{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-cohere-command-a","slug":"cohere-command-a","name":"Cohere: Command A","type":"model","url":"https://openrouter.ai/models/cohere~command-a","page_url":"https://unfragile.ai/cohere-command-a","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-a__cap_0","uri":"capability://text.generation.language.multilingual.instruction.following.with.256k.context.window","name":"multilingual instruction-following with 256k context window","description":"Command A processes natural language instructions across 100+ languages with a 256k token context window, enabling long-document understanding and multi-turn conversations without context truncation. The model uses a transformer-based architecture trained on diverse multilingual corpora with instruction-tuning to follow user intents accurately across linguistic boundaries. This extended context allows processing of entire codebases, research papers, or conversation histories in a single forward pass.","intents":["Process long documents in non-English languages without losing context","Maintain coherent multi-turn conversations spanning 50+ exchanges","Analyze entire codebases or research papers in a single request","Build multilingual chatbots that understand nuanced instructions"],"best_for":["Teams building multilingual customer support agents","Developers creating code analysis tools for large repositories","Organizations processing long-form content in multiple languages"],"limitations":["256k context window still has practical latency tradeoffs — processing full window adds 2-5 seconds vs 8k context","Multilingual performance varies by language; low-resource languages may have degraded accuracy","Context length doesn't guarantee perfect recall of information at document boundaries"],"requires":["API access via OpenRouter or direct Cohere API","Network connectivity for inference","Input text encoded in UTF-8"],"input_types":["text (natural language instructions)","code (for analysis and generation tasks)","structured prompts with examples"],"output_types":["text (natural language responses)","code (generated or refactored)","structured JSON (when prompted)"],"categories":["text-generation-language","multilingual-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-cohere-command-a__cap_1","uri":"capability://tool.use.integration.agentic.reasoning.with.tool.use.integration","name":"agentic reasoning with tool-use integration","description":"Command A supports function calling and tool orchestration through a schema-based interface, enabling the model to decompose complex tasks into subtasks and invoke external APIs or functions. The model learns to generate structured tool calls (function name, parameters) based on user intent, with built-in support for multi-step reasoning where tool outputs inform subsequent decisions. This is implemented via instruction-tuning on tool-use examples and constrained decoding to ensure valid JSON output.","intents":["Build autonomous agents that call APIs to fetch data, compute results, or trigger actions","Create task decomposition workflows where the model decides which tools to use and in what order","Implement retrieval-augmented generation by having the model call search/database functions","Orchestrate multi-step workflows combining model reasoning with external system calls"],"best_for":["Developers building autonomous agents with external tool dependencies","Teams implementing RAG systems where the model decides what to retrieve","Organizations automating multi-step business processes"],"limitations":["Tool calling accuracy degrades with complex nested schemas or >10 tools in a single request","No built-in error recovery — failed tool calls require explicit retry logic in application code","Latency increases with tool invocation overhead; each tool call adds network round-trip time"],"requires":["API access via OpenRouter or Cohere API","Tool definitions in JSON schema format","Application-level orchestration logic to execute tool calls and feed results back"],"input_types":["text (user intent/task description)","JSON schema (tool definitions)","structured context (previous tool outputs)"],"output_types":["structured tool calls (JSON with function name and parameters)","text (reasoning or final response)","chained tool invocations"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-cohere-command-a__cap_2","uri":"capability://code.generation.editing.code.generation.and.analysis.with.language.agnostic.understanding","name":"code generation and analysis with language-agnostic understanding","description":"Command A generates, completes, and analyzes code across 40+ programming languages by leveraging transformer-based semantic understanding rather than syntax-specific rules. The model is trained on diverse code repositories and can perform tasks like code completion, bug detection, refactoring suggestions, and test generation. It understands code semantics (variable scope, function dependencies, type relationships) and can generate contextually appropriate code that integrates with existing codebases.","intents":["Generate code snippets or full functions from natural language descriptions","Complete partial code with context-aware suggestions","Analyze code for bugs, security vulnerabilities, or performance issues","Refactor code to improve readability or apply design patterns","Generate unit tests or documentation for existing code"],"best_for":["Solo developers using AI-assisted coding in IDEs or terminals","Teams building code review automation tools","Organizations migrating codebases or modernizing legacy systems"],"limitations":["Code generation accuracy decreases for domain-specific languages or niche frameworks","Cannot execute code or verify correctness — generated code requires human review and testing","Performance on very large files (>10k lines) degrades due to context limitations","No built-in awareness of project-specific conventions or internal libraries"],"requires":["API access via OpenRouter or Cohere API","Code provided as text input (UTF-8 encoded)","Optional: language specification for better accuracy"],"input_types":["code (partial or complete)","natural language descriptions","code snippets with context"],"output_types":["code (generated or refactored)","analysis results (text or structured)","test cases"],"categories":["code-generation-editing","developer-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-cohere-command-a__cap_3","uri":"capability://text.generation.language.long.context.document.summarization.and.extraction","name":"long-context document summarization and extraction","description":"Command A summarizes and extracts structured information from documents up to 256k tokens by maintaining coherence across the entire document and identifying key information without losing context. The model uses attention mechanisms to weight important sections and can extract specific data (entities, relationships, facts) while preserving document structure. This enables processing of entire research papers, legal documents, or knowledge bases in a single pass.","intents":["Summarize long research papers or technical documentation into concise overviews","Extract structured data (tables, entities, relationships) from unstructured documents","Identify key sections or relevant passages in large documents","Generate executive summaries of multi-page reports or contracts"],"best_for":["Legal and compliance teams processing contracts or regulatory documents","Research organizations analyzing academic papers at scale","Content teams creating summaries for knowledge bases or documentation"],"limitations":["Summarization quality depends on document structure; poorly formatted documents may produce incoherent summaries","Extraction accuracy for domain-specific terminology requires domain-specific prompting","Processing 256k tokens adds latency (2-5 seconds) vs shorter documents","No built-in support for multi-document summarization or cross-document relationships"],"requires":["API access via OpenRouter or Cohere API","Document text in UTF-8 encoding","Optional: structured extraction schema (JSON) for targeted data extraction"],"input_types":["text (documents, articles, papers)","structured prompts (extraction instructions)","JSON schema (for structured extraction)"],"output_types":["text (summaries, extracted text)","structured data (JSON with extracted fields)","key passages or citations"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-cohere-command-a__cap_4","uri":"capability://text.generation.language.multi.turn.conversational.context.management","name":"multi-turn conversational context management","description":"Command A maintains coherent multi-turn conversations by tracking conversation history and context across 50+ exchanges without losing semantic understanding. The model uses attention mechanisms to weight recent and relevant context, enabling it to reference earlier statements, correct misunderstandings, and maintain consistent personality or knowledge across turns. This is implemented through instruction-tuning on dialogue data and careful context window management.","intents":["Build chatbots that maintain context across long conversations","Create interactive tutoring systems that remember student progress","Implement customer support agents that reference previous interactions","Develop conversational interfaces for complex workflows"],"best_for":["Teams building customer support chatbots","Educational platforms creating interactive learning experiences","Organizations implementing conversational interfaces for internal tools"],"limitations":["Context window fills up with long conversations — requires explicit history pruning or summarization after 50+ turns","Model may hallucinate or misremember details from early conversation turns","No built-in persistence — conversation history must be stored externally","Performance degrades if conversation includes many unrelated topics"],"requires":["API access via OpenRouter or Cohere API","Application-level conversation history management","External storage for persistence (database, cache, etc.)"],"input_types":["text (user messages)","conversation history (previous turns)","system prompts (personality/behavior definition)"],"output_types":["text (conversational responses)","structured data (when requested)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-cohere-command-a__cap_5","uri":"capability://text.generation.language.instruction.following.with.few.shot.learning","name":"instruction-following with few-shot learning","description":"Command A follows complex, nuanced instructions by leveraging instruction-tuning and few-shot learning capabilities, allowing users to provide examples of desired behavior and have the model generalize to new inputs. The model can learn task-specific patterns from 2-5 examples without fine-tuning, adapting its behavior based on provided context. This is implemented through transformer attention mechanisms that weight example patterns and apply them to new inputs.","intents":["Teach the model custom output formats or styles through examples","Adapt the model to domain-specific terminology or conventions","Implement task-specific behaviors without fine-tuning","Create consistent responses across multiple API calls"],"best_for":["Developers building specialized AI applications with custom requirements","Teams implementing domain-specific language models without fine-tuning","Organizations standardizing AI output formats across applications"],"limitations":["Few-shot learning effectiveness depends on example quality and relevance","Performance plateaus after 5-10 examples; more examples don't guarantee better results","Examples consume context window tokens, reducing space for actual task input","No persistent learning — examples must be provided with every request"],"requires":["API access via OpenRouter or Cohere API","Well-crafted examples demonstrating desired behavior","Clear task instructions"],"input_types":["text (task instructions)","examples (input-output pairs)","task input"],"output_types":["text (following learned patterns)","structured data (if examples demonstrate structure)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-cohere-command-a__cap_6","uri":"capability://memory.knowledge.semantic.search.and.retrieval.augmented.generation.integration","name":"semantic search and retrieval-augmented generation integration","description":"Command A integrates with semantic search systems by accepting retrieved context and generating responses grounded in that context, enabling retrieval-augmented generation (RAG) workflows. The model can process retrieved documents or passages and synthesize answers that cite or reference the source material. This is implemented through instruction-tuning on RAG tasks and the model's ability to maintain context awareness of source documents.","intents":["Build RAG systems where the model answers questions based on retrieved documents","Create knowledge-base chatbots that cite sources","Implement fact-checking systems that ground responses in retrieved evidence","Generate summaries of search results"],"best_for":["Teams implementing RAG systems for knowledge bases or documentation","Organizations building fact-grounded chatbots","Search platforms adding generative answer capabilities"],"limitations":["Model may hallucinate or ignore retrieved context if instructions are unclear","Performance depends on quality and relevance of retrieved documents","No built-in semantic search — requires external vector database or search system","Citation accuracy varies; model may misattribute information to wrong sources"],"requires":["API access via OpenRouter or Cohere API","External semantic search system (vector database, search engine, etc.)","Retrieved documents or passages to provide as context"],"input_types":["text (user query)","retrieved documents (context)","instructions (how to use context)"],"output_types":["text (grounded response)","citations (source references)","structured data (if requested)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-cohere-command-a__cap_7","uri":"capability://data.processing.analysis.structured.output.generation.with.schema.validation","name":"structured output generation with schema validation","description":"Command A generates structured outputs (JSON, XML, YAML) that conform to user-specified schemas through instruction-tuning and constrained decoding. The model can be prompted to output data in specific formats with guaranteed schema compliance, enabling reliable integration with downstream systems. This is implemented via instruction-tuning on structured output tasks and optional constrained decoding to enforce schema validity.","intents":["Generate JSON responses for API integration","Extract structured data from unstructured text","Create validated configuration files or data exports","Implement data transformation pipelines"],"best_for":["Developers building AI-powered data pipelines","Teams implementing AI-driven ETL systems","Organizations automating data extraction and transformation"],"limitations":["Schema complexity affects generation accuracy; deeply nested schemas may produce invalid output","Constrained decoding (if used) adds latency and may limit output diversity","Model may struggle with domain-specific data types or validation rules","No built-in schema validation — application must validate output"],"requires":["API access via OpenRouter or Cohere API","JSON schema or format specification","Clear instructions on desired output structure"],"input_types":["text (task description or unstructured data)","JSON schema (output format specification)","examples (desired output format)"],"output_types":["JSON (structured data)","XML or YAML (alternative formats)","validated structured output"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["API access via OpenRouter or direct Cohere API","Network connectivity for inference","Input text encoded in UTF-8","API access via OpenRouter or Cohere API","Tool definitions in JSON schema format","Application-level orchestration logic to execute tool calls and feed results back","Code provided as text input (UTF-8 encoded)","Optional: language specification for better accuracy","Document text in UTF-8 encoding","Optional: structured extraction schema (JSON) for targeted data extraction"],"failure_modes":["256k context window still has practical latency tradeoffs — processing full window adds 2-5 seconds vs 8k context","Multilingual performance varies by language; low-resource languages may have degraded accuracy","Context length doesn't guarantee perfect recall of information at document boundaries","Tool calling accuracy degrades with complex nested schemas or >10 tools in a single request","No built-in error recovery — failed tool calls require explicit retry logic in application code","Latency increases with tool invocation overhead; each tool call adds network round-trip time","Code generation accuracy decreases for domain-specific languages or niche frameworks","Cannot execute code or verify correctness — generated code requires human review and testing","Performance on very large files (>10k lines) degrades due to context limitations","No built-in awareness of project-specific conventions or internal libraries","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-a","compare_url":"https://unfragile.ai/compare?artifact=cohere-command-a"}},"signature":"Q1VcyToS0k6skEg9yObXJey7QxZ9IhOC1L+4fpb+UkLFQMkcqQK4933yhlHwR623jfYw4oPdBnL0/paWoUafBg==","signedAt":"2026-06-19T17:49:02.190Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/cohere-command-a","artifact":"https://unfragile.ai/cohere-command-a","verify":"https://unfragile.ai/api/v1/verify?slug=cohere-command-a","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"}}