{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"cohere-api","slug":"cohere-api","name":"Cohere API","type":"api","url":"https://cohere.com","page_url":"https://unfragile.ai/cohere-api","categories":["llm-apis","rag-knowledge"],"tags":[],"pricing":{"model":"usage","free":false,"starting_price":"$0.50/1M tokens"},"status":"active","verified":false},"capabilities":[{"id":"cohere-api__cap_0","uri":"capability://text.generation.language.multilingual.text.generation.with.enterprise.reasoning","name":"multilingual text generation with enterprise reasoning","description":"Command R+ model generates coherent text and multi-turn conversational responses across 23 languages using a transformer-based architecture optimized for enterprise reasoning tasks. The model integrates with RAG systems to ground generation in retrieved documents, enabling fact-anchored outputs that cite source data. Supports streaming responses for real-time user interaction and handles complex reasoning chains for multi-step problem solving.","intents":["Generate customer-facing content in multiple languages without building separate models per language","Build conversational AI agents that reason over enterprise data and provide cited answers","Create chatbots that maintain context across multi-turn conversations while grounding responses in company documents","Implement fact-checked text generation that references specific source documents for compliance and transparency"],"best_for":["Enterprise teams building multilingual customer support systems","Organizations requiring RAG-grounded generation for compliance and auditability","Teams migrating from closed-source LLMs to managed API solutions with data residency options"],"limitations":["Context window size unknown — no documented token limit for input or output","Streaming latency profile unknown — no SLA or response time benchmarks provided","Language support limited to 23 languages (vs 100+ for embeddings), creating potential bottlenecks in truly global deployments","Fine-tuning capabilities exist but technical details (training data requirements, cost, turnaround time) are undocumented"],"requires":["Production API key (requires application approval, not auto-issued)","Cohere account with billing setup for pay-as-you-go usage","Per-token pricing structure unknown — actual cost calculation requires contacting sales"],"input_types":["text (natural language prompts)","structured context (documents for RAG integration)","conversation history (multi-turn chat state)"],"output_types":["text (generated responses)","streaming tokens (for real-time UI updates)","structured metadata (citation references, confidence scores — if supported)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cohere-api__cap_1","uri":"capability://data.processing.analysis.semantic.text.embeddings.with.100.language.support","name":"semantic text embeddings with 100+ language support","description":"Embed 4 model converts text into fixed-dimensional vector representations (embeddings) that capture semantic meaning across 100+ languages using a transformer-based encoder architecture. Embeddings enable semantic search, document clustering, and similarity comparisons without requiring explicit keyword matching. Available in Small and Medium tier variants for deployment flexibility, with support for both API-based and dedicated Model Vault instance deployment for data privacy.","intents":["Build semantic search systems that find documents by meaning rather than keyword matching","Implement document deduplication and clustering across multilingual corpora","Create recommendation systems that match users to content based on semantic similarity","Enable vector database integration (Pinecone, Weaviate, Milvus) for large-scale similarity search"],"best_for":["Teams building search systems for multilingual content (100+ language support is rare)","Organizations with strict data residency requirements using Model Vault dedicated instances","Enterprises implementing RAG pipelines where embeddings are the retrieval backbone"],"limitations":["Embedding dimension size unknown — affects vector database storage and query latency","Maximum input length per embedding unknown — may require chunking strategies for long documents","Batch processing capabilities unknown — unclear if bulk embedding requests are optimized or require sequential calls","Model Vault pricing ($2,500–$3,250/month for Small/Medium tiers) creates high barrier for cost-sensitive projects"],"requires":["Production API key or Model Vault instance subscription","Vector database or similarity search infrastructure (external)","For Model Vault: VPC or on-premises deployment capability"],"input_types":["text (documents, queries, sentences)","batch text arrays (for bulk embedding)"],"output_types":["dense vectors (fixed-dimensional embeddings)","similarity scores (if using reranking in combination)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cohere-api__cap_10","uri":"capability://planning.reasoning.north.platform.for.ai.agent.orchestration.and.workflow.automation","name":"north platform for ai agent orchestration and workflow automation","description":"North is an all-in-one AI platform built on Cohere's models that provides pre-built agents for routine tasks (data retrieval, document processing, customer support) and workflow automation capabilities. Agents are composed of generation, retrieval, and reasoning components with built-in guardrails and monitoring. Enables non-technical users to build AI workflows via UI without coding, while supporting advanced customization for developers.","intents":["Build AI agents for routine business tasks (customer support, data entry, document processing) without custom development","Automate multi-step workflows that require reasoning and tool use (e.g., 'retrieve customer data → generate response → send email')","Monitor and audit AI agent behavior for compliance and safety"],"best_for":["Enterprise teams seeking low-code/no-code AI agent deployment","Organizations with routine, well-defined tasks suitable for automation","Teams prioritizing built-in safety and monitoring over custom agent architectures"],"limitations":["Agent capabilities unknown — no documentation on which tasks are pre-built vs custom-buildable","Workflow customization limits unknown — unclear how much agents can be tailored to non-standard processes","Pricing unknown — requires 'getting in touch' for custom enterprise pricing, blocking cost estimation","Integration capabilities unknown — unclear which external systems (CRM, ERP, databases) agents can connect to","Monitoring/audit trail details unknown — no documentation on what events are logged or how long logs are retained"],"requires":["Cohere account with North product access (custom pricing)","Integration credentials for connected systems (CRM, email, databases, etc.)"],"input_types":["task definitions (via UI or API)","external system credentials"],"output_types":["agent execution results","audit logs (format unknown)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cohere-api__cap_11","uri":"capability://text.generation.language.multi.language.support.across.23.languages.for.generation","name":"multi-language support across 23 languages for generation","description":"Command R+ generative model supports 23 languages for text generation and conversation, enabling multilingual chatbots and content creation without language-specific model selection or switching. Language support is built into single model rather than requiring separate language-specific models.","intents":["Build a single chatbot that serves users in 23 languages without language detection or model switching","Generate content (marketing copy, documentation, support responses) in multiple languages from single API","Create multilingual customer support agents that maintain conversation context across language switches","Support global teams with native-language interfaces without separate model deployments"],"best_for":["Global SaaS platforms serving users in 23+ languages","Multinational enterprises with multilingual customer support requirements","Content platforms and publishers creating content in multiple languages"],"limitations":["Language list unknown — no specification of which 23 languages are supported","Language detection unknown — no specification of whether language must be specified or auto-detected","Quality variance unknown — no documentation of whether generation quality is consistent across all 23 languages","Language mixing unknown — no specification of whether single request can mix languages or must be single-language"],"requires":["Production API key","Text input in one of 23 supported languages (language list unknown)"],"input_types":["text (in one of 23 supported languages)"],"output_types":["text (in same language as input)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cohere-api__cap_2","uri":"capability://search.retrieval.search.result.relevance.ranking.with.personalization","name":"search result relevance ranking with personalization","description":"Rerank models (3.5, 4 Fast, 4 Pro) re-score search results to optimize relevance ranking using learned-to-rank algorithms that consider semantic similarity, user context, and interaction history. Operates as a post-processing layer after initial retrieval (from BM25, vector search, or hybrid systems), dynamically adjusting result order based on user preferences and query intent. Available in multiple performance tiers (Fast for latency-sensitive, Pro for accuracy-focused) and deployment options (API or Model Vault).","intents":["Improve search result quality by re-ranking initial retrieval results from vector or keyword search","Personalize search results based on user history, preferences, and interaction patterns","Reduce irrelevant results in large document collections without reindexing","Optimize search relevance for domain-specific queries (legal, medical, technical documentation)"],"best_for":["Teams operating large search systems (e-commerce, documentation, knowledge bases) where initial retrieval is imperfect","Organizations with user interaction data available for personalization signals","Latency-sensitive applications using Rerank 4 Fast variant"],"limitations":["Reranking adds latency to search pipelines — no documented latency SLA (typical: 50–500ms per rerank call)","Requires pre-existing retrieval system (vector search, BM25, hybrid) — cannot replace initial indexing","Personalization mechanism unknown — unclear how user interaction history is ingested and weighted","Model Vault pricing ($3,250–$6,500/month) makes dedicated instances expensive for low-volume use cases"],"requires":["Initial retrieval system (vector database, search engine, or hybrid retrieval)","Production API key or Model Vault subscription","User interaction data or query context for personalization (optional but recommended)"],"input_types":["query (search query string)","documents (list of candidate results to rerank)","user context (optional: user ID, history, preferences)"],"output_types":["ranked document list (reordered by relevance score)","relevance scores (numeric confidence per document)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cohere-api__cap_3","uri":"capability://text.generation.language.speech.to.text.transcription.with.conversational.robustness","name":"speech-to-text transcription with conversational robustness","description":"Transcribe endpoint converts audio input to text across 14 languages using an ASR (automatic speech recognition) model optimized for real-world conversational environments (background noise, accents, informal speech). Integrates downstream with generative and retrieval systems to enable end-to-end speech-driven workflows (e.g., voice search, voice-to-chat). Handles streaming audio input for real-time transcription use cases.","intents":["Build voice-enabled search interfaces that transcribe user speech and retrieve relevant documents","Create voice chatbots that transcribe user input and generate spoken responses","Implement meeting transcription and summarization workflows","Enable accessibility features for voice-based interaction in applications"],"best_for":["Teams building voice-first interfaces (smart speakers, voice assistants, accessibility tools)","Organizations processing customer support calls or meeting recordings","Applications requiring real-time speech-to-text with downstream NLU/generation"],"limitations":["Language support limited to 14 languages (vs 100+ for embeddings) — significant constraint for global deployments","Audio format specifications unknown — unclear which codecs, sample rates, and file sizes are supported","Streaming vs batch processing capabilities unknown — no documentation on real-time transcription latency","Confidence scores or alternative hypotheses unknown — unclear if model provides uncertainty estimates for downstream error handling"],"requires":["Production API key","Audio input in supported format (format list unknown)","For streaming: WebSocket or streaming HTTP support (not documented)"],"input_types":["audio (WAV, MP3, or other formats — list unknown)","streaming audio (for real-time transcription)"],"output_types":["text (transcribed speech)","timestamps (word-level timing — if supported)","confidence scores (per-word or per-segment — if supported)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cohere-api__cap_4","uri":"capability://memory.knowledge.rag.integration.with.pre.built.data.connectors","name":"rag integration with pre-built data connectors","description":"Compass product provides pre-built connectors to enterprise data sources (Salesforce, Slack, Jira, Google Drive, etc.) that automatically index documents and enable retrieval-augmented generation without manual ETL. Connectors handle authentication, incremental syncing, and document chunking, feeding retrieved context directly into Command R+ for grounded text generation. Managed index handles vector storage and similarity search internally.","intents":["Quickly build RAG systems over enterprise data without writing custom connectors or managing vector databases","Enable employees to query company knowledge (Slack history, Jira tickets, Google Drive docs) via natural language","Implement fact-checked customer support that cites internal documentation","Reduce time-to-market for knowledge-grounded AI applications"],"best_for":["Enterprise teams with existing SaaS tool stacks (Salesforce, Slack, Jira) seeking quick RAG deployment","Organizations without dedicated ML infrastructure or vector database expertise","Teams prioritizing speed-to-market over custom optimization"],"limitations":["Connector list unknown — unclear which data sources are supported beyond marketing claims","Connector customization unknown — no documentation on extending connectors to unsupported sources","Managed index details unknown — no visibility into chunking strategy, embedding model used, or index refresh frequency","Data residency for managed index unknown — unclear if indexed data stays in Cohere-managed cloud or supports VPC/on-premises","Pricing unknown — Compass requires 'getting in touch' for custom enterprise pricing, blocking self-serve cost estimation"],"requires":["Cohere account with Compass product access (custom pricing)","Authentication credentials for connected data sources (OAuth, API keys, etc.)","Data source must be in supported connector list (unknown)"],"input_types":["data source credentials (OAuth, API keys)","natural language queries"],"output_types":["retrieved documents (with source metadata)","generated text (grounded in retrieved context)"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cohere-api__cap_5","uri":"capability://code.generation.editing.model.fine.tuning.for.domain.specific.adaptation","name":"model fine-tuning for domain-specific adaptation","description":"Fine-tuning capability allows customization of Command R+ or embedding models on enterprise-specific data to improve performance on domain-specific tasks (legal document analysis, medical coding, technical support). Training process uses supervised learning on labeled examples, updating model weights to specialize behavior. Supports both generative and embedding model fine-tuning with custom pricing based on data volume and training duration.","intents":["Adapt Command R+ to domain-specific terminology and reasoning patterns (legal, medical, financial)","Improve embedding quality for specialized document types with custom similarity metrics","Reduce hallucinations in domain-specific generation by training on curated examples","Achieve better performance than prompt engineering alone for consistent, repeatable tasks"],"best_for":["Enterprise teams with large labeled datasets (1000+ examples) in specialized domains","Organizations with regulatory or compliance requirements for model transparency","Teams with dedicated ML expertise to manage fine-tuning workflows"],"limitations":["Fine-tuning technical details completely unknown — no documentation on minimum dataset size, training time, convergence criteria, or cost structure","No versioning or rollback mechanism documented — unclear how to manage multiple fine-tuned model versions","Training data requirements unknown — no guidance on data format, labeling standards, or quality thresholds","Inference cost for fine-tuned models unknown — unclear if pricing differs from base model","Custom pricing model blocks self-serve adoption — requires sales engagement for cost estimation"],"requires":["Labeled training dataset (size unknown, likely 1000+ examples)","Production API key with fine-tuning permissions","Custom enterprise agreement with Cohere (pricing negotiation required)"],"input_types":["labeled training examples (text + labels for classification, or text pairs for ranking)","validation dataset (optional, for hyperparameter tuning)"],"output_types":["fine-tuned model (deployed as custom model endpoint)","training metrics (loss, accuracy — if provided)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cohere-api__cap_6","uri":"capability://automation.workflow.dedicated.model.deployment.with.vpc.and.on.premises.options","name":"dedicated model deployment with vpc and on-premises options","description":"Model Vault provides dedicated, fully-managed deployment of Cohere models (Command R+, Embed 4, Rerank variants) in customer-controlled environments (VPC, on-premises, or Cohere-managed private cloud). Eliminates data sharing with Cohere infrastructure, enabling compliance with data residency regulations (GDPR, HIPAA, SOC 2). Pricing is hourly or monthly commitment-based rather than per-token, with fixed costs regardless of usage volume.","intents":["Deploy AI models in regulated industries (healthcare, finance, government) with strict data residency requirements","Achieve predictable costs for high-volume inference by committing to monthly/annual instances","Maintain data sovereignty by running models in private infrastructure","Reduce latency for geographically distributed users by deploying in regional VPCs"],"best_for":["Enterprise teams in regulated industries (healthcare, finance, government) with data residency mandates","Organizations with high inference volume (>1M tokens/month) where per-token pricing becomes expensive","Teams with existing VPC or on-premises infrastructure seeking to integrate AI without cloud data transfer"],"limitations":["High minimum commitment cost ($2,500–$6,500/month) creates barrier for small teams or experimental projects","Deployment complexity unknown — no documentation on setup time, infrastructure requirements, or operational overhead","Auto-scaling behavior unknown — unclear if instances auto-scale with demand or require manual provisioning","Model update frequency unknown — unclear how often new model versions are deployed to instances","Support SLA unknown — no documentation on uptime guarantees or incident response times"],"requires":["VPC or on-premises infrastructure (AWS, GCP, Azure, or private datacenter)","Enterprise agreement with Cohere","Minimum monthly commitment ($2,500 for Embed 4 Small, $6,500 for Rerank 4 Pro Large)"],"input_types":["API requests (same format as cloud API)"],"output_types":["API responses (same format as cloud API)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cohere-api__cap_7","uri":"capability://safety.moderation.api.key.based.authentication.with.trial.and.production.tiers","name":"api key-based authentication with trial and production tiers","description":"Two-tier authentication system provides trial API keys (auto-generated on account creation, rate-limited, free) for experimentation and production keys (requires application approval, pay-as-you-go billing) for commercial use. Trial keys are explicitly prohibited for production/commercial workloads. Authentication uses standard API key headers (implementation details unknown) with rate limiting enforced per key tier.","intents":["Quickly experiment with Cohere API without payment or approval process","Transition from trial to production with explicit approval gate for compliance and billing setup","Manage API access across teams with per-key rate limiting and usage tracking"],"best_for":["Developers prototyping AI features before production deployment","Enterprise teams with procurement/compliance requirements for API access approval"],"limitations":["Trial rate limits unknown — no documentation on requests-per-minute, tokens-per-day, or other quota metrics","Production key approval process unknown — no SLA for approval turnaround or approval criteria","Rate limit enforcement unknown — unclear if limits are hard blocks or soft throttling","Key rotation/revocation mechanism unknown — no documentation on key lifecycle management","Per-key usage tracking unknown — unclear if usage metrics are available per API key for cost allocation"],"requires":["Cohere account (free signup)","For production: application approval (criteria and timeline unknown)"],"input_types":["API key (in HTTP header)"],"output_types":["authentication success/failure response"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cohere-api__cap_8","uri":"capability://tool.use.integration.multi.model.api.with.unified.request.response.interface","name":"multi-model api with unified request/response interface","description":"Single API surface exposes multiple specialized models (Command R+ for generation, Embed 4 for embeddings, Rerank variants for ranking, Transcribe for speech) with consistent request/response patterns across endpoints. Enables building complex AI workflows (e.g., transcribe → generate → rerank) by chaining API calls without context switching between different provider APIs. Model selection is explicit via endpoint or model parameter.","intents":["Build end-to-end AI workflows (speech → generation → ranking) using a single API provider","Reduce integration complexity by avoiding multiple API clients for different AI tasks","Simplify cost tracking and billing by consolidating multiple AI capabilities under one account"],"best_for":["Teams building complex AI applications requiring multiple AI capabilities (speech, generation, search)","Organizations seeking to minimize vendor fragmentation and integration complexity"],"limitations":["Request/response schema unknown — no documentation on payload structure, field names, or error codes","Model parameter naming unknown — unclear how to specify model versions (e.g., 'Command R+' vs 'command-r-plus')","Batch API unknown — no documentation on bulk request support for cost optimization","Async/webhook support unknown — unclear if long-running operations support callbacks or polling"],"requires":["Single production API key","SDK or HTTP client library (SDK availability unknown)"],"input_types":["JSON request payloads (schema unknown)"],"output_types":["JSON response payloads (schema unknown)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cohere-api__cap_9","uri":"capability://automation.workflow.pay.as.you.go.token.based.billing.for.api.usage","name":"pay-as-you-go token-based billing for api usage","description":"Production API usage is billed on a pay-as-you-go model based on token consumption (per-token pricing structure unknown). Billing is metered per API call with costs aggregated across all endpoints (generation, embeddings, ranking, transcription). No upfront commitment required, enabling cost-proportional scaling. Trial tier is free but rate-limited and non-commercial.","intents":["Start using Cohere API without upfront payment or long-term commitment","Scale API usage elastically with costs proportional to actual consumption","Experiment with different models and endpoints without fixed infrastructure costs"],"best_for":["Startups and small teams with variable or unpredictable API usage","Teams prototyping multiple AI features and seeking cost flexibility"],"limitations":["Per-token pricing unknown — no public pricing page for standard API usage (only Model Vault instance pricing documented)","Cost estimation impossible — developers cannot calculate expected costs without contacting sales","No volume discounts documented — unclear if high-volume users receive pricing breaks","Billing granularity unknown — unclear if billing is per-token, per-request, or per-minute","Cost controls unknown — no documentation on spending limits, alerts, or budget caps"],"requires":["Production API key (requires approval)","Valid payment method (credit card, invoice billing — details unknown)"],"input_types":["API requests (metered by token count)"],"output_types":["monthly billing statement (format unknown)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cohere-api__headline","uri":"capability://llm.apis.enterprise.ai.api.for.text.generation.and.search.optimization","name":"enterprise ai api for text generation and search optimization","description":"Cohere API is an enterprise-focused AI API that provides powerful tools for text generation, embeddings, and search relevance optimization, designed for multilingual support and advanced retrieval tasks.","intents":["best AI API for text generation","AI API for search relevance","enterprise API for embeddings","multilingual AI API for enterprises","AI API with RAG capabilities"],"best_for":["enterprise applications","search optimization","multilingual text 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profile unknown — no SLA or response time benchmarks provided","Language support limited to 23 languages (vs 100+ for embeddings), creating potential bottlenecks in truly global deployments","Fine-tuning capabilities exist but technical details (training data requirements, cost, turnaround time) are undocumented","Embedding dimension size unknown — affects vector database storage and query latency","Maximum input length per embedding unknown — may require chunking strategies for long documents","Batch processing capabilities unknown — unclear if bulk embedding requests are optimized or require sequential calls","Model Vault pricing ($2,500–$3,250/month for Small/Medium tiers) creates high barrier for cost-sensitive projects","Agent capabilities unknown — no documentation on which tasks are pre-built vs custom-buildable","Workflow customization limits unknown — unclear how much agents can be tailored to non-standard processes","builder identity is not verified yet","no observed match 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