{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-deepseek-deepseek-chat","slug":"deepseek-deepseek-chat","name":"DeepSeek: DeepSeek V3","type":"model","url":"https://openrouter.ai/models/deepseek~deepseek-chat","page_url":"https://unfragile.ai/deepseek-deepseek-chat","categories":["chatbots-assistants"],"tags":["deepseek","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$3.20e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-deepseek-deepseek-chat__cap_0","uri":"capability://text.generation.language.instruction.following.conversational.chat.with.multi.turn.context","name":"instruction-following conversational chat with multi-turn context","description":"Processes natural language instructions and maintains coherent multi-turn conversations by tracking full conversation history within a context window. Uses transformer-based attention mechanisms trained on 15 trillion tokens to understand nuanced user intent, follow complex instructions, and generate contextually appropriate responses. Supports system prompts for role-based behavior customization and instruction refinement.","intents":["I need an AI assistant that understands complex, multi-step instructions and maintains context across 20+ conversation turns","I want to build a chatbot that can follow detailed system prompts and adapt its tone/behavior based on user-defined roles","I need to integrate a conversational AI that handles ambiguous queries and asks clarifying questions when needed"],"best_for":["developers building conversational AI products and chatbot applications","teams integrating general-purpose AI assistants into customer support or internal tools","researchers evaluating instruction-following capabilities across diverse domains"],"limitations":["context window size limits conversation history retention — older messages beyond window are lost","no persistent memory across sessions — each conversation starts fresh without prior interaction history","latency varies with input length and model load; typical response time 1-5 seconds depending on query complexity","instruction-following quality degrades on highly specialized domain tasks without fine-tuning"],"requires":["API key for OpenRouter or direct DeepSeek API access","HTTP client library (curl, Python requests, Node.js fetch, etc.)","network connectivity to DeepSeek inference endpoints","understanding of prompt engineering for optimal instruction clarity"],"input_types":["text (natural language instructions and queries)","structured prompts with system role definitions","conversation history in message array format"],"output_types":["text (natural language responses)","structured reasoning traces (when explicitly requested)","code snippets embedded in text responses"],"categories":["text-generation-language","conversational-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-chat__cap_1","uri":"capability://code.generation.editing.code.generation.and.completion.with.multi.language.support","name":"code generation and completion with multi-language support","description":"Generates syntactically correct, functional code across 40+ programming languages by leveraging transformer attention patterns trained on billions of code tokens. Supports code completion from partial snippets, full function generation from docstrings, and code explanation. Uses context-aware token prediction to maintain language-specific syntax rules, indentation, and idioms without explicit grammar constraints.","intents":["I need to generate boilerplate code or complete partial code snippets in Python, JavaScript, Go, Rust, and other languages","I want to convert pseudocode or natural language descriptions into working, executable code","I need an AI that can explain existing code and suggest refactorings or optimizations"],"best_for":["individual developers and small teams accelerating development velocity","teams building code generation features into IDEs or development tools","educators teaching programming who want to generate example code quickly"],"limitations":["generated code may contain logical errors or edge-case bugs — requires human review and testing","performance degrades on very long functions (>500 lines) due to context window constraints","no built-in awareness of project-specific libraries or internal APIs without explicit context injection","generated code follows training data patterns; may not reflect latest language versions or best practices from the past 6 months"],"requires":["API access to DeepSeek V3 via OpenRouter or direct endpoint","ability to send code context in API requests (typically 2-8KB per request)","programming language knowledge to validate and test generated code","optional: IDE integration layer for seamless in-editor code completion"],"input_types":["partial code snippets (incomplete functions, classes)","natural language descriptions of desired functionality","docstrings and type hints","code comments describing intent"],"output_types":["complete, executable code in target language","multiple code generation alternatives (when requested)","code with inline comments explaining logic"],"categories":["code-generation-editing","developer-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-chat__cap_2","uri":"capability://planning.reasoning.reasoning.chain.generation.with.step.by.step.problem.decomposition","name":"reasoning-chain generation with step-by-step problem decomposition","description":"Generates explicit reasoning chains that decompose complex problems into intermediate steps, enabling transparent problem-solving logic. Uses chain-of-thought prompting patterns to surface reasoning before final answers, allowing verification of logic at each step. Trained to recognize problem structure and apply appropriate reasoning strategies (mathematical derivation, logical deduction, case analysis) based on problem type.","intents":["I need an AI that shows its work when solving math problems, logic puzzles, or complex reasoning tasks","I want to build applications that require explainable AI decisions with visible reasoning traces","I need to verify AI reasoning quality by inspecting intermediate steps rather than trusting final answers"],"best_for":["educational platforms teaching problem-solving and critical thinking","enterprise applications requiring explainable AI for compliance or audit purposes","researchers studying AI reasoning capabilities and failure modes"],"limitations":["reasoning chains can be verbose and increase token consumption by 3-5x compared to direct answers","intermediate reasoning steps may contain logical errors that compound toward incorrect final answers","reasoning quality varies significantly by domain — strong on math/logic, weaker on subjective judgment calls","no guarantee of optimal reasoning path; may take inefficient routes to correct answers"],"requires":["API access to DeepSeek V3","prompt engineering to explicitly request step-by-step reasoning (e.g., 'Think step by step')","higher token budgets due to longer response lengths","parsing logic to extract and validate intermediate reasoning steps"],"input_types":["math problems with numerical or algebraic components","logic puzzles and constraint satisfaction problems","complex multi-step questions requiring decomposition","natural language problems with implicit structure"],"output_types":["step-by-step reasoning traces in natural language","intermediate calculations and logical deductions","final answer with supporting justification","structured reasoning in JSON or markdown format"],"categories":["planning-reasoning","problem-solving"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-chat__cap_3","uri":"capability://tool.use.integration.api.based.inference.with.streaming.response.support","name":"api-based inference with streaming response support","description":"Exposes model inference through REST API endpoints with support for streaming token-by-token responses, enabling real-time output consumption. Implements OpenAI-compatible API schema for drop-in compatibility with existing LLM application frameworks. Supports batch processing for non-real-time workloads and configurable sampling parameters (temperature, top-p, max-tokens) for controlling output diversity and length.","intents":["I need to integrate a powerful LLM into my application via standard REST API without managing infrastructure","I want streaming responses to display AI output in real-time to end users","I need to use DeepSeek with existing frameworks like LangChain, LlamaIndex, or Vercel AI SDK without custom adapters"],"best_for":["startups and teams building LLM applications without ML infrastructure expertise","developers integrating AI into web apps, mobile apps, or backend services","teams already using OpenAI API who want to switch providers with minimal code changes"],"limitations":["API latency depends on DeepSeek's server load and geographic distance — typically 1-5 seconds per request","streaming responses require persistent HTTP connections; not suitable for serverless functions with strict timeout limits","rate limiting applies based on API tier — high-volume applications may hit quota limits","no local inference option — all requests traverse the internet, introducing privacy considerations for sensitive data"],"requires":["API key from OpenRouter or DeepSeek direct API","HTTP client library (curl, Python requests, JavaScript fetch, etc.)","network connectivity and firewall rules allowing outbound HTTPS to DeepSeek endpoints","optional: LLM framework integration (LangChain, LlamaIndex, etc.) for simplified usage"],"input_types":["JSON request bodies with messages array (OpenAI-compatible format)","system prompts and user messages","sampling parameters (temperature, top_p, max_tokens, etc.)"],"output_types":["streaming JSON chunks with token-by-token output (Server-Sent Events format)","complete response objects with usage statistics","structured data when using function-calling mode"],"categories":["tool-use-integration","api-infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-chat__cap_4","uri":"capability://tool.use.integration.function.calling.with.schema.based.tool.invocation","name":"function calling with schema-based tool invocation","description":"Enables the model to invoke external tools and APIs by generating structured function calls based on JSON schema definitions. Model receives tool schemas, reasons about which tools to use, and generates properly-formatted function calls with arguments. Supports multi-turn tool use where model can call multiple functions sequentially and incorporate results into reasoning. Implements OpenAI-compatible function-calling protocol for framework compatibility.","intents":["I need an AI agent that can call APIs, databases, or custom functions to retrieve real-time information","I want to build a multi-step workflow where the AI decides which tools to use and in what order","I need to integrate DeepSeek with existing tool-use frameworks that expect OpenAI-compatible function calling"],"best_for":["developers building AI agents that interact with external systems and APIs","teams creating autonomous workflows that require tool orchestration","applications needing real-time data retrieval (weather, stock prices, database queries) integrated with LLM reasoning"],"limitations":["model may hallucinate function calls with incorrect argument types or missing required parameters — requires validation before execution","no built-in error handling or retry logic — application must implement tool execution and error recovery","function schema complexity affects model's ability to choose correct tools; overly complex schemas reduce accuracy","no guarantee of optimal tool selection order — model may call tools inefficiently or redundantly"],"requires":["API access to DeepSeek V3 with function-calling support enabled","JSON schema definitions for all available tools/functions","application-level tool execution layer to actually invoke functions and return results","error handling and validation logic for function arguments and return values"],"input_types":["JSON schema definitions describing available functions (name, description, parameters)","user queries requesting tool-based actions","tool execution results to feed back into model for multi-turn reasoning"],"output_types":["structured function calls with function name and arguments","reasoning about which tools to use and why","final response incorporating tool results"],"categories":["tool-use-integration","agent-orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-chat__cap_5","uri":"capability://memory.knowledge.long.context.understanding.with.extended.token.windows","name":"long-context understanding with extended token windows","description":"Processes extended input sequences up to the model's context window limit (typically 4K-8K tokens, expandable to 32K+ with specific configurations), enabling analysis of long documents, code files, and conversation histories without truncation. Uses efficient attention mechanisms to maintain coherence across long sequences while managing computational costs. Supports retrieval-augmented generation patterns where long documents are passed directly rather than requiring external retrieval systems.","intents":["I need to analyze entire documents, code files, or long conversations without summarizing or chunking them first","I want to build RAG applications that pass full documents to the model instead of relying on vector similarity search","I need to maintain conversation context across 50+ turns without losing early conversation details"],"best_for":["document analysis and summarization applications","code review and analysis tools processing entire files","long-form content generation and editing workflows","conversational AI requiring deep historical context"],"limitations":["context window size is fixed — very long documents (>8K tokens) still require chunking or summarization","latency increases linearly with input length — processing 8K tokens takes 2-3x longer than 2K tokens","token costs scale with input length; long-context queries consume significantly more API credits","attention mechanism may dilute focus on relevant information when context is very long and sparse"],"requires":["API access to DeepSeek V3","understanding of token counting to estimate input size before API calls","application logic to chunk documents if they exceed context window","optional: vector database for hybrid retrieval when documents exceed context limits"],"input_types":["long text documents (articles, reports, books)","complete source code files or multiple files concatenated","extended conversation histories with 20+ turns","structured data with rich context (JSON, CSV, markdown)"],"output_types":["summaries and analyses of long documents","code reviews and refactoring suggestions","contextually-aware responses incorporating full conversation history","structured extraction from long documents"],"categories":["memory-knowledge","context-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-chat__cap_6","uri":"capability://text.generation.language.multilingual.understanding.and.generation.across.100.languages","name":"multilingual understanding and generation across 100+ languages","description":"Processes and generates text in 100+ languages including English, Chinese, Spanish, French, German, Japanese, Korean, Arabic, and many others. Uses multilingual transformer embeddings trained on diverse language corpora to maintain semantic understanding across language boundaries. Supports code-switching (mixing languages in single response) and language-aware formatting (RTL text, character encoding, punctuation conventions).","intents":["I need an AI assistant that can understand and respond in multiple languages without language-specific fine-tuning","I want to build global applications that serve users in 20+ countries with native language support","I need to translate content, analyze multilingual documents, or handle code-switching in user inputs"],"best_for":["global SaaS applications serving international user bases","translation and localization tools","multilingual customer support chatbots","research on cross-lingual NLP and language understanding"],"limitations":["performance varies by language — English and major languages (Chinese, Spanish) are stronger than low-resource languages","no explicit translation mode; translation quality is lower than specialized translation models (Google Translate, DeepL)","code-switching may produce inconsistent output if mixing many languages in single prompt","character encoding issues possible with rare scripts or emoji-heavy inputs"],"requires":["API access to DeepSeek V3","proper UTF-8 encoding for non-Latin scripts","language specification in prompts for optimal performance (e.g., 'Respond in Spanish')","optional: language detection library to identify input language automatically"],"input_types":["text in any of 100+ supported languages","code-switched inputs mixing multiple languages","language-specific formatting (RTL text, diacritics, special characters)"],"output_types":["responses in requested language","translations between language pairs","multilingual summaries and analyses","language-aware formatted output"],"categories":["text-generation-language","multilingual-support"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-chat__cap_7","uri":"capability://data.processing.analysis.structured.data.extraction.and.json.schema.compliance","name":"structured data extraction and json schema compliance","description":"Extracts structured data from unstructured text and generates output conforming to specified JSON schemas. Model receives schema definitions and natural language input, then generates valid JSON output matching the schema structure. Supports nested objects, arrays, optional fields, and type constraints. Enables reliable data extraction for downstream processing without manual parsing or validation.","intents":["I need to extract structured information (entities, relationships, attributes) from documents and convert to JSON","I want to generate API responses that conform to specific JSON schemas without manual validation","I need to parse natural language input into structured database records or form submissions"],"best_for":["data extraction and ETL pipelines","API response generation with schema validation","form filling and data entry automation","knowledge base construction from unstructured text"],"limitations":["model may generate invalid JSON or violate schema constraints — requires post-processing validation","extraction accuracy depends on schema clarity and input text quality; ambiguous schemas produce inconsistent results","complex nested schemas (>5 levels deep) may confuse the model and produce incomplete or malformed output","no built-in error recovery — invalid JSON requires re-prompting or manual correction"],"requires":["API access to DeepSeek V3","JSON schema definitions for desired output structure","JSON validation library to verify output conforms to schema","error handling and re-prompting logic for invalid outputs"],"input_types":["unstructured text documents","natural language descriptions of desired data","JSON schema definitions specifying output structure","examples of desired output format"],"output_types":["valid JSON conforming to specified schema","structured data with typed fields and nested objects","arrays of extracted entities or records"],"categories":["data-processing-analysis","structured-extraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-chat__cap_8","uri":"capability://planning.reasoning.knowledge.cutoff.awareness.and.temporal.reasoning","name":"knowledge cutoff awareness and temporal reasoning","description":"Model acknowledges its knowledge cutoff date and can reason about temporal information, historical events, and time-dependent facts. Trained to distinguish between information from training data (pre-cutoff) and information requiring real-time lookup. Supports relative date reasoning (e.g., 'what happened 3 months ago') and temporal logic for understanding sequences of events. Does not hallucinate future information or claim knowledge of events after training cutoff.","intents":["I need an AI that honestly states its knowledge limitations and doesn't hallucinate recent events","I want to build applications that combine historical knowledge with real-time data from APIs","I need to reason about temporal sequences and historical context without confusing past and present"],"best_for":["applications requiring factual accuracy about historical events","hybrid systems combining LLM knowledge with real-time data APIs","educational tools teaching history and temporal reasoning","research applications requiring clear knowledge boundaries"],"limitations":["knowledge cutoff means no awareness of events after training date — requires external data sources for current information","temporal reasoning is limited to logical inference; no access to calendar, timezone, or scheduling systems","model may still hallucinate details about pre-cutoff events if training data was sparse or contradictory","relative date reasoning (e.g., 'today') requires explicit date context in prompts"],"requires":["API access to DeepSeek V3","understanding of model's knowledge cutoff date (typically 6-12 months before release)","optional: integration with real-time data APIs for current information","explicit date context in prompts when temporal reasoning is important"],"input_types":["queries about historical events and facts","temporal reasoning questions with date references","requests for current information (which model will decline)"],"output_types":["historically accurate information with confidence levels","acknowledgment of knowledge cutoff when relevant","temporal reasoning about sequences of events","recommendations to use external sources for current information"],"categories":["planning-reasoning","knowledge-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-chat__cap_9","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 producing harmful, illegal, or unethical content through alignment training and safety filters. Model is trained to refuse requests for illegal activities, violence, hate speech, sexual content involving minors, and other harmful outputs. Implements graceful refusal patterns that explain why requests cannot be fulfilled rather than abruptly blocking users. Supports configurable safety levels for different use cases.","intents":["I need an AI assistant that won't generate harmful content and can explain safety decisions to users","I want to deploy AI in production with confidence that it won't produce illegal or unethical outputs","I need to build applications for regulated industries (healthcare, finance, education) with safety guarantees"],"best_for":["production applications serving general audiences","regulated industries requiring compliance with content policies","educational platforms and child-safe applications","enterprise deployments with strict content governance requirements"],"limitations":["safety filters may over-refuse legitimate requests (false positives) — requires user feedback to improve","determined users can sometimes bypass safety filters through prompt engineering or indirect requests","safety training may reduce model's ability to discuss sensitive topics for legitimate purposes (education, research)","safety decisions are probabilistic — no guarantee of 100% harmful content prevention"],"requires":["API access to DeepSeek V3 with safety features enabled","understanding of model's safety policies and refusal patterns","user feedback mechanisms to report safety failures or over-refusals","optional: content moderation layer for additional filtering if needed"],"input_types":["any user query or instruction","requests that may violate safety policies","sensitive topics requiring careful handling"],"output_types":["safe, non-harmful responses","graceful refusals explaining why requests cannot be fulfilled","alternative suggestions for legitimate use cases"],"categories":["safety-moderation","content-filtering"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["API key for OpenRouter or direct DeepSeek API access","HTTP client library (curl, Python requests, Node.js fetch, etc.)","network connectivity to DeepSeek inference endpoints","understanding of prompt engineering for optimal instruction clarity","API access to DeepSeek V3 via OpenRouter or direct endpoint","ability to send code context in API requests (typically 2-8KB per request)","programming language knowledge to validate and test generated code","optional: IDE integration layer for seamless in-editor code completion","API access to DeepSeek V3","prompt engineering to explicitly request step-by-step reasoning (e.g., 'Think step by step')"],"failure_modes":["context window size limits conversation history retention — older messages beyond window are lost","no persistent memory across sessions — each conversation starts fresh without prior interaction history","latency varies with input length and model load; typical response time 1-5 seconds depending on query complexity","instruction-following quality degrades on highly specialized domain tasks without fine-tuning","generated code may contain logical errors or edge-case bugs — requires human review and testing","performance degrades on very long functions (>500 lines) due to context window constraints","no built-in awareness of project-specific libraries or internal APIs without explicit context injection","generated code follows training data patterns; may not reflect latest language versions or best practices from the past 6 months","reasoning chains can be verbose and increase token consumption by 3-5x compared to direct answers","intermediate reasoning steps may contain logical errors that compound toward incorrect final answers","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=deepseek-deepseek-chat","compare_url":"https://unfragile.ai/compare?artifact=deepseek-deepseek-chat"}},"signature":"Q1RQ8a7eymvTM28iHhuoCzh+iZq8Fo6jYHLzy/W24q/qqTHALOkqbhHqaCeoGeTKMtD83+dI+MrUgqB5wgchAg==","signedAt":"2026-06-22T01:19:38.563Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/deepseek-deepseek-chat","artifact":"https://unfragile.ai/deepseek-deepseek-chat","verify":"https://unfragile.ai/api/v1/verify?slug=deepseek-deepseek-chat","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"}}