{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-deepseek-deepseek-r1-distill-llama-70b","slug":"deepseek-deepseek-r1-distill-llama-70b","name":"DeepSeek: R1 Distill Llama 70B","type":"model","url":"https://openrouter.ai/models/deepseek~deepseek-r1-distill-llama-70b","page_url":"https://unfragile.ai/deepseek-deepseek-r1-distill-llama-70b","categories":["llm-apis"],"tags":["deepseek","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$7.00e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-deepseek-deepseek-r1-distill-llama-70b__cap_0","uri":"capability://text.generation.language.knowledge.distilled.reasoning.enhanced.text.generation","name":"knowledge-distilled reasoning-enhanced text generation","description":"Generates coherent, contextually-aware text responses by leveraging knowledge distilled from DeepSeek R1's chain-of-thought reasoning into a 70B parameter Llama-3.3 base model. The distillation process transfers reasoning patterns and decision-making logic from the larger R1 model into a more efficient architecture, enabling structured problem-solving without explicit chain-of-thought token overhead. Accessed via OpenRouter's unified API endpoint with streaming and non-streaming modes.","intents":["Generate multi-turn conversational responses with reasoning transparency","Solve complex problems requiring step-by-step logical decomposition","Produce technical explanations with underlying reasoning visible","Create content that balances reasoning depth with inference latency"],"best_for":["Teams building reasoning-heavy chatbots without R1 latency/cost constraints","Developers prototyping multi-turn agents requiring transparent decision-making","Organizations needing 70B-class reasoning at mid-tier inference costs"],"limitations":["Distilled reasoning may lose some nuance compared to full R1 chain-of-thought outputs","No explicit access to intermediate reasoning steps — reasoning is implicit in weights","Context window and reasoning depth trade-offs inherited from Llama-3.3-70B base (likely 8K-128K tokens)","Distillation quality depends on R1 training data; edge cases in R1 may propagate"],"requires":["OpenRouter API key with billing enabled","HTTP/2 client library or REST SDK (curl, axios, httpx, etc.)","Minimum request payload: model identifier + messages array","Network connectivity to OpenRouter inference endpoints"],"input_types":["text (natural language prompts)","structured messages (system, user, assistant roles)","multi-turn conversation history"],"output_types":["text (streaming or complete)","structured JSON (via OpenRouter response format)","token usage metadata (prompt_tokens, completion_tokens)"],"categories":["text-generation-language","reasoning-models"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-r1-distill-llama-70b__cap_1","uri":"capability://text.generation.language.multi.turn.conversational.context.management","name":"multi-turn conversational context management","description":"Maintains and processes multi-turn conversation history with role-based message sequencing (system, user, assistant) through OpenRouter's message API. The model tracks conversation state across requests, applying attention mechanisms to earlier turns while maintaining coherence and consistency. Supports dynamic context window management where older messages can be pruned or summarized based on token budget constraints.","intents":["Build stateful chatbot applications that remember conversation history","Implement multi-turn dialogue systems with consistent character/persona","Create interactive debugging assistants that reference previous code exchanges","Develop conversational agents that adapt responses based on conversation arc"],"best_for":["Chatbot developers building consumer-facing conversational interfaces","Enterprise teams implementing internal AI assistants with conversation memory","Developers creating interactive coding tutors or pair-programming agents"],"limitations":["Context window is finite (likely 8K-128K tokens) — long conversations require external memory/summarization","No built-in conversation persistence — state must be stored externally (database, cache)","Token counting for multi-turn history requires manual calculation or OpenRouter token estimation","No automatic context pruning — developers must implement sliding-window or summarization strategies"],"requires":["OpenRouter API key","Message history stored as array of {role, content} objects","Client-side conversation state management (session storage, database, or cache)","Token counter library (e.g., js-tiktoken) for budget-aware context management"],"input_types":["message objects with role (system/user/assistant) and content (text)","conversation history arrays","optional system prompts for persona/instruction injection"],"output_types":["assistant message text","token usage per turn","conversation metadata (timestamps, turn count)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-r1-distill-llama-70b__cap_2","uri":"capability://text.generation.language.instruction.following.with.structured.output.formatting","name":"instruction-following with structured output formatting","description":"Executes complex, multi-part instructions with high fidelity through Llama-3.3-70B's instruction-tuning combined with R1's reasoning distillation. The model interprets detailed system prompts, follows formatting constraints (JSON, XML, markdown), and produces structured outputs that can be reliably parsed. Supports few-shot prompting patterns where examples guide output format without explicit schema validation.","intents":["Generate JSON/XML outputs for downstream processing without schema validation","Create formatted documents (markdown, HTML) following specific style guidelines","Extract structured data from unstructured text with format constraints","Implement prompt-based function calling where output format encodes function calls"],"best_for":["Developers building LLM-powered data extraction pipelines","Teams implementing prompt-based structured output without formal schema validation","Builders creating content generation workflows with format requirements"],"limitations":["No formal schema validation — output format compliance depends on prompt quality and model behavior","JSON/XML generation can hallucinate invalid syntax; requires post-processing validation","Complex nested structures may exceed model's ability to maintain format consistency","Few-shot examples increase token usage and context window pressure"],"requires":["OpenRouter API key","Well-crafted system prompt with format examples","JSON/XML parser for output validation (optional but recommended)","Error handling for malformed structured outputs"],"input_types":["natural language instructions with format specifications","few-shot examples demonstrating desired output structure","unstructured text to be formatted/extracted"],"output_types":["JSON objects/arrays","XML documents","markdown with specific heading/list structures","delimited text (CSV-like formats)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-r1-distill-llama-70b__cap_3","uri":"capability://code.generation.editing.code.generation.and.technical.explanation","name":"code generation and technical explanation","description":"Generates code snippets, complete functions, and technical explanations by applying Llama-3.3-70B's code-training combined with R1's reasoning distillation for logic clarity. The model produces syntactically-correct code across multiple languages (Python, JavaScript, SQL, etc.) and explains implementation decisions with reasoning transparency. Supports context-aware code generation where previous code exchanges inform subsequent suggestions.","intents":["Generate code solutions for specific programming problems with explanation","Produce boilerplate code for common patterns (API handlers, database queries)","Explain existing code with reasoning about design choices and trade-offs","Create multi-file code solutions with cross-file dependency awareness"],"best_for":["Developers using AI-assisted coding without IDE plugins (e.g., in web interfaces)","Teams building code generation features into internal tools","Educators creating interactive coding tutorials with AI explanations"],"limitations":["Generated code may contain logical errors or security vulnerabilities — requires human review","No syntax validation — invalid code syntax can be produced, especially in less-common languages","Limited awareness of project-specific patterns unless provided in context","Multi-file generation lacks cross-file consistency guarantees without explicit context"],"requires":["OpenRouter API key","Code linter/formatter for output validation (eslint, pylint, etc.)","Context about target language, framework, and project constraints","Optional: code snippets or architecture diagrams to guide generation"],"input_types":["natural language problem descriptions","code snippets to extend or refactor","technical specifications or requirements","existing codebase context (file structure, patterns)"],"output_types":["code snippets (single functions/classes)","complete files or modules","code with inline comments","technical explanations of code logic"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-r1-distill-llama-70b__cap_4","uri":"capability://text.generation.language.domain.specific.knowledge.synthesis.and.explanation","name":"domain-specific knowledge synthesis and explanation","description":"Synthesizes knowledge across domains (science, medicine, law, finance) by applying Llama-3.3-70B's broad training combined with R1's reasoning distillation for accuracy and logical coherence. The model produces detailed explanations that connect concepts, identify assumptions, and reason through implications. Supports multi-step explanations where each step builds on previous reasoning, creating transparent knowledge synthesis.","intents":["Explain complex scientific or technical concepts with step-by-step reasoning","Synthesize information across multiple domains to answer interdisciplinary questions","Identify logical fallacies or unsupported claims in domain-specific arguments","Create educational content that shows reasoning behind domain knowledge"],"best_for":["Educational platforms building AI tutoring systems","Knowledge workers (researchers, analysts) seeking reasoning-transparent explanations","Content creators producing educational or technical documentation"],"limitations":["Knowledge cutoff limits recency of domain-specific information (training data dependent)","No real-time access to current research, market data, or breaking news","Domain-specific accuracy varies — stronger in well-represented domains (CS, general science) than niche fields","Reasoning transparency doesn't guarantee factual accuracy — requires human verification"],"requires":["OpenRouter API key","Domain-specific context or constraints in system prompt","Optional: reference materials or citations to ground explanations","Human expertise for fact-checking domain-critical outputs"],"input_types":["domain-specific questions","concepts to explain or synthesize","requests for reasoning transparency","comparative analysis prompts"],"output_types":["detailed explanations with step-by-step reasoning","concept maps or logical structures","citations or references (when trained on them)","caveats and limitations of explanations"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-r1-distill-llama-70b__cap_5","uri":"capability://tool.use.integration.api.based.inference.with.streaming.and.token.level.control","name":"api-based inference with streaming and token-level control","description":"Provides inference through OpenRouter's REST API with support for streaming responses (Server-Sent Events), token-level control (max_tokens, temperature, top_p), and usage tracking. The model processes requests asynchronously, returning partial responses via streaming for real-time UI updates or progressive output handling. Token budgeting is managed client-side through explicit parameters and response metadata.","intents":["Build web applications with real-time streaming text output","Implement token-budgeted inference for cost control in production systems","Create progressive output handlers that process model responses incrementally","Monitor token usage per request for billing and quota management"],"best_for":["Web developers building streaming chat interfaces","Teams managing inference costs with strict token budgets","Builders creating real-time AI features (live transcription, progressive generation)"],"limitations":["Streaming adds ~50-200ms latency overhead compared to non-streaming due to SSE protocol","Token counting is approximate — actual token usage may vary by ±5% due to tokenizer differences","No built-in rate limiting — client must implement backoff/retry logic","Streaming responses cannot be interrupted mid-generation without connection termination"],"requires":["OpenRouter API key with billing enabled","HTTP client supporting Server-Sent Events (fetch API, axios, httpx, etc.)","JSON parsing for request/response bodies","Optional: token counter library for client-side budget estimation"],"input_types":["JSON request body with model, messages, and parameters","HTTP headers with Authorization and Content-Type"],"output_types":["streaming: Server-Sent Events with delta text chunks","non-streaming: complete JSON response with full text","usage metadata: {prompt_tokens, completion_tokens, total_tokens}"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-r1-distill-llama-70b__cap_6","uri":"capability://text.generation.language.temperature.and.sampling.based.output.diversity.control","name":"temperature and sampling-based output diversity control","description":"Controls output randomness and diversity through temperature (0.0-2.0), top_p (nucleus sampling), and top_k parameters passed to the inference engine. Lower temperatures (0.0-0.5) produce deterministic, focused outputs; higher temperatures (1.0+) increase creativity and diversity. The model applies these parameters at token-generation time, affecting probability distributions over the vocabulary without post-processing.","intents":["Generate deterministic outputs for factual tasks (code, data extraction)","Create diverse creative outputs (brainstorming, content variations)","Balance consistency and novelty for conversational applications","Implement temperature-based output quality tiers (fast/cheap vs. creative)"],"best_for":["Developers building applications requiring output diversity control","Teams implementing A/B testing with temperature-based variants","Builders creating multi-variant content generation (headlines, descriptions)"],"limitations":["Temperature effects are non-linear and model-dependent — same temperature produces different diversity across models","Very high temperatures (>1.5) often produce incoherent or nonsensical outputs","Temperature doesn't guarantee diversity — repeated calls with same temperature may produce identical outputs","No direct control over output length distribution — only token count limits"],"requires":["OpenRouter API key","Understanding of temperature semantics (0=deterministic, 1=baseline, >1=creative)","Optional: A/B testing framework to measure temperature effects on output quality"],"input_types":["temperature parameter (float, 0.0-2.0)","top_p parameter (float, 0.0-1.0)","top_k parameter (integer, 1-100)"],"output_types":["text with controlled randomness","multiple variants from same prompt with different temperatures"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"high","permissions":["OpenRouter API key with billing enabled","HTTP/2 client library or REST SDK (curl, axios, httpx, etc.)","Minimum request payload: model identifier + messages array","Network connectivity to OpenRouter inference endpoints","OpenRouter API key","Message history stored as array of {role, content} objects","Client-side conversation state management (session storage, database, or cache)","Token counter library (e.g., js-tiktoken) for budget-aware context management","Well-crafted system prompt with format examples","JSON/XML parser for output validation (optional but recommended)"],"failure_modes":["Distilled reasoning may lose some nuance compared to full R1 chain-of-thought outputs","No explicit access to intermediate reasoning steps — reasoning is implicit in weights","Context window and reasoning depth trade-offs inherited from Llama-3.3-70B base (likely 8K-128K tokens)","Distillation quality depends on R1 training data; edge cases in R1 may propagate","Context window is finite (likely 8K-128K tokens) — long conversations require external memory/summarization","No built-in conversation persistence — state must be stored externally (database, cache)","Token counting for multi-turn history requires manual calculation or OpenRouter token estimation","No automatic context pruning — developers must implement sliding-window or summarization strategies","No formal schema validation — output format compliance depends on prompt quality and model behavior","JSON/XML generation can hallucinate invalid syntax; requires post-processing validation","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.39,"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-r1-distill-llama-70b","compare_url":"https://unfragile.ai/compare?artifact=deepseek-deepseek-r1-distill-llama-70b"}},"signature":"mKaw417WhVaef4x4DhWrZzXDHw2ofN3BiEhjTp9NeTuf8Fn4Gt41r3Xj8bj7Q5XXODvmNrHC7PwTqoKSBfUTAw==","signedAt":"2026-06-22T20:57:23.574Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/deepseek-deepseek-r1-distill-llama-70b","artifact":"https://unfragile.ai/deepseek-deepseek-r1-distill-llama-70b","verify":"https://unfragile.ai/api/v1/verify?slug=deepseek-deepseek-r1-distill-llama-70b","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"}}