{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-cohere-command-r7b-12-2024","slug":"cohere-command-r7b-12-2024","name":"Cohere: Command R7B (12-2024)","type":"model","url":"https://openrouter.ai/models/cohere~command-r7b-12-2024","page_url":"https://unfragile.ai/cohere-command-r7b-12-2024","categories":["rag-knowledge"],"tags":["cohere","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$3.75e-8 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-cohere-command-r7b-12-2024__cap_0","uri":"capability://memory.knowledge.retrieval.augmented.generation.with.multi.document.ranking","name":"retrieval-augmented generation with multi-document ranking","description":"Implements RAG by accepting external document contexts and ranking them based on relevance to the query before generation, using a learned ranking mechanism that weights document importance during token generation. The model integrates retrieved context directly into the prompt context window, allowing it to synthesize answers grounded in provided documents while maintaining coherence across multiple sources.","intents":["I need to answer questions about proprietary documents without fine-tuning the model","I want to reduce hallucinations by grounding responses in retrieved knowledge bases","I need to build a Q&A system over large document collections with ranked relevance"],"best_for":["teams building enterprise knowledge systems with document retrieval pipelines","developers implementing customer support chatbots over internal documentation","builders creating domain-specific assistants with external knowledge sources"],"limitations":["Context window is finite (4096 tokens for Command R7B) — document ranking must filter aggressively for large corpora","No native vector database integration — requires external embedding and retrieval infrastructure","Ranking quality depends on document preprocessing and chunking strategy; poorly formatted sources degrade performance"],"requires":["API access to Cohere Command R7B via OpenRouter or direct Cohere API","External retrieval system (vector DB, BM25 index, or semantic search) to pre-filter documents","Document corpus preprocessed into retrievable chunks with metadata"],"input_types":["text (user query)","text (external documents/passages as context)"],"output_types":["text (generated answer grounded in provided documents)"],"categories":["memory-knowledge","rag"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-cohere-command-r7b-12-2024__cap_1","uri":"capability://tool.use.integration.tool.use.and.function.calling.with.schema.based.routing","name":"tool-use and function calling with schema-based routing","description":"Supports structured tool invocation through a schema-based function registry where tools are defined as JSON schemas with parameters, descriptions, and return types. The model generates tool calls as structured JSON that can be routed to external APIs or local functions, with built-in support for multi-turn tool use where results are fed back into the conversation context for further reasoning.","intents":["I need my LLM agent to call APIs (search, calculator, database queries) in a structured way","I want to build an agentic system where the model decides which tools to use and in what order","I need reliable function calling with proper error handling and result integration"],"best_for":["developers building autonomous agents with external tool dependencies","teams implementing workflow automation where LLMs orchestrate multiple APIs","builders creating specialized assistants (research, data analysis, DevOps) that need tool access"],"limitations":["Tool schema complexity is limited by context window — deeply nested or highly parameterized tools may cause parsing failures","No native retry logic for failed tool calls — requires application-level error handling and re-prompting","Tool execution is synchronous within a single turn; parallel tool execution requires custom orchestration"],"requires":["API key for Cohere Command R7B (via OpenRouter or direct API)","Tool definitions as JSON schemas with parameter descriptions","Application layer to execute tools and return results to the model"],"input_types":["text (user query)","JSON (tool schemas and definitions)"],"output_types":["JSON (structured tool calls with parameters)","text (natural language responses after tool execution)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-cohere-command-r7b-12-2024__cap_10","uri":"capability://text.generation.language.instruction.following.and.prompt.compliance","name":"instruction-following and prompt compliance","description":"Follows complex, multi-part instructions with high fidelity, respecting constraints on output format, length, style, and content restrictions. The model is trained to parse and execute detailed prompts, maintaining compliance across multiple simultaneous constraints and handling edge cases gracefully.","intents":["I need the model to strictly follow output format specifications (JSON, CSV, XML)","I want to enforce content restrictions or safety guidelines through prompts","I need to build systems where prompt compliance is critical for downstream processing"],"best_for":["developers building structured output systems where format compliance is required","teams implementing content moderation or safety guardrails through prompting","builders creating automation pipelines where LLM output feeds directly into other systems"],"limitations":["Instruction compliance is probabilistic; edge cases or conflicting instructions may cause failures","No native validation of compliance — requires post-processing checks for critical applications","Complex instructions with many constraints increase failure rate and latency"],"requires":["API key for Cohere Command R7B","Clear, well-structured instructions with explicit constraints","Optional: validation layer to verify compliance"],"input_types":["text (detailed instructions and input)"],"output_types":["text (output strictly following specified format and constraints)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-cohere-command-r7b-12-2024__cap_2","uri":"capability://text.generation.language.multi.turn.conversational.reasoning.with.state.preservation","name":"multi-turn conversational reasoning with state preservation","description":"Maintains conversation history across multiple turns with full context preservation, allowing the model to reference previous exchanges, build on prior reasoning, and correct itself based on feedback. The model uses a sliding context window that prioritizes recent messages while optionally summarizing or truncating older turns to stay within token limits.","intents":["I need a chatbot that remembers context across multiple user interactions","I want to build an iterative problem-solving assistant that refines answers based on user feedback","I need to implement a conversational agent that can reason across long dialogues"],"best_for":["developers building customer support chatbots with multi-turn interactions","teams creating interactive coding assistants or tutoring systems","builders implementing collaborative reasoning systems where context accumulates"],"limitations":["Context window of 4096 tokens limits conversation depth — long dialogues require explicit summarization or truncation strategy","No native conversation compression — developers must implement their own summarization to preserve context in long sessions","State is ephemeral; no built-in persistence — requires external session storage for multi-session continuity"],"requires":["API key for Cohere Command R7B","Application layer to manage conversation history and context window","Optional: external storage (database, cache) for multi-session persistence"],"input_types":["text (user message)","text (conversation history)"],"output_types":["text (model response)","optional: structured metadata (intent, entities, confidence)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-cohere-command-r7b-12-2024__cap_3","uri":"capability://planning.reasoning.complex.reasoning.and.chain.of.thought.decomposition","name":"complex reasoning and chain-of-thought decomposition","description":"Supports explicit reasoning chains where the model breaks down complex problems into intermediate steps, showing work before arriving at conclusions. This is implemented through prompt-level instruction for step-by-step reasoning, combined with the model's training on reasoning tasks, enabling it to handle multi-hop logical inference, mathematical problem-solving, and structured decision-making.","intents":["I need the model to show its reasoning steps for transparency and debugging","I want to solve multi-step math or logic problems with intermediate verification","I need to build systems where reasoning quality matters more than speed"],"best_for":["developers building explainable AI systems for regulated industries","teams creating educational or tutoring assistants that need to show work","builders implementing verification systems where reasoning transparency is critical"],"limitations":["Chain-of-thought reasoning increases token generation by 2-3x, raising latency and cost","Reasoning quality degrades on problems requiring domain expertise beyond training data","No native verification of intermediate steps — requires external validators for critical applications"],"requires":["API key for Cohere Command R7B","Prompt engineering to explicitly request step-by-step reasoning","Optional: external verification layer for high-stakes applications"],"input_types":["text (problem statement or query)"],"output_types":["text (step-by-step reasoning followed by final answer)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-cohere-command-r7b-12-2024__cap_4","uri":"capability://text.generation.language.semantic.text.generation.with.style.and.tone.control","name":"semantic text generation with style and tone control","description":"Generates coherent, contextually appropriate text across multiple styles and tones through instruction-based control, where prompts can specify desired voice (formal, casual, technical, creative), length constraints, and output format. The model uses instruction-tuning to respect these constraints while maintaining semantic accuracy and coherence.","intents":["I need to generate marketing copy, technical documentation, or creative content with consistent tone","I want to adapt the same content for different audiences (executives, developers, end-users)","I need to generate structured outputs like emails, reports, or summaries with specific formatting"],"best_for":["content teams automating copywriting and documentation generation","developers building personalized communication systems","builders creating multi-audience content platforms"],"limitations":["Style control is instruction-based and not always reliable for highly specific brand voices — may require post-processing","Length constraints are approximate; token counting is required for strict length guarantees","Creative generation quality varies with prompt specificity — vague requests produce generic output"],"requires":["API key for Cohere Command R7B","Well-crafted prompts specifying desired style, tone, and format","Optional: post-processing for strict formatting or brand compliance"],"input_types":["text (content prompt or topic)"],"output_types":["text (generated content in specified style and format)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-cohere-command-r7b-12-2024__cap_5","uri":"capability://data.processing.analysis.structured.data.extraction.and.entity.recognition","name":"structured data extraction and entity recognition","description":"Extracts structured information (entities, relationships, attributes) from unstructured text by accepting JSON schema definitions and returning parsed data matching those schemas. The model performs entity recognition, relationship extraction, and attribute assignment through instruction-tuned prompting, with support for nested structures and optional fields.","intents":["I need to extract structured data from documents, emails, or user input without building custom NER models","I want to parse semi-structured text (resumes, invoices, contracts) into databases","I need to identify entities and relationships for knowledge graph construction"],"best_for":["teams automating data entry and document processing","developers building knowledge extraction pipelines","builders creating data enrichment systems for unstructured sources"],"limitations":["Extraction accuracy depends on schema clarity and example quality — ambiguous schemas produce inconsistent results","No native validation of extracted data against constraints — requires post-processing validation","Performance degrades on domain-specific terminology not well-represented in training data"],"requires":["API key for Cohere Command R7B","JSON schema defining expected output structure","Optional: examples or few-shot prompts for complex extraction tasks"],"input_types":["text (unstructured source document)","JSON (schema definition)"],"output_types":["JSON (structured data matching schema)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-cohere-command-r7b-12-2024__cap_6","uri":"capability://code.generation.editing.code.generation.and.technical.problem.solving","name":"code generation and technical problem-solving","description":"Generates code snippets, complete functions, and multi-file solutions in multiple programming languages through instruction-based prompting. The model understands code context, can refactor existing code, and provides explanations alongside generated code, leveraging its training on diverse codebases and technical documentation.","intents":["I need to generate boilerplate code or complete functions quickly","I want to get code examples for APIs or libraries I'm unfamiliar with","I need to solve algorithmic problems or debug code with explanations"],"best_for":["developers using AI as a coding assistant for faster prototyping","teams generating API client libraries or SDK code","builders creating educational platforms for programming"],"limitations":["Generated code may contain subtle bugs or security issues — requires human review before production use","Performance optimization is not guaranteed; generated code may be inefficient for large-scale use","Language support varies; less common languages produce lower-quality output"],"requires":["API key for Cohere Command R7B","Clear specification of requirements (language, function signature, expected behavior)","Optional: existing code context for refactoring or completion tasks"],"input_types":["text (code request or problem description)","code (existing code for refactoring or context)"],"output_types":["code (generated functions, classes, or complete programs)","text (explanations and documentation)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-cohere-command-r7b-12-2024__cap_7","uri":"capability://text.generation.language.summarization.with.configurable.detail.levels","name":"summarization with configurable detail levels","description":"Condenses long documents or conversations into summaries of varying lengths and detail levels, from single-sentence abstracts to detailed bullet-point summaries. The model uses instruction-based control to balance comprehensiveness with brevity, preserving key information while removing redundancy.","intents":["I need to create executive summaries of long documents or meeting transcripts","I want to generate abstracts for research papers or articles","I need to condense customer feedback or support tickets into actionable insights"],"best_for":["teams automating document processing and knowledge management","developers building search result summarization or news aggregation","builders creating productivity tools that reduce information overload"],"limitations":["Summarization quality degrades on highly technical or domain-specific content","No native preservation of specific details — important but non-obvious information may be omitted","Length control is approximate; strict length requirements need post-processing"],"requires":["API key for Cohere Command R7B","Source text or document to summarize","Optional: instructions specifying summary length, focus areas, or style"],"input_types":["text (document or conversation to summarize)"],"output_types":["text (summary of specified length and detail level)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-cohere-command-r7b-12-2024__cap_8","uri":"capability://text.generation.language.multilingual.text.generation.and.translation","name":"multilingual text generation and translation","description":"Generates and translates text across multiple languages with support for context-aware localization. The model understands cultural nuances and can adapt content for different linguistic contexts, though translation quality varies by language pair and domain.","intents":["I need to translate content into multiple languages for global audiences","I want to generate content in non-English languages with cultural appropriateness","I need to build multilingual chatbots or support systems"],"best_for":["teams building global products with multilingual support","developers creating translation pipelines for content platforms","builders implementing international customer support systems"],"limitations":["Translation quality is best for high-resource languages (English, Spanish, French, German); low-resource languages produce lower quality","Cultural adaptation is limited to training data representation; may not capture all regional nuances","No native support for domain-specific terminology — requires custom glossaries for technical translation"],"requires":["API key for Cohere Command R7B","Source text in supported language","Optional: target language specification and cultural context"],"input_types":["text (source content in any supported language)"],"output_types":["text (translated or generated content in target language)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-cohere-command-r7b-12-2024__cap_9","uri":"capability://search.retrieval.semantic.similarity.and.relevance.ranking","name":"semantic similarity and relevance ranking","description":"Ranks text passages or documents by relevance to a query through semantic understanding, without explicit vector embeddings. The model evaluates semantic similarity by processing both query and candidates in context, producing relevance scores that reflect deeper semantic relationships than keyword matching.","intents":["I need to rank search results or retrieved documents by relevance to a user query","I want to identify the most relevant passages from a document for a specific question","I need to filter or sort candidates based on semantic fit"],"best_for":["developers building semantic search systems without dedicated embedding models","teams implementing RAG pipelines where ranking is critical","builders creating recommendation systems based on semantic similarity"],"limitations":["Ranking is computationally expensive compared to vector similarity — requires API calls for each ranking decision","No native batch ranking optimization — ranking many candidates requires sequential API calls","Ranking quality depends on query clarity; ambiguous queries produce inconsistent rankings"],"requires":["API key for Cohere Command R7B","Query text and candidate passages to rank","Optional: relevance criteria or ranking instructions"],"input_types":["text (query)","text (candidate passages or documents)"],"output_types":["ranked list of candidates with relevance scores or ordering"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["API access to Cohere Command R7B via OpenRouter or direct Cohere API","External retrieval system (vector DB, BM25 index, or semantic search) to pre-filter documents","Document corpus preprocessed into retrievable chunks with metadata","API key for Cohere Command R7B (via OpenRouter or direct API)","Tool definitions as JSON schemas with parameter descriptions","Application layer to execute tools and return results to the model","API key for Cohere Command R7B","Clear, well-structured instructions with explicit constraints","Optional: validation layer to verify compliance","Application layer to manage conversation history and context window"],"failure_modes":["Context window is finite (4096 tokens for Command R7B) — document ranking must filter aggressively for large corpora","No native vector database integration — requires external embedding and retrieval infrastructure","Ranking quality depends on document preprocessing and chunking strategy; poorly formatted sources degrade performance","Tool schema complexity is limited by context window — deeply nested or highly parameterized tools may cause parsing failures","No native retry logic for failed tool calls — requires application-level error handling and re-prompting","Tool execution is synchronous within a single turn; parallel tool execution requires custom orchestration","Instruction compliance is probabilistic; edge cases or conflicting instructions may cause failures","No native validation of compliance — requires post-processing checks for critical applications","Complex instructions with many constraints increase failure rate and latency","Context window of 4096 tokens limits conversation depth — long dialogues require explicit summarization or truncation strategy","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.47,"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-r7b-12-2024","compare_url":"https://unfragile.ai/compare?artifact=cohere-command-r7b-12-2024"}},"signature":"9LS98QUAcvEqBONXSqKyexOd2e/mCt0RiNB/UelcUI/Z/b5tA+YnWLm5HG5u5ZZpMEpuL9KU1wsINBfWJilMCw==","signedAt":"2026-06-21T12:01:11.657Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/cohere-command-r7b-12-2024","artifact":"https://unfragile.ai/cohere-command-r7b-12-2024","verify":"https://unfragile.ai/api/v1/verify?slug=cohere-command-r7b-12-2024","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"}}