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The model maintains semantic coherence across the full context span without requiring context windowing or summarization strategies, allowing builders to pass complete documents or lengthy conversation threads without truncation.","intents":["I need to process an entire research paper or legal document in one request without losing context","I want to maintain full conversation history without managing context windows manually","I need to analyze long-form content like books or technical specifications end-to-end"],"best_for":["enterprise document analysis teams","RAG system builders handling large knowledge bases","developers building long-running conversational agents"],"limitations":["128K token limit is absolute ceiling per request — cannot exceed in single inference","Inference latency increases with context length; no published latency curves provided","Token counting must be done client-side before submission to avoid rejection"],"requires":["Ollama runtime (any version supporting command-r-plus)","Sufficient GPU/CPU memory to load 104B parameter model","59GB disk space for model weights"],"input_types":["text","chat message sequences","document content as plain text"],"output_types":["text","streaming text chunks"],"categories":["text-generation-language","enterprise-document-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-command-r-plus__cap_1","uri":"capability://memory.knowledge.retrieval.augmented.generation.with.inline.citations","name":"retrieval-augmented generation with inline citations","description":"Integrates external knowledge sources into generation by accepting retrieved documents/passages as context and producing citations inline with generated text, reducing hallucinations through grounding in provided source material. The model learns to reference specific passages and attribute claims to sources during generation, enabling builders to verify factual claims against the original documents without post-hoc citation extraction.","intents":["I want to ground LLM responses in my knowledge base with verifiable citations","I need to reduce hallucinations by forcing the model to cite sources for claims","I want to build a fact-checked Q&A system that shows where answers come from"],"best_for":["knowledge base Q&A system builders","enterprise search teams requiring citation trails","compliance-heavy industries needing audit trails for AI responses"],"limitations":["Citation accuracy depends on quality of retrieved documents — garbage in, garbage out","No quantitative hallucination reduction metrics published; 'reduces' is qualitative claim","Citation format/structure not standardized in documentation; implementation details unknown","Requires external retrieval system (vector DB, BM25, etc.) — not built-in"],"requires":["External document retrieval system (Pinecone, Weaviate, Elasticsearch, etc.)","Pre-chunked and indexed knowledge base","Prompt engineering to structure retrieved documents for citation"],"input_types":["text query","retrieved document passages","chat messages with context"],"output_types":["text with inline citations","structured citation metadata"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-command-r-plus__cap_2","uri":"capability://tool.use.integration.tool.use.and.function.calling.for.business.process.automation","name":"tool-use and function calling for business process automation","description":"Supports structured function calling via tool schemas, enabling the model to invoke external APIs, databases, or business logic by generating properly-formatted function calls in response to user requests. The model learns to decompose tasks into tool invocations, handle multi-step workflows, and chain tool outputs as inputs to subsequent calls, enabling agentic automation of business processes without explicit prompt engineering for each tool.","intents":["I want the model to automatically call APIs or database functions based on user requests","I need to build an agent that can chain multiple tool calls to complete complex workflows","I want to automate business processes like ticket creation, data lookup, or report generation"],"best_for":["enterprise automation teams building internal AI agents","developers integrating LLMs into existing business systems","teams building customer-facing chatbots with backend integrations"],"limitations":["Tool calling accuracy depends on schema clarity and model's understanding of tool semantics","No built-in error handling or retry logic for failed tool calls — requires wrapper implementation","Maximum number of tools per request unknown; no documented limits provided","Tool call format/schema specification not detailed in available documentation"],"requires":["Tool schema definitions (JSON Schema or similar format)","External tool/API implementations to execute called functions","Wrapper code to parse model output and invoke actual tools","Error handling layer for tool execution failures"],"input_types":["text query","chat messages with tool context","tool schema definitions"],"output_types":["structured tool calls","function invocation parameters","text responses with tool results"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-command-r-plus__cap_3","uri":"capability://text.generation.language.multilingual.text.generation.across.10.languages","name":"multilingual text generation across 10 languages","description":"Generates coherent text in 10 key languages with maintained semantic quality and cultural context awareness, enabling single-model deployment for global business operations without language-specific model switching. The model applies shared transformer weights across languages, allowing knowledge transfer and consistent behavior across linguistic boundaries while maintaining language-specific nuances in generation.","intents":["I need to serve customers in multiple languages without deploying separate models","I want to translate and generate content in non-English languages with consistent quality","I need a single model for global operations across different language markets"],"best_for":["global enterprises with multi-language customer bases","SaaS platforms serving international markets","teams building localized chatbots or content generation systems"],"limitations":["Specific 10 supported languages not documented — must verify on HuggingFace or test","No language-specific benchmark scores provided; quality variance across languages unknown","Language detection must be handled externally — model does not auto-detect input language","Code-switching (mixing languages) behavior undocumented"],"requires":["Language identification logic (external library or prompt-based)","Ollama runtime with command-r-plus model loaded","Awareness of supported language list (not provided in documentation)"],"input_types":["text in any of 10 supported languages","chat messages in supported languages"],"output_types":["text in requested language","streaming text in target language"],"categories":["text-generation-language","internationalization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-command-r-plus__cap_4","uri":"capability://automation.workflow.local.inference.via.ollama.with.unlimited.usage","name":"local inference via ollama with unlimited usage","description":"Runs the 104B parameter model entirely on user-owned hardware via Ollama runtime, enabling unlimited inference without API rate limits, token quotas, or per-request costs. The model executes locally with full control over inference parameters, caching, and resource allocation, allowing builders to optimize for latency, throughput, or cost based on their hardware constraints without external service dependencies.","intents":["I want to run a large language model without cloud API costs or rate limits","I need to keep all data on-premises for compliance or privacy reasons","I want to optimize inference latency and throughput for my specific hardware"],"best_for":["enterprises with strict data residency requirements","teams with high inference volume seeking cost optimization","developers building offline-capable applications","organizations with existing GPU infrastructure"],"limitations":["Requires 59GB disk space for model weights — significant storage footprint","Hardware requirements for acceptable inference speed unknown; no published specs","Inference speed varies dramatically by hardware (GPU type, VRAM, CPU) — no benchmarks provided","No built-in load balancing or multi-GPU orchestration in Ollama","Model quantization method unknown — may impact quality vs speed tradeoff"],"requires":["Ollama runtime installed (https://ollama.com)","59GB available disk space","GPU with sufficient VRAM (exact requirement unknown) or CPU for inference","Network connectivity for initial model download only"],"input_types":["text","chat message sequences"],"output_types":["text","streaming text via HTTP API"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-command-r-plus__cap_5","uri":"capability://automation.workflow.cloud.deployment.with.usage.based.gpu.time.billing","name":"cloud deployment with usage-based gpu time billing","description":"Runs Command R Plus on Cohere/Ollama cloud infrastructure with billing based on GPU compute time rather than token counts, offering three pricing tiers (Free, Pro $20/mo, Max $100/mo) with different concurrency limits and session/weekly usage caps. The billing model charges for actual GPU time consumed during inference, allowing variable costs based on model size and inference duration rather than fixed per-token pricing.","intents":["I want to use Command R Plus without managing my own hardware","I need predictable monthly costs with tiered pricing based on usage level","I want to scale from free tier testing to production without changing code"],"best_for":["startups and small teams without GPU infrastructure","developers prototyping before committing to local deployment","teams with variable inference loads that fit within tier limits"],"limitations":["Free tier: 1 concurrent model, light usage limits (exact token/time limits unknown)","Pro tier: 3 concurrent models, day-to-day usage limits (exact limits unknown)","Max tier: 10 concurrent models, heavy sustained usage (exact limits unknown)","Session limits reset every 5 hours; weekly limits reset every 7 days (exact quota amounts unknown)","Requests exceeding concurrency limits are queued with fixed queue limit (queue size unknown)","GPU time billing model less predictable than per-token pricing for variable workloads","No published pricing per GPU-hour — cost calculation opaque to users"],"requires":["Ollama Cloud account (free signup)","Internet connectivity for API calls","No local hardware required"],"input_types":["text","chat message sequences"],"output_types":["text","streaming text via HTTP API"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-command-r-plus__cap_6","uri":"capability://tool.use.integration.rest.api.and.language.sdk.access.via.ollama","name":"rest api and language sdk access via ollama","description":"Exposes Command R Plus through standardized REST API endpoints and language-specific SDKs (Python, JavaScript/Node.js) via Ollama, enabling integration into applications without custom HTTP handling. The API uses standard chat message format (`{role, content}`) compatible with OpenAI-style interfaces, allowing drop-in replacement of other models with minimal code changes. Streaming responses are supported via HTTP chunked transfer encoding for real-time output.","intents":["I want to integrate Command R Plus into my application with minimal code changes","I need to use the model from Python or JavaScript without learning a custom API","I want streaming responses for real-time user-facing applications"],"best_for":["application developers integrating LLMs into existing codebases","teams familiar with OpenAI API patterns seeking compatible alternatives","builders requiring streaming responses for interactive UIs"],"limitations":["REST API endpoint format not fully documented in provided materials — requires Ollama docs","SDK feature parity unknown — Python and JavaScript SDKs may have different capabilities","No built-in authentication/authorization for multi-tenant scenarios","Streaming response handling varies by language SDK — no unified error handling documented"],"requires":["Ollama runtime running locally (for local deployment) or Ollama Cloud account (for cloud)","Python 3.7+ (for Python SDK) or Node.js 14+ (for JavaScript SDK)","HTTP client library (requests, fetch, etc.) for custom integrations"],"input_types":["JSON chat message format","text prompts"],"output_types":["JSON response with text content","streaming JSON chunks (for streaming mode)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-command-r-plus__cap_7","uri":"capability://code.generation.editing.code.generation.for.enterprise.applications","name":"code generation for enterprise applications","description":"Generates code across multiple programming languages for enterprise use cases, leveraging the 104B parameter capacity and enterprise-optimized training to produce production-quality code with business logic understanding. The model integrates with pre-built applications (Claude Code, Codex, OpenCode, OpenClaw, Hermes Agent) that wrap code generation with IDE integration, testing frameworks, and deployment pipelines specific to enterprise workflows.","intents":["I want to generate code for enterprise applications with business logic understanding","I need to integrate code generation into my IDE or development workflow","I want to use pre-built code generation applications optimized for enterprise patterns"],"best_for":["enterprise development teams building business applications","developers seeking code generation with domain-specific knowledge","teams using Ollama-integrated IDEs (Claude Code, Codex, OpenCode)"],"limitations":["Code generation quality not benchmarked against alternatives — no metrics provided","Pre-built applications (Claude Code, Codex, etc.) are separate tools — integration details unknown","No explicit support for specific languages documented; inferred from 'code generation' claim","No built-in code execution or testing — generated code must be validated externally","Enterprise-specific patterns not detailed — unclear what makes it 'enterprise-optimized'"],"requires":["Ollama runtime with command-r-plus loaded","Optional: IDE integration (Claude Code, Codex, OpenCode, etc.)","External code execution/testing framework"],"input_types":["natural language code requests","code snippets for completion/refactoring","business logic descriptions"],"output_types":["code in multiple programming languages","code explanations","refactored code"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-command-r-plus__cap_8","uri":"capability://text.generation.language.streaming.text.output.for.real.time.applications","name":"streaming text output for real-time applications","description":"Outputs generated text incrementally via HTTP streaming (chunked transfer encoding), enabling real-time display of model output as it's generated rather than waiting for complete response. Streaming reduces perceived latency in user-facing applications by showing partial results immediately, allowing users to read early tokens while the model continues generating later tokens. Both local (Ollama) and cloud deployments support streaming via standard HTTP mechanisms.","intents":["I want to show model output in real-time as it's being generated","I need to reduce perceived latency in chatbot interfaces","I want to enable user interruption of long-running generations"],"best_for":["interactive chatbot and UI builders","real-time customer support applications","developers building streaming-aware frontend applications"],"limitations":["Streaming response handling varies by language SDK — no unified error handling documented","Token-by-token streaming may increase total latency vs batch processing for non-interactive use cases","Streaming requires persistent HTTP connection — incompatible with some proxy/firewall configurations","No built-in backpressure handling — client must manage buffer overflow for slow consumers"],"requires":["HTTP client supporting chunked transfer encoding (most modern clients do)","Streaming-aware application code to handle partial responses","Ollama runtime (local or cloud)"],"input_types":["text prompts","chat messages"],"output_types":["streaming text chunks via HTTP","partial JSON responses"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-command-r-plus__cap_9","uri":"capability://text.generation.language.enterprise.optimized.conversational.ai.for.business.use.cases","name":"enterprise-optimized conversational ai for business use cases","description":"Model is explicitly trained and optimized for enterprise business scenarios (stated as 'purpose-built to excel at real-world enterprise use cases'), incorporating domain knowledge and patterns relevant to business operations, customer service, and organizational workflows. The training approach prioritizes accuracy, reliability, and business logic understanding over general-purpose capabilities, enabling deployment in mission-critical business applications with reduced hallucination and improved task completion rates.","intents":["I need a conversational AI that understands business processes and domain terminology","I want to deploy an LLM in production for customer-facing business applications","I need reliable AI for enterprise workflows with minimal hallucinations"],"best_for":["enterprise customer service teams","business process automation initiatives","organizations deploying LLMs in mission-critical applications"],"limitations":["Enterprise optimization is claimed but not quantified — no benchmarks vs general-purpose models","Specific business domains optimized for unknown — unclear which industries benefit most","No published failure mode analysis or bias assessment for enterprise contexts","Hallucination reduction claimed but not measured — no quantitative metrics provided"],"requires":["Ollama runtime","Understanding of enterprise use case requirements","Integration with business systems (CRM, ERP, etc.) for full value"],"input_types":["business process queries","customer service requests","enterprise domain-specific prompts"],"output_types":["business-relevant text responses","structured business data","tool calls for business systems"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"high","permissions":["Ollama runtime (any version supporting command-r-plus)","Sufficient GPU/CPU memory to load 104B parameter model","59GB disk space for model weights","External document retrieval system (Pinecone, Weaviate, Elasticsearch, etc.)","Pre-chunked and indexed knowledge base","Prompt engineering to structure retrieved documents for citation","Tool schema definitions (JSON Schema or similar format)","External tool/API implementations to execute called functions","Wrapper code to parse model output and invoke actual tools","Error handling layer for tool execution failures"],"failure_modes":["128K token limit is absolute ceiling per request — cannot exceed in single inference","Inference latency increases with context length; no published latency curves provided","Token counting must be done client-side before submission to avoid rejection","Citation accuracy depends on quality of retrieved documents — garbage in, garbage out","No quantitative hallucination reduction metrics published; 'reduces' is qualitative claim","Citation format/structure not standardized in documentation; implementation details unknown","Requires external retrieval system (vector DB, BM25, etc.) — not built-in","Tool calling accuracy depends on schema clarity and model's understanding of tool semantics","No built-in error handling or retry logic for failed tool calls — requires wrapper implementation","Maximum number of tools per request unknown; no documented limits provided","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.3,"ecosystem":0.38999999999999996,"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":"2026-05-24T12:16:24.483Z","last_scraped_at":"2026-05-03T15:20:48.403Z","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-plus","compare_url":"https://unfragile.ai/compare?artifact=command-r-plus"}},"signature":"zX6nDZ80TNu4k9u1LqovxAal+74F7mp7A63gUgADDAyMD+ahK+WdZu8nm/N6Wi8LtZpowl034mGiR7wOXQuGDg==","signedAt":"2026-06-22T22:24:22.403Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/command-r-plus","artifact":"https://unfragile.ai/command-r-plus","verify":"https://unfragile.ai/api/v1/verify?slug=command-r-plus","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"}}