions","No guarantee of format compliance — model may deviate from specified output format","Instructions add token overhead to every request, increasing latency and cost","Adversarial instructions can override safety guidelines (requires external guardrails)"],"requires":["API key for Cohere or OpenRouter","Well-written system prompt with clear instructions","Output validation logic to handle format deviations"],"input_types":["text (system prompt)","text (user message)"],"output_types":["text (response following system prompt instructions)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-cohere-command-r-08-2024__cap_6","uri":"capability://automation.workflow.batch.processing.and.asynchronous.api.calls.for.high.volume.inference","name":"batch processing and asynchronous api calls for high-volume inference","description":"Supports batch API endpoints where developers submit multiple requests in a single API call, receiving results asynchronously. Useful for processing large document collections, bulk classification, or offline analysis. The batch endpoint queues requests and returns results via callback or polling. This reduces per-request overhead and enables cost optimization through batch pricing discounts.","intents":["Process thousands of documents for classification or summarization without rate limits","Analyze large datasets offline without blocking on individual API calls","Reduce API costs by leveraging batch pricing discounts"],"best_for":["Data teams processing large corpora for analysis or labeling","Developers building offline pipelines for document processing","Organizations with high-volume inference needs seeking cost optimization"],"limitations":["Batch processing introduces latency — results may take minutes to hours depending on queue","No real-time feedback; developers must poll or implement callback handlers","Batch size limits constrain maximum requests per batch (typically 10K-100K)","Debugging failed requests is harder due to asynchronous nature"],"requires":["API key for Cohere or OpenRouter with batch API access","Batch request formatting (JSONL or similar)","Callback endpoint or polling mechanism to retrieve results"],"input_types":["structured data (batch request array with multiple prompts)"],"output_types":["structured data (batch results array with responses)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-cohere-command-r-08-2024__cap_7","uri":"capability://text.generation.language.response.streaming.for.real.time.token.generation","name":"response streaming for real-time token generation","description":"Streams response tokens in real-time as they are generated, enabling progressive display in user interfaces without waiting for the full response. Implementation uses server-sent events (SSE) or WebSocket connections to push tokens to the client. Reduces perceived latency and improves user experience for long-form content generation. Supports streaming of both text and structured outputs (e.g., JSON tokens).","intents":["Display LLM responses progressively in chat interfaces","Reduce perceived latency for long-form content generation","Build real-time collaborative writing tools"],"best_for":["Frontend developers building chat UIs with real-time feedback","Teams implementing streaming APIs for web and mobile applications","Developers reducing perceived latency in user-facing LLM applications"],"limitations":["Streaming adds complexity to client-side code (token buffering, error handling)","Network interruptions mid-stream require reconnection logic","Structured output parsing is harder with streaming (tokens arrive out of order)","Cost is identical to non-streaming; no financial benefit"],"requires":["API key for Cohere or OpenRouter","Client-side code to handle streaming responses (SSE or WebSocket)","Error handling for network interruptions"],"input_types":["text (prompt)"],"output_types":["text (streamed tokens)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"low","permissions":["API key for Cohere or OpenRouter access","External retrieval system (vector DB, BM25 index, or custom ranking) to supply documents","Document preprocessing pipeline to chunk and embed content","API key for Cohere or OpenRouter","Tool definitions in JSON schema format (name, description, parameters with types)","Orchestration layer to execute tool calls and feed results back to the model","Clear problem description or code context for best results","Testing infrastructure to validate generated code","Message history management in application code","Token counting to avoid exceeding context limits"],"failure_modes":["RAG quality depends on retrieval quality — poor document ranking upstream degrades output","Context window limits (typically 4K-8K tokens) constrain document volume per query","No built-in re-ranking or relevance filtering — requires external ranking pipeline for large document sets","Multilingual performance varies by language pair; low-resource languages may show degradation","Tool selection quality depends on schema clarity — ambiguous descriptions lead to incorrect tool calls","No built-in error handling or retry logic — requires wrapper code to handle tool failures","Limited to tools defined in the schema; dynamic tool discovery not supported","Tool call latency adds ~100-200ms per invocation due to API round-trip","Code generation quality varies by language; better for Python/JavaScript than niche languages","No real-time compilation or execution — generated code must be tested separately","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.41,"ecosystem":0.24,"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":"2026-05-24T12:16:24.484Z","last_scraped_at":"2026-05-03T15:20:45.776Z","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=cohere-command-r-08-2024","compare_url":"https://unfragile.ai/compare?artifact=cohere-command-r-08-2024"}},"signature":"zXwSwGhSlkMMjfvnnbp4uPe144vW9IqpsarFnCw6JlbjdboE6EmJxzlFaqRbeeYOto8XJQGZk1d0fMRuDKuOBg==","signedAt":"2026-06-21T00:03:08.183Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/cohere-command-r-08-2024","artifact":"https://unfragile.ai/cohere-command-r-08-2024","verify":"https://unfragile.ai/api/v1/verify?slug=cohere-command-r-08-2024","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"}}