{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-meta-llama-llama-3.1-70b-instruct","slug":"meta-llama-llama-3.1-70b-instruct","name":"Meta: Llama 3.1 70B Instruct","type":"model","url":"https://openrouter.ai/models/meta-llama~llama-3.1-70b-instruct","page_url":"https://unfragile.ai/meta-llama-llama-3.1-70b-instruct","categories":["chatbots-assistants","testing-quality"],"tags":["meta-llama","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$4.00e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-meta-llama-llama-3.1-70b-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 to user prompts using transformer-based attention mechanisms trained on instruction-following data. The 70B parameter model maintains conversation state across multiple turns by processing the full dialogue history as input tokens, enabling it to track context, correct itself, and adapt tone based on accumulated interaction patterns. Uses causal self-attention with rotary positional embeddings (RoPE) to handle variable-length sequences up to 128K tokens.","intents":["Build a conversational AI assistant that understands nuanced user requests and maintains coherent dialogue over 10+ exchanges","Create a chatbot that can switch between technical explanation, casual tone, and formal writing based on conversation context","Implement a multi-turn reasoning system where the model references earlier statements to resolve ambiguities"],"best_for":["Teams building customer support chatbots requiring natural conversation flow","Developers creating interactive AI tutoring systems with pedagogical dialogue","Builders prototyping conversational agents where context retention is critical"],"limitations":["Context window of 128K tokens means very long conversations (>50K tokens) may hit memory constraints on consumer hardware","No built-in memory persistence across sessions — each conversation starts fresh without access to previous interactions","Instruction-tuning optimizes for following explicit directives; may struggle with implicit, unspoken user needs","Latency increases linearly with context length; 100K token context may add 2-5 seconds per response vs. 500ms for short prompts"],"requires":["API access via OpenRouter or direct Meta endpoint","Minimum 40GB VRAM for local deployment, or cloud API key with rate limits","Input formatted as conversation messages (system prompt + user/assistant turns)"],"input_types":["text (natural language prompts)","structured conversation history (JSON message arrays with role/content)","system prompts (optional, for behavior steering)"],"output_types":["text (natural language response)","streaming tokens (for real-time UI updates)"],"categories":["text-generation-language","conversational-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.1-70b-instruct__cap_1","uri":"capability://code.generation.editing.code.generation.and.explanation.from.natural.language.specifications","name":"code generation and explanation from natural language specifications","description":"Generates syntactically correct, executable code snippets in 15+ programming languages from natural language descriptions. Uses transformer attention to map semantic intent to language-specific syntax patterns learned during pre-training. The model can generate complete functions, debug existing code, explain implementation choices, and suggest optimizations by treating code as a special token sequence with learned patterns for indentation, imports, and language idioms.","intents":["Quickly scaffold boilerplate code (API endpoints, database queries, UI components) from English descriptions","Get explanations of how existing code works and why certain patterns were chosen","Generate test cases and edge-case handling code for a given function specification"],"best_for":["Solo developers and small teams accelerating prototyping velocity","Non-expert programmers translating domain knowledge into working code","Teams using code generation as a starting point for code review and refinement"],"limitations":["Generated code may contain logical errors or security vulnerabilities (e.g., SQL injection, unhandled exceptions) — always requires human review","Performance is not optimized; generated code often lacks algorithmic efficiency improvements","No awareness of existing codebase patterns or style guides unless explicitly provided in context","Struggles with very long functions (>500 lines) or complex architectural patterns requiring deep system knowledge"],"requires":["API access via OpenRouter or cloud provider","Clear, specific natural language descriptions (vague prompts yield lower-quality code)","Optional: code context or existing codebase snippets for style consistency"],"input_types":["text (natural language function/feature description)","code (existing code to refactor, debug, or extend)","structured specifications (JSON schemas, API contracts)"],"output_types":["code (Python, JavaScript, Java, Go, Rust, C++, etc.)","explanations (markdown with inline comments)","test cases (unit test code)"],"categories":["code-generation-editing","developer-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.1-70b-instruct__cap_10","uri":"capability://code.generation.editing.code.review.and.quality.assessment.with.explanations","name":"code review and quality assessment with explanations","description":"Analyzes code for bugs, security vulnerabilities, performance issues, and style violations, providing detailed explanations and improvement suggestions. Uses learned patterns from code review examples to identify common anti-patterns, suggest refactoring opportunities, and explain why certain patterns are problematic. Can assess code quality across multiple dimensions (correctness, security, performance, readability) and prioritize issues by severity.","intents":["Automate initial code review pass to catch obvious bugs and style issues before human review","Provide learning feedback to junior developers on code quality and best practices","Identify security vulnerabilities and performance bottlenecks in existing codebases"],"best_for":["Development teams using AI to augment human code review","Educational contexts where students need feedback on code quality","Security-conscious teams automating vulnerability scanning"],"limitations":["Code review quality depends on code context; isolated functions may receive poor feedback without understanding broader system design","May miss subtle logical errors or domain-specific issues requiring deep expertise","Cannot verify code correctness without execution; may suggest changes that break functionality","Security assessment is pattern-based; may miss novel vulnerability types or context-specific risks"],"requires":["API access via OpenRouter or cloud provider","Code input in supported languages (Python, JavaScript, Java, Go, Rust, C++, etc.)","Optional: context about code purpose, architecture, or constraints"],"input_types":["code (single functions, files, or code snippets)","structured code metadata (language, purpose, constraints)"],"output_types":["text (review comments with explanations)","structured issues (categorized by type and severity)","suggested improvements (refactored code or best practices)"],"categories":["code-generation-editing","quality-assurance"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.1-70b-instruct__cap_11","uri":"capability://search.retrieval.semantic.similarity.and.relevance.ranking","name":"semantic similarity and relevance ranking","description":"Evaluates semantic similarity between text passages and ranks items by relevance to a query. Uses transformer representations to compute semantic distance between texts, enabling ranking of documents, search results, or recommendations by relevance. Can be used for duplicate detection, semantic search, and recommendation systems without explicit vector database integration.","intents":["Rank search results by semantic relevance rather than keyword matching","Detect duplicate or near-duplicate documents in large corpora","Recommend similar items (articles, products, users) based on semantic similarity"],"best_for":["Search and discovery systems requiring semantic understanding","Recommendation engines for content, products, or users","Duplicate detection and deduplication workflows"],"limitations":["Semantic similarity is computed on-demand; ranking large result sets (>1000 items) is computationally expensive","No persistent embeddings; requires recomputation for each query unless cached","Similarity judgments are based on training data patterns; may not align with domain-specific relevance","Cannot handle multimodal similarity (text + images, text + metadata) without additional models"],"requires":["API access via OpenRouter or cloud provider","Query text and candidate items to rank","Optional: similarity threshold or ranking parameters"],"input_types":["text (query and candidate items)","structured data (documents with metadata)"],"output_types":["ranked list (items ordered by relevance)","similarity scores (numerical relevance metrics)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.1-70b-instruct__cap_2","uri":"capability://planning.reasoning.reasoning.and.step.by.step.problem.decomposition","name":"reasoning and step-by-step problem decomposition","description":"Breaks down complex problems into intermediate reasoning steps using chain-of-thought patterns learned during instruction-tuning. The model generates explicit intermediate reasoning before producing final answers, improving accuracy on math, logic, and multi-step inference tasks. Implements this through learned token sequences that mirror human problem-solving: problem restatement → sub-problem identification → solution of each sub-problem → final synthesis.","intents":["Solve multi-step math problems with visible working and intermediate answers","Debug complex logic by having the model explain its reasoning at each step","Improve factual accuracy on knowledge-intensive questions by forcing explicit reasoning before answering"],"best_for":["Educational applications where showing work is as important as the answer","Quality-critical systems (medical, legal, financial) where reasoning transparency is required","Teams building AI systems that need to justify decisions to stakeholders"],"limitations":["Reasoning steps add 2-5x latency compared to direct answer generation","Model can generate plausible-sounding but incorrect reasoning (hallucinated logic chains)","Reasoning quality degrades on problems outside the training distribution (novel domains, unusual constraints)","No guarantee that intermediate steps are actually used for the final answer — may be post-hoc rationalization"],"requires":["Explicit prompt engineering to trigger chain-of-thought (e.g., 'Let's think step by step')","API access with sufficient token budget for longer responses","Problems that benefit from decomposition (not effective for simple factual recall)"],"input_types":["text (problem statement)","structured data (math equations, logic puzzles, decision trees)"],"output_types":["text (reasoning steps + final answer)","structured reasoning (step-by-step breakdown with intermediate results)"],"categories":["planning-reasoning","problem-solving"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.1-70b-instruct__cap_3","uri":"capability://text.generation.language.knowledge.synthesis.and.fact.grounded.response.generation","name":"knowledge synthesis and fact-grounded response generation","description":"Generates responses grounded in factual knowledge learned during pre-training, with the ability to cite reasoning and acknowledge uncertainty. The model uses learned patterns to distinguish between high-confidence facts (e.g., historical dates, scientific principles) and uncertain claims, often signaling confidence levels through hedging language ('likely', 'probably', 'uncertain'). Does not perform real-time web search or access external knowledge bases — all knowledge comes from training data with a knowledge cutoff date.","intents":["Answer factual questions about history, science, technology, and culture with appropriate confidence levels","Generate summaries of complex topics that synthesize information from multiple domains","Identify gaps in knowledge and explicitly state what the model doesn't know or is uncertain about"],"best_for":["Knowledge base systems and FAQ automation where training data covers the domain well","Educational content generation for established subjects (history, science, literature)","General-purpose Q&A systems where users accept knowledge cutoff limitations"],"limitations":["Knowledge cutoff (training data ends at a specific date) means no awareness of recent events, product releases, or current information","No access to real-time data, web search, or external APIs — cannot verify facts or access live information","Prone to hallucination on niche topics or questions requiring specialized expertise outside training distribution","Cannot distinguish between common misconceptions and accurate information if both were prevalent in training data"],"requires":["API access via OpenRouter or cloud provider","User acceptance of knowledge cutoff limitations","Optional: augmentation with RAG (retrieval-augmented generation) to ground responses in external documents"],"input_types":["text (factual questions, topic requests)"],"output_types":["text (factual response with confidence indicators)","structured data (facts with metadata about certainty)"],"categories":["text-generation-language","knowledge-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.1-70b-instruct__cap_4","uri":"capability://text.generation.language.content.summarization.and.abstractive.compression","name":"content summarization and abstractive compression","description":"Condenses long-form text (articles, documents, conversations) into concise summaries while preserving key information. Uses transformer attention to identify salient content and generate abstractive summaries (rewritten, not extracted) that capture main ideas in fewer tokens. Supports variable compression ratios (e.g., 10:1, 100:1) and can generate summaries at different levels of detail (executive summary vs. detailed outline).","intents":["Quickly extract key points from long documents (research papers, meeting transcripts, legal contracts)","Generate executive summaries for stakeholder reports","Create bullet-point summaries of articles for news aggregation or knowledge management systems"],"best_for":["Document management and knowledge organization systems","News aggregation and content curation platforms","Meeting transcription and note-taking automation"],"limitations":["Abstractive summaries may omit important details or introduce subtle inaccuracies when compressing heavily","No awareness of document structure or importance hierarchy — treats all content equally unless explicitly weighted","Struggles with multi-document summarization (synthesizing across multiple sources)","Context window limits mean very long documents (>100K tokens) may lose information from earlier sections"],"requires":["API access via OpenRouter or cloud provider","Input text in supported formats (plain text, markdown, HTML)","Optional: specification of summary length or detail level"],"input_types":["text (articles, documents, transcripts)","structured text (markdown with headers, HTML with semantic tags)"],"output_types":["text (abstractive summary)","structured summary (bullet points, outline format)"],"categories":["text-generation-language","content-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.1-70b-instruct__cap_5","uri":"capability://text.generation.language.translation.and.cross.lingual.content.generation","name":"translation and cross-lingual content generation","description":"Translates text between 100+ language pairs and generates content in non-English languages with cultural and linguistic appropriateness. Uses multilingual transformer representations learned during pre-training to map semantic meaning across languages while preserving tone, formality, and cultural context. Supports both direct translation and localization (adapting content for cultural context, not just word-for-word translation).","intents":["Translate user-generated content (support tickets, reviews, social media) into English for analysis","Generate multilingual customer support responses without hiring native speakers","Localize marketing copy and product documentation for international audiences"],"best_for":["Global SaaS platforms needing multilingual support without dedicated translation teams","Content platforms serving international audiences","Localization workflows for software and marketing materials"],"limitations":["Translation quality varies significantly by language pair; high-resource pairs (English-Spanish) are strong, low-resource pairs (English-Icelandic) are weaker","No awareness of domain-specific terminology or brand voice unless explicitly provided in context","Cultural nuances and idioms may be lost or mistranslated, especially in creative or marketing content","Cannot handle code-switching (mixing multiple languages in single text) well"],"requires":["API access via OpenRouter or cloud provider","Source language specification (optional; model can often auto-detect)","Target language specification"],"input_types":["text (any language supported by model)","structured content (JSON with language tags, HTML with lang attributes)"],"output_types":["text (translated content in target language)","structured translation (preserving original formatting)"],"categories":["text-generation-language","localization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.1-70b-instruct__cap_6","uri":"capability://text.generation.language.creative.writing.and.content.generation.with.style.control","name":"creative writing and content generation with style control","description":"Generates original creative content (stories, poetry, marketing copy, social media posts) in specified styles and tones. Uses learned patterns from diverse writing examples to generate coherent, engaging content that matches requested tone (formal, casual, humorous, etc.) and style (blog post, tweet, screenplay, etc.). Supports style transfer (rewriting existing content in a different voice) and multi-paragraph generation with narrative consistency.","intents":["Generate marketing copy and product descriptions that match brand voice","Create social media content (tweets, LinkedIn posts, Instagram captions) at scale","Draft blog posts, newsletters, or creative writing with specified tone and style"],"best_for":["Content marketing teams automating routine copy generation","Social media managers creating bulk content calendars","Creative professionals using AI as a brainstorming and drafting tool"],"limitations":["Generated content may lack originality or unique voice if prompts are generic","Tone and style consistency degrades over very long outputs (>2000 tokens)","No awareness of brand guidelines or company voice unless explicitly provided","May produce clichéd or formulaic content, especially for marketing copy"],"requires":["API access via OpenRouter or cloud provider","Clear specification of style, tone, and target audience","Optional: brand guidelines or style examples for consistency"],"input_types":["text (style/tone specifications, topic descriptions)","structured prompts (JSON with style parameters)"],"output_types":["text (creative content in specified style)","structured output (multiple variations, ranked by quality)"],"categories":["text-generation-language","content-creation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.1-70b-instruct__cap_7","uri":"capability://data.processing.analysis.structured.data.extraction.and.schema.based.parsing","name":"structured data extraction and schema-based parsing","description":"Extracts structured information from unstructured text and converts it into JSON, CSV, or other structured formats. Uses learned patterns to identify entities, relationships, and attributes matching a specified schema. Can parse natural language descriptions into structured data (e.g., extracting product details from reviews, converting meeting notes into action items with owners and deadlines).","intents":["Extract key information from documents (invoices, contracts, resumes) into structured databases","Parse user input into structured API requests or database records","Convert natural language specifications into structured configuration files or data models"],"best_for":["Data pipeline automation and ETL workflows","Document processing and information extraction systems","Form automation and data entry reduction"],"limitations":["Extraction accuracy depends on schema clarity and text quality; ambiguous or poorly-formatted input yields errors","No validation against external data sources; extracted data may be factually incorrect or inconsistent","Struggles with complex nested schemas or highly domain-specific terminology","Cannot enforce strict type constraints or referential integrity without post-processing"],"requires":["API access via OpenRouter or cloud provider","Clear schema specification (JSON schema, examples, or natural language description)","Input text in supported formats (plain text, markdown, HTML)"],"input_types":["text (unstructured documents, natural language descriptions)","structured schemas (JSON schema, examples of desired output)"],"output_types":["structured data (JSON, CSV, XML)","validated records (with optional confidence scores)"],"categories":["data-processing-analysis","information-extraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.1-70b-instruct__cap_8","uri":"capability://memory.knowledge.question.answering.with.context.and.retrieval.augmentation","name":"question answering with context and retrieval augmentation","description":"Answers questions based on provided context documents or knowledge bases, with the ability to cite sources and explain reasoning. When used with retrieval augmentation (RAG), the model receives relevant documents retrieved from a vector database, then generates answers grounded in those documents. Supports both extractive QA (finding answers in text) and abstractive QA (synthesizing answers from multiple sources).","intents":["Build customer support systems that answer questions based on company documentation","Create domain-specific Q&A systems (medical, legal, technical) grounded in authoritative sources","Implement search systems that return natural language answers instead of document links"],"best_for":["Enterprise knowledge management and internal documentation systems","Customer support automation with access to knowledge bases","Specialized Q&A systems for regulated domains (medical, legal, financial)"],"limitations":["Answer quality depends entirely on quality and relevance of provided context; missing or incorrect documents yield poor answers","No real-time knowledge updates without reindexing the vector database","Hallucination risk remains even with context — model may generate plausible-sounding answers not supported by provided documents","Requires integration with retrieval system (vector database, search engine) for practical deployment"],"requires":["API access via OpenRouter or cloud provider","Context documents (provided directly or retrieved via RAG)","Optional: vector database (Pinecone, Weaviate, Milvus) for document retrieval","Optional: document indexing and embedding pipeline"],"input_types":["text (question)","context documents (retrieved or provided directly)","structured metadata (document titles, sources, dates)"],"output_types":["text (natural language answer)","structured answer (with source citations and confidence scores)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.1-70b-instruct__cap_9","uri":"capability://planning.reasoning.dialogue.based.task.automation.and.instruction.following","name":"dialogue-based task automation and instruction following","description":"Executes multi-step tasks through conversational interaction, following complex instructions and adapting behavior based on user feedback. The model can break down high-level requests into sub-tasks, ask clarifying questions, and refine outputs based on corrections. Supports iterative refinement loops where users provide feedback and the model adjusts its approach.","intents":["Automate complex workflows (report generation, data analysis, content creation) through conversational specification","Build interactive systems where users guide the AI through multi-step processes with natural language","Create adaptive assistants that learn user preferences and adjust behavior based on feedback"],"best_for":["Interactive automation systems where users need fine-grained control","Assistants for knowledge workers (analysts, writers, developers) augmenting human expertise","Prototyping and exploration tools where users iteratively refine outputs"],"limitations":["Task execution depends on model's ability to understand implicit requirements; ambiguous instructions yield suboptimal results","No persistent memory across sessions — feedback and preferences are not retained between conversations","Iterative refinement adds latency; multi-turn task automation may require 10-30 seconds per step","No integration with external systems (databases, APIs, file systems) without explicit tool-calling support"],"requires":["API access via OpenRouter or cloud provider","Conversational interface (chat UI, messaging API, etc.)","Optional: tool-calling support for integration with external systems"],"input_types":["text (high-level task descriptions, feedback, clarifications)","structured task specifications (JSON with parameters)"],"output_types":["text (task results, clarifying questions, progress updates)","structured outputs (generated artifacts, data, code)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["API access via OpenRouter or direct Meta endpoint","Minimum 40GB VRAM for local deployment, or cloud API key with rate limits","Input formatted as conversation messages (system prompt + user/assistant turns)","API access via OpenRouter or cloud provider","Clear, specific natural language descriptions (vague prompts yield lower-quality code)","Optional: code context or existing codebase snippets for style consistency","Code input in supported languages (Python, JavaScript, Java, Go, Rust, C++, etc.)","Optional: context about code purpose, architecture, or constraints","Query text and candidate items to rank","Optional: similarity threshold or ranking parameters"],"failure_modes":["Context window of 128K tokens means very long conversations (>50K tokens) may hit memory constraints on consumer hardware","No built-in memory persistence across sessions — each conversation starts fresh without access to previous interactions","Instruction-tuning optimizes for following explicit directives; may struggle with implicit, unspoken user needs","Latency increases linearly with context length; 100K token context may add 2-5 seconds per response vs. 500ms for short prompts","Generated code may contain logical errors or security vulnerabilities (e.g., SQL injection, unhandled exceptions) — always requires human review","Performance is not optimized; generated code often lacks algorithmic efficiency improvements","No awareness of existing codebase patterns or style guides unless explicitly provided in context","Struggles with very long functions (>500 lines) or complex architectural patterns requiring deep system knowledge","Code review quality depends on code context; isolated functions may receive poor feedback without understanding broader system design","May miss subtle logical errors or domain-specific issues requiring deep expertise","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.49,"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":"2026-05-03T15:20:45.776Z","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=meta-llama-llama-3.1-70b-instruct","compare_url":"https://unfragile.ai/compare?artifact=meta-llama-llama-3.1-70b-instruct"}},"signature":"UxJsat6d/2eZYusYnxnqf1reFjVr17FXc0VVHMPD4lfXcH3vT4OOXJ77fueMOmADUTtpky5bKYtze3om+omFBQ==","signedAt":"2026-06-19T21:52:30.103Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/meta-llama-llama-3.1-70b-instruct","artifact":"https://unfragile.ai/meta-llama-llama-3.1-70b-instruct","verify":"https://unfragile.ai/api/v1/verify?slug=meta-llama-llama-3.1-70b-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"}}