{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-meta-llama-llama-3-70b-instruct","slug":"meta-llama-llama-3-70b-instruct","name":"Meta: Llama 3 70B Instruct","type":"model","url":"https://openrouter.ai/models/meta-llama~llama-3-70b-instruct","page_url":"https://unfragile.ai/meta-llama-llama-3-70b-instruct","categories":["chatbots-assistants","testing-quality"],"tags":["meta-llama","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$5.10e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-meta-llama-llama-3-70b-instruct__cap_0","uri":"capability://text.generation.language.instruction.following.dialogue.generation.with.multi.turn.context","name":"instruction-following dialogue generation with multi-turn context","description":"Generates coherent, contextually-aware responses in multi-turn conversations using instruction-tuned transformer architecture optimized for dialogue. The model maintains conversation history through standard transformer context windows (8K tokens) and applies instruction-following fine-tuning to prioritize user intent over raw next-token prediction, enabling it to follow explicit directives, refuse harmful requests, and maintain consistent persona across exchanges.","intents":["Build a customer support chatbot that understands complex multi-step requests","Create an AI assistant that can follow detailed instructions and adapt tone based on user preference","Implement a conversational interface that maintains context across 10+ message exchanges","Deploy a dialogue system that refuses harmful requests while remaining helpful for legitimate ones"],"best_for":["Teams building production chatbots and conversational AI products","Developers creating customer-facing dialogue systems requiring nuanced instruction-following","Organizations needing high-quality multi-turn conversations without fine-tuning overhead"],"limitations":["Context window limited to ~8K tokens; long conversations require external memory/summarization","No real-time streaming output support via standard API (requires polling or WebSocket wrapper)","Instruction-following quality degrades with adversarial prompts or jailbreak attempts; not immune to prompt injection","No built-in conversation state persistence; requires external database for session management","Latency typically 2-5 seconds per response depending on output length and API load"],"requires":["API key for OpenRouter or direct Meta API access","HTTP client library (curl, Python requests, Node.js fetch, etc.)","Network connectivity to OpenRouter or Meta inference endpoint","Understanding of prompt engineering for optimal instruction-following behavior"],"input_types":["text (natural language instructions, questions, conversation history)"],"output_types":["text (natural language response, typically 50-2000 tokens per response)"],"categories":["text-generation-language","conversational-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3-70b-instruct__cap_1","uri":"capability://text.generation.language.code.aware.reasoning.and.explanation.generation","name":"code-aware reasoning and explanation generation","description":"Analyzes and explains code snippets, generates code walkthroughs, and reasons about algorithmic correctness by leveraging instruction-tuning that emphasizes logical decomposition and step-by-step explanation. The model can parse code syntax, identify patterns, and generate detailed explanations of what code does and why, though it does not perform actual code execution or static analysis.","intents":["Generate detailed code review comments explaining potential bugs or improvements","Create educational explanations of how existing code works for documentation","Reason about code correctness and suggest refactoring approaches","Explain error messages and suggest debugging strategies"],"best_for":["Technical documentation teams needing AI-assisted code explanation generation","Educational platforms creating interactive code tutorials","Development teams using AI for code review assistance and knowledge transfer"],"limitations":["Cannot execute code or verify correctness through runtime; explanations may contain logical errors","Limited to code visible in context window (~8K tokens); cannot analyze large codebases holistically","No language-specific semantic understanding beyond pattern matching; may miss subtle type system issues","Reasoning quality varies significantly with code clarity; obfuscated or unusual patterns may confuse the model","No integration with linters, type checkers, or static analysis tools for validation"],"requires":["API key for OpenRouter or Meta","Code snippets formatted as plain text or within conversation context","Understanding that explanations should be verified by human developers"],"input_types":["text (code snippets in any language, natural language questions about code)"],"output_types":["text (code explanations, reasoning about correctness, refactoring suggestions)"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3-70b-instruct__cap_2","uri":"capability://data.processing.analysis.structured.data.extraction.from.unstructured.text","name":"structured data extraction from unstructured text","description":"Extracts structured information (entities, relationships, key-value pairs) from natural language text by leveraging instruction-tuning to follow explicit extraction schemas and output formats. The model can parse instructions like 'extract all email addresses and associated names' or 'convert this paragraph into JSON with fields X, Y, Z' and generate structured outputs, though without formal schema validation or type enforcement.","intents":["Extract customer information from support tickets and convert to structured database records","Parse unstructured survey responses into categorical data for analysis","Convert natural language requirements into structured task lists or JSON schemas","Extract entities and relationships from documents for knowledge graph construction"],"best_for":["Data teams processing semi-structured text data at scale","Organizations migrating from manual data entry to AI-assisted extraction","Teams building data pipelines that need flexible extraction without custom regex/parsing"],"limitations":["No schema validation; output may not conform to specified structure without explicit instruction reinforcement","Accuracy degrades with ambiguous or poorly-formatted input text","Cannot handle binary or image data; text-only extraction","No built-in deduplication or consistency checking across multiple extractions","Hallucination risk: model may invent plausible-sounding data if information is missing from source text"],"requires":["API key for OpenRouter or Meta","Clear extraction instructions or schema specification in prompt","Post-processing validation logic to verify extracted data quality","Understanding that outputs require human review for critical applications"],"input_types":["text (unstructured documents, survey responses, support tickets, emails)"],"output_types":["text (JSON, CSV, structured key-value pairs, or other specified formats)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3-70b-instruct__cap_3","uri":"capability://text.generation.language.creative.and.technical.writing.generation.with.style.adaptation","name":"creative and technical writing generation with style adaptation","description":"Generates original written content (articles, emails, documentation, creative fiction) while adapting to specified tone, style, and audience through instruction-tuning that emphasizes stylistic control and user intent alignment. The model can generate content ranging from formal technical documentation to casual marketing copy by following explicit style instructions and examples, maintaining coherence across multi-paragraph outputs.","intents":["Generate product documentation and technical guides from specifications","Create marketing copy and email campaigns with specific tone and messaging","Write creative fiction, stories, or narrative content with consistent voice","Generate multiple style variations of the same content for A/B testing"],"best_for":["Content teams and marketing departments needing AI-assisted writing at scale","Technical writers generating documentation from specifications","Creative professionals using AI as a brainstorming and drafting tool","Small teams without dedicated copywriting resources"],"limitations":["Generated content requires editorial review; may contain factual inaccuracies or outdated information","Style adaptation quality depends on clarity of instructions; vague style requests produce inconsistent output","No built-in fact-checking or source verification; hallucination risk for claims requiring accuracy","Tone consistency may drift across long documents (>2000 tokens); requires section-by-section generation","No access to real-time information; knowledge cutoff limits currency of generated content"],"requires":["API key for OpenRouter or Meta","Clear style guidelines or examples in prompt","Editorial process for review and fact-checking","Understanding of prompt engineering for optimal style control"],"input_types":["text (specifications, outlines, style examples, tone descriptions)"],"output_types":["text (articles, emails, documentation, creative writing, typically 500-5000 tokens)"],"categories":["text-generation-language","content-creation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3-70b-instruct__cap_4","uri":"capability://text.generation.language.question.answering.and.knowledge.synthesis.from.context","name":"question-answering and knowledge synthesis from context","description":"Answers questions and synthesizes information from provided context (documents, code, specifications) by reading and reasoning over the supplied text without external knowledge retrieval. The model processes context windows up to ~8K tokens and generates answers grounded in that context, useful for Q&A over documents, FAQs, and knowledge base queries without requiring vector databases or RAG systems.","intents":["Build a document Q&A system that answers questions about uploaded PDFs or text files","Create FAQ systems that answer questions based on provided knowledge base text","Implement code documentation lookup that answers questions about codebase behavior","Generate summaries and answer questions about meeting transcripts or research papers"],"best_for":["Teams building document Q&A systems without RAG infrastructure","Organizations with smaller knowledge bases (<8K tokens) that fit in context window","Developers prototyping Q&A systems before investing in vector databases","Use cases where context freshness is critical (no stale embeddings)"],"limitations":["Context window limited to ~8K tokens; cannot answer questions requiring synthesis across large documents","No built-in retrieval; requires manual context selection or external retrieval system for large knowledge bases","Answers may be inaccurate if context is ambiguous or contradictory; no source attribution by default","No persistent memory of previous questions; each query is independent","Latency increases with context size; longer documents slow response time"],"requires":["API key for OpenRouter or Meta","Relevant context text provided in prompt (documents, code, specifications)","Understanding that answers are only as accurate as provided context"],"input_types":["text (context documents, code, specifications, natural language questions)"],"output_types":["text (answers, summaries, explanations grounded in provided context)"],"categories":["text-generation-language","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3-70b-instruct__cap_5","uri":"capability://planning.reasoning.logical.reasoning.and.problem.solving.with.step.by.step.decomposition","name":"logical reasoning and problem-solving with step-by-step decomposition","description":"Solves complex problems by breaking them into steps, reasoning through each component, and synthesizing solutions. The instruction-tuning emphasizes chain-of-thought reasoning patterns, enabling the model to articulate intermediate steps, identify assumptions, and correct errors mid-reasoning. Useful for math problems, logic puzzles, debugging, and decision-making scenarios where explicit reasoning is valuable.","intents":["Solve math and logic problems with detailed step-by-step working","Debug complex issues by reasoning through potential causes systematically","Generate decision trees and reasoning frameworks for business problems","Verify correctness of solutions by walking through logic explicitly"],"best_for":["Educational platforms teaching problem-solving and critical thinking","Technical teams debugging complex system issues","Decision-making systems requiring explainable reasoning","Organizations building AI-assisted analysis tools"],"limitations":["Reasoning quality varies with problem complexity; very hard problems may exceed model capacity","No access to external tools (calculators, code execution) for verification; arithmetic errors possible","Step-by-step reasoning adds latency (2-3x slower than direct answers)","Reasoning may contain logical fallacies or circular arguments; human verification recommended","No built-in backtracking; if reasoning goes wrong early, subsequent steps may be invalid"],"requires":["API key for OpenRouter or Meta","Clear problem statement and context","Prompt engineering to encourage step-by-step reasoning (e.g., 'think step by step')"],"input_types":["text (problem statements, questions, scenarios requiring reasoning)"],"output_types":["text (step-by-step reasoning, solutions, decision frameworks)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3-70b-instruct__cap_6","uri":"capability://text.generation.language.summarization.and.information.condensation.with.configurable.detail.levels","name":"summarization and information condensation with configurable detail levels","description":"Condenses long documents, articles, or conversations into summaries of varying lengths and detail levels by following explicit summarization instructions. The model can generate executive summaries, bullet-point summaries, or detailed abstracts while preserving key information and maintaining factual accuracy relative to source material. Supports both extractive (selecting key sentences) and abstractive (rephrasing) summarization patterns.","intents":["Generate executive summaries of long reports or research papers","Create bullet-point summaries of meeting transcripts or conversations","Condense customer feedback into key themes and insights","Generate abstracts for articles or documentation"],"best_for":["Organizations processing large volumes of documents and needing quick overviews","Knowledge workers managing information overload","Teams generating meeting notes and action items automatically","Content platforms creating preview summaries for articles"],"limitations":["Summarization quality depends on source material clarity; poorly-written input produces poor summaries","May omit important details if instructions are too aggressive on compression ratio","No built-in fact-checking; summaries may contain inaccuracies if source material is inaccurate","Abstractive summarization may introduce subtle meaning shifts or misinterpretations","Context window limits summarization to documents <8K tokens; larger documents require chunking"],"requires":["API key for OpenRouter or Meta","Source document or text to summarize","Clear instructions on desired summary length and detail level"],"input_types":["text (documents, articles, transcripts, reports)"],"output_types":["text (summaries in specified format: bullet points, paragraphs, structured outlines)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3-70b-instruct__cap_7","uri":"capability://text.generation.language.translation.and.cross.language.content.adaptation","name":"translation and cross-language content adaptation","description":"Translates text between languages and adapts content for different linguistic and cultural contexts. The model supports translation from English to many languages and vice versa, with instruction-tuning enabling control over formality level, terminology, and cultural adaptation. Translations maintain semantic meaning while adapting for target language idioms and conventions.","intents":["Translate product documentation and marketing materials into multiple languages","Localize customer support responses for different language audiences","Translate code comments and documentation for international teams","Generate culturally-adapted versions of content for different regions"],"best_for":["Global companies needing cost-effective translation for documentation and support","International teams collaborating across language barriers","Platforms serving multilingual user bases","Organizations localizing products for new markets"],"limitations":["Translation quality varies by language pair; less common language pairs may be less accurate","No domain-specific terminology databases; may mistranslate specialized terms","Cultural adaptation quality depends on model's training data representation of target culture","Idioms and wordplay often don't translate well; may lose nuance","No built-in glossary or terminology consistency across multiple translations"],"requires":["API key for OpenRouter or Meta","Source text in supported language","Target language specification","Optional: terminology glossary or style guide for consistency"],"input_types":["text (content in any supported language)"],"output_types":["text (translated content in target language)"],"categories":["text-generation-language","content-creation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3-70b-instruct__cap_8","uri":"capability://data.processing.analysis.sentiment.analysis.and.emotional.tone.detection","name":"sentiment analysis and emotional tone detection","description":"Analyzes text to identify sentiment (positive, negative, neutral), emotional tone, and underlying attitudes or opinions. The model can classify sentiment at document or sentence level, identify nuanced emotions beyond binary sentiment, and explain the reasoning behind sentiment judgments by pointing to specific phrases or context clues.","intents":["Analyze customer reviews and feedback to identify satisfaction trends","Monitor social media sentiment about products or brands","Classify support tickets by urgency and emotional tone","Analyze survey responses to understand customer satisfaction drivers"],"best_for":["Customer experience teams monitoring satisfaction and identifying issues","Marketing teams analyzing brand perception and campaign reception","Support teams prioritizing tickets by emotional urgency","Research teams analyzing qualitative feedback at scale"],"limitations":["Sentiment detection may be inaccurate for sarcasm, irony, or culturally-specific expressions","No built-in context awareness across multiple messages; treats each input independently","Accuracy varies by domain; sentiment in technical discussions may be misclassified","No quantitative confidence scores; outputs are categorical without probability estimates","Biases in training data may affect sentiment classification for certain demographics or topics"],"requires":["API key for OpenRouter or Meta","Text to analyze (reviews, comments, feedback, social media posts)"],"input_types":["text (customer reviews, feedback, social media posts, survey responses)"],"output_types":["text (sentiment classification, emotional tone analysis, reasoning/explanation)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3-70b-instruct__cap_9","uri":"capability://safety.moderation.content.moderation.and.safety.aware.response.filtering","name":"content moderation and safety-aware response filtering","description":"Evaluates text for harmful, inappropriate, or policy-violating content and generates responses that refuse harmful requests while remaining helpful for legitimate ones. The instruction-tuning includes safety training that enables the model to identify harmful intent, explain why requests are problematic, and suggest alternative approaches when possible.","intents":["Build chatbots that refuse harmful requests while remaining helpful","Moderate user-generated content for policy violations","Identify and flag potentially harmful requests in support tickets","Generate safe, policy-compliant responses to edge-case queries"],"best_for":["Teams building public-facing chatbots and conversational products","Platforms with user-generated content requiring moderation","Organizations with strict content policies and compliance requirements","Support teams handling sensitive or potentially harmful requests"],"limitations":["Safety training is not foolproof; adversarial prompts and jailbreak attempts may succeed","Refusal behavior may be overly conservative, refusing legitimate requests","No real-time policy updates; safety guidelines are fixed at model training time","Safety decisions are not explainable in detail; model may refuse without clear reasoning","Different safety thresholds across use cases require prompt engineering or fine-tuning"],"requires":["API key for OpenRouter or Meta","Understanding that safety is probabilistic, not absolute","Monitoring and logging of refusals for policy refinement","Additional moderation layers for high-stakes applications"],"input_types":["text (user queries, requests, content to moderate)"],"output_types":["text (safe responses, refusals with explanations, alternative suggestions)"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["API key for OpenRouter or direct Meta API access","HTTP client library (curl, Python requests, Node.js fetch, etc.)","Network connectivity to OpenRouter or Meta inference endpoint","Understanding of prompt engineering for optimal instruction-following behavior","API key for OpenRouter or Meta","Code snippets formatted as plain text or within conversation context","Understanding that explanations should be verified by human developers","Clear extraction instructions or schema specification in prompt","Post-processing validation logic to verify extracted data quality","Understanding that outputs require human review for critical applications"],"failure_modes":["Context window limited to ~8K tokens; long conversations require external memory/summarization","No real-time streaming output support via standard API (requires polling or WebSocket wrapper)","Instruction-following quality degrades with adversarial prompts or jailbreak attempts; not immune to prompt injection","No built-in conversation state persistence; requires external database for session management","Latency typically 2-5 seconds per response depending on output length and API load","Cannot execute code or verify correctness through runtime; explanations may contain logical errors","Limited to code visible in context window (~8K tokens); cannot analyze large codebases holistically","No language-specific semantic understanding beyond pattern matching; may miss subtle type system issues","Reasoning quality varies significantly with code clarity; obfuscated or unusual patterns may confuse the model","No integration with linters, type checkers, or static analysis tools for validation","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.45,"ecosystem":0.34,"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":"2026-05-03T15:20:45.777Z","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=meta-llama-llama-3-70b-instruct","compare_url":"https://unfragile.ai/compare?artifact=meta-llama-llama-3-70b-instruct"}},"signature":"OkJv7f2q3JSa4VUqMat3LMNMnP1F3O/SDgRyj855+kwXI+6s3J3k7q4re9/m2I+/n7MvVFQd9kdq7sm7mY5nDA==","signedAt":"2026-06-21T06:43:26.288Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/meta-llama-llama-3-70b-instruct","artifact":"https://unfragile.ai/meta-llama-llama-3-70b-instruct","verify":"https://unfragile.ai/api/v1/verify?slug=meta-llama-llama-3-70b-instruct","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"}}