{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-meta-llama-llama-3.3-70b-instruct","slug":"meta-llama-llama-3.3-70b-instruct","name":"Meta: Llama 3.3 70B Instruct","type":"model","url":"https://openrouter.ai/models/meta-llama~llama-3.3-70b-instruct","page_url":"https://unfragile.ai/meta-llama-llama-3.3-70b-instruct","categories":["llm-apis"],"tags":["meta-llama","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$1.00e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-meta-llama-llama-3.3-70b-instruct__cap_0","uri":"capability://text.generation.language.multilingual.instruction.following.text.generation","name":"multilingual instruction-following text generation","description":"Generates coherent, contextually appropriate text responses across 8+ languages using a 70B parameter transformer architecture with instruction-tuning applied post-pretraining. The model uses standard causal language modeling with attention mechanisms optimized for long-context reasoning, enabling it to follow complex multi-step instructions and maintain semantic consistency across diverse linguistic domains without language-specific fine-tuning branches.","intents":["I need to generate natural language responses in multiple languages from a single model without language switching overhead","I want to build a multilingual chatbot that understands nuanced instructions in non-English languages","I need to process user queries in mixed-language contexts and respond appropriately in the user's language"],"best_for":["teams building global SaaS products requiring multilingual support without model switching","developers creating conversational AI for non-English-primary markets","enterprises needing instruction-following capabilities across EMEA, APAC, and Americas regions"],"limitations":["Performance degrades on low-resource languages (e.g., Amharic, Tagalog) due to underrepresentation in training data","No explicit language detection — requires upstream language identification for optimal routing","Context window limited to ~8K tokens, constraining multilingual document processing tasks","Instruction-tuning optimized for English-style prompting patterns; non-English instruction formats may require prompt engineering"],"requires":["API key for OpenRouter or direct Meta API access","HTTP/2 capable client library (requests, httpx, or equivalent)","Minimum 24GB VRAM for local deployment; API-based access requires only network connectivity"],"input_types":["plain text","structured prompts with system/user/assistant roles","code snippets for explanation or debugging","multilingual mixed-language inputs"],"output_types":["plain text","markdown-formatted text","code blocks with syntax highlighting","structured responses (JSON when prompted)"],"categories":["text-generation-language","multilingual-nlp"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.3-70b-instruct__cap_1","uri":"capability://planning.reasoning.few.shot.in.context.learning.with.chain.of.thought.reasoning","name":"few-shot in-context learning with chain-of-thought reasoning","description":"Leverages transformer attention mechanisms to learn task patterns from 2-8 examples provided in the prompt context, enabling zero-shot and few-shot task adaptation without retraining. The model applies implicit chain-of-thought reasoning by generating intermediate reasoning steps when prompted with structured examples, using learned patterns from instruction-tuning to decompose complex problems into solvable sub-tasks.","intents":["I want to adapt the model to a new task by showing it 3-5 examples without fine-tuning","I need the model to explain its reasoning step-by-step for complex problem-solving tasks","I want to establish consistent output formatting by providing format examples in the prompt"],"best_for":["rapid prototyping teams iterating on task definitions without fine-tuning cycles","developers building domain-specific applications with limited labeled data","researchers evaluating model capabilities on novel tasks with minimal setup"],"limitations":["Few-shot performance plateaus at 5-8 examples; additional examples may introduce noise rather than improve accuracy","Requires careful example selection and ordering — poor examples degrade performance more than no examples","Chain-of-thought reasoning adds 30-50% latency overhead due to longer output sequences","Context window constraints (8K tokens) limit the number of examples for large input domains"],"requires":["Structured prompt engineering with clear example formatting","Understanding of the model's instruction-tuning patterns to craft effective demonstrations","Sufficient context window budget (typically 2-4K tokens for examples + query)"],"input_types":["plain text examples with input-output pairs","structured prompts with explicit reasoning markers (e.g., 'Let me think step by step')","code examples for programming tasks"],"output_types":["text with intermediate reasoning steps","structured outputs matching example format","code with explanatory comments"],"categories":["planning-reasoning","prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.3-70b-instruct__cap_2","uri":"capability://code.generation.editing.code.generation.and.explanation.with.language.agnostic.understanding","name":"code generation and explanation with language-agnostic understanding","description":"Generates syntactically correct code across 15+ programming languages (Python, JavaScript, Java, C++, Go, Rust, etc.) using transformer-based code understanding learned from diverse code corpora. The model produces code with contextual awareness of language idioms, standard libraries, and common patterns; it also explains existing code by decomposing logic into natural language descriptions, leveraging instruction-tuning to balance code accuracy with readability.","intents":["I need to generate boilerplate code or complete partial implementations across multiple languages","I want the model to explain what a code snippet does in plain English for documentation or learning","I need to refactor or optimize code while maintaining semantic equivalence"],"best_for":["developers using AI-assisted coding in polyglot codebases","technical documentation teams automating code explanation generation","junior developers learning programming concepts through AI-generated examples"],"limitations":["Code generation quality varies by language popularity; rare languages (e.g., Elixir, Clojure) produce lower-quality output","No built-in syntax validation — generated code may contain subtle bugs requiring human review","Limited understanding of project-specific conventions, APIs, or internal libraries without explicit context","Security vulnerabilities (e.g., SQL injection patterns, hardcoded credentials) may appear in generated code without explicit safety prompting"],"requires":["Clear code context or specification (docstring, comments, or examples)","Language specification in prompt (e.g., 'Generate Python 3.9+ code')","Linting/testing infrastructure to validate generated code before deployment"],"input_types":["natural language specifications or requirements","partial code with TODO comments","existing code snippets for explanation or refactoring","function signatures or type hints"],"output_types":["complete code implementations","code snippets with inline comments","natural language explanations of code logic","refactored code with optimization notes"],"categories":["code-generation-editing","developer-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.3-70b-instruct__cap_3","uri":"capability://data.processing.analysis.structured.data.extraction.and.json.schema.compliance","name":"structured data extraction and json schema compliance","description":"Extracts structured information from unstructured text and generates JSON outputs conforming to user-specified schemas through instruction-tuning that emphasizes format adherence. The model uses attention mechanisms to identify relevant entities and relationships, then formats output according to schema constraints provided in the prompt; it can validate against simple schema rules (required fields, data types) through learned patterns without external validation libraries.","intents":["I need to extract customer information from support tickets and output it as structured JSON","I want to parse natural language descriptions into a predefined data model for database ingestion","I need to convert unstructured documents into structured records matching my application schema"],"best_for":["data engineering teams automating ETL pipelines with LLM-based extraction","teams building form-filling or data entry automation without traditional NLP pipelines","applications requiring flexible schema-based extraction without rigid regex or rule-based systems"],"limitations":["No guaranteed schema compliance — model may omit required fields or produce invalid JSON without explicit validation","Complex nested schemas (3+ levels) degrade accuracy; flattened schemas perform more reliably","Hallucination risk for missing data — model may invent plausible values rather than indicating absence","Large schemas (20+ fields) consume significant context window, reducing available space for input documents"],"requires":["Clear schema specification in prompt (JSON schema, TypeScript interface, or example structure)","Post-processing validation and error handling for malformed JSON or missing fields","External schema validator (e.g., jsonschema library) to enforce constraints"],"input_types":["unstructured text (emails, documents, chat messages)","semi-structured data (HTML, markdown)","natural language descriptions"],"output_types":["valid JSON conforming to specified schema","structured records with typed fields","CSV or delimited formats (when prompted)"],"categories":["data-processing-analysis","structured-extraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.3-70b-instruct__cap_4","uri":"capability://text.generation.language.conversational.context.management.with.multi.turn.dialogue","name":"conversational context management with multi-turn dialogue","description":"Maintains coherent dialogue across multiple conversation turns by processing the full conversation history as context, using transformer self-attention to track entity references, pronouns, and topic continuity. The model applies instruction-tuning patterns for conversational roles (system, user, assistant) to generate contextually appropriate responses that reference previous statements, ask clarifying questions, and maintain consistent personality or tone across turns without explicit state management.","intents":["I need to build a chatbot that remembers previous messages and responds coherently across 10+ turns","I want the model to maintain context about user preferences or constraints mentioned earlier in conversation","I need to implement a multi-turn Q&A system where answers reference previous questions"],"best_for":["teams building conversational AI products (customer support, virtual assistants)","developers creating interactive tutoring or coaching applications","applications requiring stateless conversation handling without external session storage"],"limitations":["Context window limit (8K tokens) constrains conversation length; older messages are lost when context exceeds limit","No explicit memory mechanism — model cannot recall information from conversations beyond current context window","Attention complexity grows quadratically with conversation length, increasing latency for long conversations","Model may lose track of complex multi-party conversations or conversations with many topic switches"],"requires":["Client-side conversation history management (array of message objects with roles)","Conversation truncation or summarization strategy for conversations exceeding 6K tokens","Proper message formatting with system/user/assistant role markers"],"input_types":["conversation history as array of role-tagged messages","system prompts defining assistant personality or constraints","user messages in natural language"],"output_types":["assistant response text","multi-sentence responses with contextual references","clarifying questions or follow-ups"],"categories":["text-generation-language","conversational-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.3-70b-instruct__cap_5","uri":"capability://text.generation.language.domain.specific.knowledge.application.through.prompt.engineering","name":"domain-specific knowledge application through prompt engineering","description":"Applies domain-specific knowledge by incorporating specialized terminology, concepts, and reasoning patterns provided in system prompts or context sections, enabling the model to generate domain-appropriate responses without fine-tuning. The model uses attention mechanisms to weight domain-specific context heavily in generation, applying learned instruction-following patterns to prioritize provided domain knowledge over general training data when conflicts arise.","intents":["I need the model to answer medical questions using current clinical guidelines I provide in the prompt","I want to build a legal document assistant that applies specific jurisdiction laws and regulations","I need the model to generate technical content using our company's specific terminology and standards"],"best_for":["specialized domain teams (legal, medical, financial) building AI assistants without domain-specific model training","enterprises with proprietary knowledge bases wanting to inject context without fine-tuning","consultants and agencies building domain-specific solutions for clients"],"limitations":["Domain knowledge must fit within context window (typically 2-4K tokens for substantial domain context)","Model may still apply conflicting general knowledge if domain context is ambiguous or incomplete","No persistent learning — domain knowledge must be re-provided for each request","Complex domain reasoning (e.g., multi-step legal analysis) may exceed model capabilities regardless of context"],"requires":["Curated domain knowledge or reference material formatted for prompt inclusion","Clear instructions on how to apply domain knowledge (e.g., 'prioritize the provided guidelines over general knowledge')","Domain expertise to validate model outputs for accuracy"],"input_types":["domain-specific reference material (guidelines, regulations, standards)","domain terminology glossaries","example domain-specific queries and expected responses","user queries in domain context"],"output_types":["domain-appropriate responses using provided terminology","citations or references to provided domain knowledge","structured outputs following domain conventions"],"categories":["text-generation-language","domain-adaptation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.3-70b-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, marketing copy, poetry, dialogue) in specified styles and tones using learned patterns from diverse writing corpora combined with instruction-tuning for style adherence. The model applies attention mechanisms to maintain stylistic consistency across longer outputs, using system prompts to establish voice, tone, and genre constraints that guide generation without explicit style transfer mechanisms.","intents":["I need to generate marketing copy in a specific brand voice for different product categories","I want to create fictional dialogue for characters with distinct personalities and speech patterns","I need to generate creative story premises or plot outlines in specific genres"],"best_for":["marketing and content teams automating copy generation for campaigns","game developers generating NPC dialogue and narrative content","creative professionals using AI as a brainstorming and ideation tool"],"limitations":["Style consistency degrades in outputs longer than 2-3K tokens; longer pieces may drift from specified style","Originality not guaranteed — model may reproduce patterns similar to training data without explicit novelty constraints","Tone control is approximate; subtle emotional nuances may not be captured accurately","Cultural sensitivity and appropriateness require explicit guardrails; model may generate offensive content without constraints"],"requires":["Clear style and tone specification in system prompt (e.g., 'professional but friendly', 'noir detective fiction')","Examples of target style for few-shot learning","Content moderation and review process for brand-sensitive applications"],"input_types":["style/tone specifications","genre or format constraints","topic or subject matter","example content in target style"],"output_types":["original creative text","marketing copy and ad variations","dialogue and character speech","story outlines and plot summaries"],"categories":["text-generation-language","creative-writing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.3-70b-instruct__cap_7","uri":"capability://text.generation.language.technical.documentation.and.explanation.generation","name":"technical documentation and explanation generation","description":"Generates clear technical documentation, API references, and code explanations by applying learned patterns for technical writing clarity, structure, and completeness. The model uses instruction-tuning to produce well-organized documentation with appropriate section hierarchies, code examples, and explanatory prose; it can generate documentation from code signatures, requirements, or existing documentation patterns without external documentation generation tools.","intents":["I need to auto-generate API documentation from function signatures and docstrings","I want to create user guides and tutorials for technical products","I need to document legacy code that lacks proper documentation"],"best_for":["technical teams automating documentation generation for APIs and libraries","startups documenting products quickly during rapid development","open-source maintainers scaling documentation efforts"],"limitations":["Generated documentation may lack domain-specific accuracy without expert review","Code examples in documentation may contain subtle bugs or non-idiomatic patterns","Documentation structure may not match project-specific conventions without explicit examples","Incomplete or inaccurate source material (e.g., vague function names) produces poor documentation"],"requires":["Clear source material (code, requirements, or existing documentation examples)","Documentation structure templates or examples for consistency","Expert review process to validate technical accuracy"],"input_types":["code signatures and docstrings","function/API specifications","requirements or feature descriptions","existing documentation examples"],"output_types":["markdown or HTML documentation","API reference pages","tutorial and guide content","code examples with explanations"],"categories":["text-generation-language","technical-writing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.3-70b-instruct__cap_8","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 logical problems, mathematical questions, and reasoning tasks by decomposing them into intermediate steps using learned chain-of-thought patterns from instruction-tuning. The model generates explicit reasoning steps before final answers, using attention mechanisms to track logical dependencies and maintain consistency across multi-step solutions without external symbolic reasoning engines.","intents":["I need the model to solve math problems and show its work step-by-step","I want to use the model for logical reasoning tasks like puzzle-solving or constraint satisfaction","I need the model to debug complex problems by reasoning through potential causes"],"best_for":["educational applications requiring step-by-step problem solving","teams building AI tutoring systems for STEM subjects","applications requiring transparent reasoning for auditing or explanation"],"limitations":["Mathematical accuracy limited to arithmetic and basic algebra; advanced calculus or symbolic math may fail","Reasoning quality degrades on problems requiring 10+ logical steps","No access to external tools (calculators, symbolic solvers) — all computation is implicit in token generation","Hallucination risk in reasoning steps — intermediate steps may be plausible but incorrect"],"requires":["Explicit prompting for step-by-step reasoning (e.g., 'Let me think step by step')","Clear problem specification with all constraints","Validation of reasoning steps for accuracy-critical applications"],"input_types":["mathematical problems","logical puzzles and constraints","debugging scenarios with symptoms and context","decision-making scenarios with trade-offs"],"output_types":["step-by-step reasoning with intermediate conclusions","final answers with justification","alternative solution approaches"],"categories":["planning-reasoning","problem-solving"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"low","permissions":["API key for OpenRouter or direct Meta API access","HTTP/2 capable client library (requests, httpx, or equivalent)","Minimum 24GB VRAM for local deployment; 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