{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-cohere-command-r-plus-08-2024","slug":"cohere-command-r-plus-08-2024","name":"Cohere: Command R+ (08-2024)","type":"model","url":"https://openrouter.ai/models/cohere~command-r-plus-08-2024","page_url":"https://unfragile.ai/cohere-command-r-plus-08-2024","categories":["chatbots-assistants"],"tags":["cohere","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$2.50e-6 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-cohere-command-r-plus-08-2024__cap_0","uri":"capability://text.generation.language.multi.turn.conversational.reasoning.with.retrieval.augmentation","name":"multi-turn conversational reasoning with retrieval augmentation","description":"Processes multi-turn conversations with built-in support for retrieval-augmented generation (RAG) through Cohere's native document grounding API. The model maintains conversation context across turns while integrating external document retrieval, enabling it to cite sources and ground responses in provided documents without requiring manual prompt engineering for RAG patterns.","intents":["Build a chatbot that answers questions grounded in proprietary documents without hallucination","Create a customer support agent that references knowledge bases and cites specific sources","Implement a research assistant that synthesizes information from multiple documents across conversation turns"],"best_for":["Enterprise teams building RAG-based customer support systems","Product teams implementing document-grounded Q&A without custom retrieval pipelines","Developers migrating from manual RAG implementations to native model-integrated grounding"],"limitations":["Document grounding requires explicit document passing per request — no persistent vector store integration","Citation accuracy depends on document formatting and clarity; poorly structured documents may produce inaccurate citations","Context window limits multi-turn conversations to ~4K tokens of history before truncation"],"requires":["Cohere API key with Command R+ access","Documents formatted as text or structured metadata (JSON/XML supported)","HTTP client library for REST API integration"],"input_types":["text (conversation messages)","text (documents for grounding)","structured metadata (optional document metadata for filtering)"],"output_types":["text (generated response)","structured citations (document references with spans)","confidence scores (optional, for grounding quality)"],"categories":["text-generation-language","memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-cohere-command-r-plus-08-2024__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":"Implements function calling through JSON schema-based tool definitions, allowing the model to decide when and how to invoke external APIs or functions. The model generates structured tool calls with parameters that conform to provided schemas, enabling agentic workflows where the model orchestrates multiple tools across reasoning steps without explicit prompt templates.","intents":["Build an AI agent that autonomously calls APIs (weather, database, payment systems) based on user intent","Create a workflow automation system where the model decides which tools to invoke and in what sequence","Implement a code execution agent that generates and validates function calls against strict schemas"],"best_for":["Teams building autonomous agents with deterministic tool contracts","Developers implementing multi-step workflows requiring tool orchestration","Organizations needing strict schema validation for tool invocations (compliance, safety)"],"limitations":["Tool calling requires explicit schema definition — no automatic schema inference from function signatures","Model may hallucinate tool parameters not present in schema; requires validation layer on client side","No built-in retry logic for failed tool calls — orchestration must be handled by caller","Maximum 10-20 concurrent tool definitions before performance degradation"],"requires":["Cohere API key","JSON schema definitions for each tool (OpenAPI 3.0 compatible format)","Client-side tool execution runtime (caller must implement actual function invocation)"],"input_types":["text (user intent/query)","JSON schema (tool definitions)","structured tool results (from previous invocations)"],"output_types":["JSON (tool calls with parameters)","text (reasoning before/after tool use)","structured execution trace (optional)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-cohere-command-r-plus-08-2024__cap_2","uri":"capability://text.generation.language.long.context.processing.with.efficient.attention.mechanisms","name":"long-context processing with efficient attention mechanisms","description":"Processes documents and conversations up to 128K tokens using optimized attention mechanisms (likely sliding window or sparse attention patterns) that reduce computational complexity from O(n²) to near-linear scaling. This enables processing of entire books, codebases, or conversation histories without truncation while maintaining sub-second latency through the 08-2024 performance optimization (25% lower latency vs previous version).","intents":["Analyze entire source code repositories for refactoring or security vulnerabilities in a single request","Summarize long documents (research papers, legal contracts, books) without chunking","Maintain full conversation history in multi-turn applications without context window management"],"best_for":["Document analysis teams processing 50K+ token documents","Code review automation systems analyzing full repositories","Long-form content generation requiring extensive context (research synthesis, technical writing)"],"limitations":["128K token limit still requires chunking for very large codebases (>500K lines); no infinite context","Attention efficiency gains come at cost of slightly reduced coherence in very long sequences (>100K tokens)","Latency increases non-linearly beyond 64K tokens; 128K requests may take 5-10x longer than 8K requests","Memory requirements scale with context length; 128K tokens requires ~16GB GPU memory"],"requires":["Cohere API key","HTTP client with support for large request payloads (>5MB)","Timeout configuration of 60+ seconds for maximum context requests"],"input_types":["text (documents up to 128K tokens)","code (source files, repositories as text)","structured text (markdown, JSON, XML)"],"output_types":["text (analysis, summary, or generation)","structured extraction (from long documents)","code (refactored or generated based on full context)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-cohere-command-r-plus-08-2024__cap_3","uri":"capability://data.processing.analysis.structured.data.extraction.with.schema.guided.generation","name":"structured data extraction with schema-guided generation","description":"Extracts structured information from unstructured text by constraining generation to conform to provided JSON schemas, ensuring output always matches expected data structures. The model generates valid JSON that adheres to field types, required properties, and nested object structures without post-processing or validation failures, enabling reliable ETL pipelines and data enrichment workflows.","intents":["Extract entities (names, dates, amounts) from invoices or contracts into structured JSON","Parse natural language requirements into structured task definitions for workflow automation","Convert unstructured customer feedback into categorized, tagged data for analytics"],"best_for":["Data engineering teams building ETL pipelines with LLM-based extraction","Compliance teams automating document parsing (contracts, regulatory filings)","Product teams enriching unstructured user data for analytics and personalization"],"limitations":["Schema complexity is limited — deeply nested schemas (>5 levels) may cause extraction errors","Extraction accuracy depends on input text clarity; ambiguous or poorly formatted source text reduces precision","No built-in confidence scoring for extracted fields; requires manual validation for high-stakes use cases","Hallucination risk for optional fields — model may invent plausible values if source text is ambiguous"],"requires":["Cohere API key","JSON schema definition for target data structure","Input text (unstructured documents, user input, etc.)"],"input_types":["text (unstructured documents, emails, forms)","JSON schema (extraction target structure)"],"output_types":["JSON (extracted data conforming to schema)","validation errors (if schema constraints violated)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-cohere-command-r-plus-08-2024__cap_4","uri":"capability://search.retrieval.semantic.search.and.relevance.ranking.across.document.collections","name":"semantic search and relevance ranking across document collections","description":"Ranks and retrieves relevant documents from collections based on semantic similarity to queries, using dense vector embeddings computed by the model's encoder. The ranking mechanism considers both semantic relevance and document metadata, enabling hybrid search that combines keyword and semantic signals without requiring separate embedding models or vector databases.","intents":["Find the most relevant documents from a knowledge base to ground a user query","Rank search results by semantic relevance rather than keyword matching","Implement a recommendation system that surfaces similar documents or content"],"best_for":["Teams implementing search features without maintaining separate vector databases","Knowledge base systems requiring semantic ranking of results","Content platforms needing relevance-based recommendations"],"limitations":["Ranking is computed per-request — no pre-computed embeddings or indexing for large collections (>100K documents)","Latency scales linearly with collection size; ranking 10K documents takes 5-10 seconds","No built-in filtering or faceted search — ranking is purely semantic without metadata constraints","Requires documents to be passed in each request; no persistent index or caching"],"requires":["Cohere API key","Document collection as text or structured metadata","Query text"],"input_types":["text (query)","text (documents to rank)"],"output_types":["ranked list (documents with relevance scores)","similarity scores (0-1 range)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-cohere-command-r-plus-08-2024__cap_5","uri":"capability://text.generation.language.multi.language.generation.and.understanding.with.cross.lingual.transfer","name":"multi-language generation and understanding with cross-lingual transfer","description":"Generates and understands text across 100+ languages with shared embedding space enabling cross-lingual transfer — a query in English can retrieve documents in Spanish, and responses can be generated in the user's language without language-specific fine-tuning. The model uses a unified tokenizer and embedding space trained on multilingual corpora, enabling zero-shot language switching within conversations.","intents":["Build a global customer support chatbot that handles queries in any language without language-specific models","Create a translation-aware search system where queries in one language retrieve documents in another","Implement a multilingual content generation system that produces consistent outputs across languages"],"best_for":["Global teams building products for international markets","Customer support platforms serving multilingual user bases","Content platforms requiring cross-lingual search and generation"],"limitations":["Quality varies significantly across languages — high-resource languages (English, Spanish, Mandarin) have better quality than low-resource languages (Icelandic, Swahili)","Cross-lingual transfer works best for similar language families; English-to-Japanese transfer is weaker than English-to-Spanish","Code-switching (mixing languages) may confuse the model; pure single-language inputs are more reliable","Tokenization efficiency varies by language — some languages require 2-3x more tokens than English"],"requires":["Cohere API key","Text input in any supported language (no language detection required)"],"input_types":["text (any language, including code-mixed input)"],"output_types":["text (generated in target language or source language)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-cohere-command-r-plus-08-2024__cap_6","uri":"capability://text.generation.language.instruction.following.with.complex.multi.step.reasoning","name":"instruction-following with complex multi-step reasoning","description":"Follows detailed, multi-step instructions with high fidelity by decomposing complex tasks into intermediate reasoning steps and validating outputs against instruction constraints. The model maintains instruction context across long sequences and handles edge cases specified in instructions without requiring explicit prompt engineering for each variation, using chain-of-thought-like reasoning patterns internally.","intents":["Execute complex data transformation pipelines specified in natural language instructions","Generate code that strictly adheres to architectural constraints and coding standards","Implement business logic automation where precise instruction-following is critical"],"best_for":["Teams automating complex workflows with detailed specifications","Code generation systems requiring strict adherence to architectural patterns","Compliance-heavy domains where instruction fidelity is critical"],"limitations":["Instruction complexity is limited — instructions >2000 tokens may be partially ignored","Contradictory instructions may cause the model to pick one branch arbitrarily without error signaling","Edge cases not explicitly mentioned in instructions may be handled inconsistently","Instruction-following quality degrades with ambiguous or vague specifications"],"requires":["Cohere API key","Detailed, well-structured instructions (natural language or pseudocode)"],"input_types":["text (instructions)","text (input data or context for instruction execution)"],"output_types":["text (output conforming to instruction specification)","code (if instructions specify code generation)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-cohere-command-r-plus-08-2024__cap_7","uri":"capability://text.generation.language.conversational.context.management.with.turn.level.optimization","name":"conversational context management with turn-level optimization","description":"Manages multi-turn conversations with automatic context optimization that selectively retains relevant information across turns while pruning redundant or outdated context. The model tracks conversation state implicitly and can reference earlier turns without explicit context passing, using attention mechanisms to weight recent and relevant turns more heavily than distant turns.","intents":["Build a chatbot that maintains coherent conversations over 50+ turns without manual context management","Create a collaborative assistant that remembers user preferences and past decisions across sessions","Implement a debugging assistant that tracks problem-solving progress across multiple interactions"],"best_for":["Conversational AI systems requiring long-term context retention","Customer support platforms with multi-turn interactions","Collaborative tools where conversation history is critical to task completion"],"limitations":["Context window limits total conversation length to ~4K tokens; older turns are truncated automatically","Model may lose track of context after 20+ turns even within token limits due to attention degradation","No explicit memory mechanism — context is purely attention-based and may miss important details from early turns","Conversation state is not persisted; each API call is stateless and requires full history to be passed"],"requires":["Cohere API key","Client-side conversation history management (caller must track and pass full history)"],"input_types":["text (current user message)","conversation history (previous turns as message array)"],"output_types":["text (response contextually aware of conversation history)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-cohere-command-r-plus-08-2024__cap_8","uri":"capability://safety.moderation.safety.aligned.response.generation.with.harmful.content.filtering","name":"safety-aligned response generation with harmful content filtering","description":"Generates responses that avoid harmful, toxic, or unsafe content through alignment training and built-in safety classifiers that detect and mitigate unsafe outputs. The model refuses requests for illegal activities, violence, or hate speech, and provides explanations for refusals rather than silent filtering, enabling transparent safety boundaries in production systems.","intents":["Deploy a public-facing chatbot that safely handles adversarial or harmful user inputs","Build a content moderation system that flags unsafe outputs before they reach users","Create a compliance-aware assistant that refuses requests violating regulatory requirements"],"best_for":["Public-facing applications requiring robust safety guardrails","Regulated industries (healthcare, finance) with compliance requirements","Platforms with diverse user bases requiring inclusive safety policies"],"limitations":["Safety filtering may over-refuse legitimate requests (false positives); e.g., refusing medical information about contraception","Adversarial prompts may still bypass safety mechanisms through indirect phrasing or roleplay scenarios","Safety policies are fixed and cannot be customized per application without fine-tuning","No granular control over safety thresholds — all-or-nothing refusal without severity levels"],"requires":["Cohere API key","No special configuration required; safety is enabled by default"],"input_types":["text (any user input, including adversarial prompts)"],"output_types":["text (safe response or refusal with explanation)"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-cohere-command-r-plus-08-2024__cap_9","uri":"capability://automation.workflow.batch.processing.with.throughput.optimization.for.high.volume.inference","name":"batch processing with throughput optimization for high-volume inference","description":"Processes large batches of requests (100s-1000s) with optimized throughput that leverages the 08-2024 performance improvements (50% higher throughput). Batching is handled transparently by the API, allowing callers to submit multiple independent requests that are processed in parallel on shared GPU resources, reducing per-request latency through amortized overhead.","intents":["Process thousands of customer support tickets overnight for analysis and categorization","Generate embeddings or rankings for large document collections in batch jobs","Run large-scale content moderation or data enrichment pipelines"],"best_for":["Data engineering teams running offline processing jobs","Analytics platforms requiring bulk text processing","Content platforms processing user-generated content at scale"],"limitations":["Batch processing introduces latency (minutes to hours) compared to real-time API calls","No built-in retry logic for failed requests in batch; requires manual error handling","Batch size limits may apply (typically 100-1000 requests per batch); very large jobs require multiple batches","No priority queuing — batch requests are processed in FIFO order without SLA guarantees"],"requires":["Cohere API key with batch processing enabled","Batch API endpoint (separate from real-time API)","JSONL format for batch input (one request per line)"],"input_types":["JSONL (batch of requests, each with text input and parameters)"],"output_types":["JSONL (batch of responses with results and metadata)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["Cohere API key with Command R+ access","Documents formatted as text or structured metadata (JSON/XML supported)","HTTP client library for REST API integration","Cohere API key","JSON schema definitions for each tool (OpenAPI 3.0 compatible format)","Client-side tool execution runtime (caller must implement actual function invocation)","HTTP client with support for large request payloads (>5MB)","Timeout configuration of 60+ seconds for maximum context requests","JSON schema definition for target data structure","Input text (unstructured documents, user input, etc.)"],"failure_modes":["Document grounding requires explicit document passing per request — no persistent vector store integration","Citation accuracy depends on document formatting and clarity; poorly structured documents may produce inaccurate citations","Context window limits multi-turn conversations to ~4K tokens of history before truncation","Tool calling requires explicit schema definition — no automatic schema inference from function signatures","Model may hallucinate tool parameters not present in schema; requires validation layer on client side","No built-in retry logic for failed tool calls — orchestration must be handled by caller","Maximum 10-20 concurrent tool definitions before performance degradation","128K token limit still requires chunking for very large codebases (>500K lines); no infinite context","Attention efficiency gains come at cost of slightly reduced coherence in very long sequences (>100K tokens)","Latency increases non-linearly beyond 64K tokens; 128K requests may take 5-10x longer than 8K requests","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.45,"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-plus-08-2024","compare_url":"https://unfragile.ai/compare?artifact=cohere-command-r-plus-08-2024"}},"signature":"XGMAS0DiQw4bPQshLC/NHRd66ul4ew+p8+yBgdMjdpx3cgM5t6VYughEphb5Vlu/zAR4dwHIVJMo2Rpste1vAg==","signedAt":"2026-06-21T01:39:05.578Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/cohere-command-r-plus-08-2024","artifact":"https://unfragile.ai/cohere-command-r-plus-08-2024","verify":"https://unfragile.ai/api/v1/verify?slug=cohere-command-r-plus-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"}}