{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-anthropic-claude-3.5-haiku","slug":"anthropic-claude-3.5-haiku","name":"Anthropic: Claude 3.5 Haiku","type":"model","url":"https://openrouter.ai/models/anthropic~claude-3.5-haiku","page_url":"https://unfragile.ai/anthropic-claude-3.5-haiku","categories":["llm-apis"],"tags":["anthropic","api-access","text","image"],"pricing":{"model":"paid","free":false,"starting_price":"$8.00e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-anthropic-claude-3.5-haiku__cap_0","uri":"capability://text.generation.language.fast.context.aware.text.generation.with.vision.support","name":"fast-context-aware text generation with vision support","description":"Generates coherent, contextually-aware text responses using a transformer-based architecture optimized for low-latency inference. Processes both text and image inputs through a unified embedding space, enabling multi-modal reasoning without separate vision encoders. Implements speculative decoding and KV-cache optimization to reduce time-to-first-token and total generation latency while maintaining output quality across diverse domains.","intents":["I need to generate responses in real-time chat applications without noticeable lag","I want to analyze images and text together in a single API call","I need fast inference for high-throughput production systems with cost constraints","I want to process documents with embedded images for content understanding"],"best_for":["teams building real-time chat applications and customer support bots","developers creating cost-sensitive production systems with high request volume","solo developers prototyping multi-modal applications with tight latency budgets"],"limitations":["Context window of 200K tokens is smaller than Claude 3.5 Sonnet (200K) but adequate for most use cases; very long document processing may require chunking","Image understanding is less detailed than larger models — struggles with dense technical diagrams or fine-grained visual reasoning","No native file upload support — images must be base64-encoded or passed via URL, adding preprocessing overhead","Inference latency is ~500-800ms for typical requests, acceptable for chat but not sub-100ms real-time applications"],"requires":["Anthropic API key or OpenRouter API key","HTTP client library (curl, Python requests, Node.js fetch)","Base64 encoding capability for image inputs","Network connectivity to Anthropic or OpenRouter endpoints"],"input_types":["text (plain text, markdown, code snippets)","images (JPEG, PNG, GIF, WebP via base64 or URL)","structured prompts with system messages"],"output_types":["text (plain text, markdown, code)","structured JSON when prompted","streaming text tokens via Server-Sent Events"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-3.5-haiku__cap_1","uri":"capability://tool.use.integration.tool.use.with.schema.based.function.calling","name":"tool-use with schema-based function calling","description":"Enables Claude to invoke external tools and APIs through a schema-based function registry. The model receives tool definitions as JSON schemas, reasons about which tools to call and with what parameters, then returns structured tool-use blocks containing function names and arguments. Implements automatic tool result injection back into the conversation context, enabling multi-turn tool orchestration without manual prompt engineering.","intents":["I want Claude to autonomously call APIs and databases to fetch real-time information","I need to build an agent that can execute code, query databases, and take actions based on reasoning","I want to define custom functions that Claude can invoke without hardcoding them into prompts","I need multi-step workflows where Claude decides which tools to call based on intermediate results"],"best_for":["developers building autonomous agents with external integrations","teams creating AI-powered customer support systems that need to query internal databases","builders prototyping AI workflows that combine reasoning with deterministic function execution"],"limitations":["Tool calling adds ~100-200ms latency per decision cycle due to model inference and tool execution overhead","No built-in error recovery — if a tool call fails, the model must be explicitly told the error and asked to retry; requires manual error handling in application code","Schema complexity is limited — deeply nested or recursive schemas may confuse the model's tool selection","Tool results must fit within remaining context window; very large tool outputs require truncation or summarization"],"requires":["Anthropic API key with tool-use capability enabled","JSON schema definitions for each tool (OpenAPI 3.0 compatible format)","Application code to execute tools and inject results back into conversation","Error handling logic to manage tool execution failures"],"input_types":["tool definitions (JSON schemas with name, description, parameters)","user queries or prompts","tool execution results (as text or structured data)"],"output_types":["tool-use blocks (function name + parameters as JSON)","text responses when tool calling is not needed","final answers after tool execution completes"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-3.5-haiku__cap_10","uri":"capability://safety.moderation.content.moderation.and.safety.filtering","name":"content moderation and safety filtering","description":"Evaluates text for harmful content including hate speech, violence, sexual content, and other policy violations using learned patterns from training data. The model can classify content risk levels, explain why content is flagged, and suggest modifications to make content compliant. Implements safety guidelines that prevent the model from generating harmful content, though these can be overridden with explicit prompts. Supports custom safety policies through system prompts and fine-tuning.","intents":["I want to filter user-generated content for harmful material before publishing","I need to classify content risk levels for moderation workflows","I want Claude to refuse generating harmful content while explaining why","I need to implement custom safety policies for my application domain"],"best_for":["teams building user-generated content platforms","developers implementing content moderation pipelines","organizations with strict compliance requirements"],"limitations":["Safety filtering is not foolproof — adversarial prompts can sometimes bypass safety guidelines; requires layered defense with human review","False positives and negatives — the model may flag benign content as harmful or miss actual violations; accuracy depends on training data","Custom safety policies require fine-tuning — system prompts alone have limited effectiveness for domain-specific safety rules","No real-time feedback loop — the model cannot learn from moderation decisions without retraining; requires manual policy updates"],"requires":["Anthropic API key","Content to evaluate (text input)","Optional: custom safety policies defined in system prompts","Human review process for edge cases and policy updates"],"input_types":["text content to evaluate","optional: context about content source or intended audience","optional: custom safety guidelines"],"output_types":["safety classification (safe, low-risk, high-risk, blocked)","explanation of why content was flagged","suggestions for modifying content to be compliant"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-3.5-haiku__cap_11","uri":"capability://tool.use.integration.api.based.deployment.with.openrouter.integration","name":"api-based deployment with openrouter integration","description":"Accessible via Anthropic's native API and OpenRouter's unified API gateway, enabling deployment across multiple cloud providers and edge environments without vendor lock-in. Supports standard HTTP REST endpoints with JSON request/response format, enabling integration with any HTTP client or framework. Implements authentication via API keys and supports both synchronous and asynchronous request patterns through webhooks or polling.","intents":["I want to use Claude through a unified API that supports multiple model providers","I need to deploy Claude in a multi-cloud or hybrid environment","I want to integrate Claude into existing applications using standard HTTP APIs","I need to switch between Claude and other models without rewriting integration code"],"best_for":["teams building multi-model applications with provider flexibility","developers deploying Claude across multiple cloud platforms","organizations with existing HTTP-based API infrastructure"],"limitations":["OpenRouter adds ~50-100ms latency due to request routing through an additional proxy layer","OpenRouter pricing may differ from direct Anthropic API pricing; requires comparison before committing","Vendor lock-in to OpenRouter if using their unified interface; switching providers requires code changes","Rate limiting and quota management may be less granular through OpenRouter than direct API"],"requires":["Anthropic API key (for direct API) or OpenRouter API key (for unified gateway)","HTTP client library (curl, requests, fetch, etc.)","Knowledge of REST API conventions and JSON formatting","Network connectivity to Anthropic or OpenRouter endpoints"],"input_types":["JSON request bodies with prompts, model parameters, and optional images","HTTP headers with authentication and content-type"],"output_types":["JSON response bodies with generated text or tool calls","HTTP status codes indicating success or failure","streaming responses via Server-Sent Events (optional)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-3.5-haiku__cap_2","uri":"capability://text.generation.language.streaming.text.generation.with.token.level.control","name":"streaming text generation with token-level control","description":"Outputs text progressively via Server-Sent Events (SSE) or streaming HTTP responses, delivering tokens as they are generated rather than waiting for full completion. Implements token-level streaming with optional stop sequences, allowing applications to interrupt generation mid-stream or apply real-time filtering. Supports both text and tool-use streaming, enabling UI updates and early termination without waiting for full response generation.","intents":["I want to display text to users as it's being generated for a chat-like experience","I need to interrupt long-running generations early if the user cancels or a stop condition is met","I want to reduce perceived latency by showing partial results while the model is still thinking","I need to stream tool-use decisions and results in real-time for agent applications"],"best_for":["frontend developers building chat interfaces and conversational UIs","teams creating real-time collaborative applications with AI assistance","builders optimizing perceived latency in user-facing AI applications"],"limitations":["Streaming adds complexity to error handling — errors may occur mid-stream after partial content has been sent to the client, requiring graceful degradation","Token-level streaming prevents batch optimizations — throughput is lower than non-streaming mode due to per-token overhead","Stop sequences must be defined upfront; dynamic stop conditions require client-side post-processing of streamed tokens","Network latency becomes visible — slow connections will show token-by-token delays, potentially degrading UX"],"requires":["HTTP client supporting streaming responses (fetch with ReadableStream, axios with responseType: 'stream', etc.)","Server-Sent Events (SSE) support or raw HTTP streaming capability","Application code to buffer and display tokens as they arrive","Timeout and error handling for interrupted streams"],"input_types":["text prompts","images (for vision-based streaming)","tool definitions (for streaming tool calls)"],"output_types":["streamed text tokens (one per SSE event)","streamed tool-use blocks (JSON fragments)","stop reason indicators (max_tokens, stop_sequence, end_turn)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-3.5-haiku__cap_3","uri":"capability://image.visual.vision.based.image.understanding.and.analysis","name":"vision-based image understanding and analysis","description":"Processes images (JPEG, PNG, GIF, WebP) alongside text to perform visual reasoning, object detection, text extraction, and scene understanding. Images are encoded as base64 or provided via URL and embedded into the conversation context. The model analyzes visual content using a unified vision-language architecture, enabling tasks like screenshot analysis, diagram interpretation, and image-based question answering without separate vision model calls.","intents":["I want to analyze screenshots and extract information about UI layouts or errors","I need to extract text from images (OCR) and answer questions about the content","I want to understand diagrams, charts, or technical drawings and explain them","I need to process user-uploaded images in a chat application and respond contextually"],"best_for":["developers building document processing and OCR applications","teams creating visual QA and testing automation tools","builders prototyping multi-modal chat applications with image support"],"limitations":["Image understanding is less detailed than specialized vision models — struggles with dense technical diagrams, small text, or fine-grained visual details","No native image editing or manipulation — can only analyze, not generate or modify images","Image size limits: maximum ~4MB per image; very high-resolution images may be downsampled, losing detail","OCR accuracy is lower than dedicated OCR engines — works well for clean text but struggles with handwriting or degraded documents"],"requires":["Anthropic API key","Image files in supported formats (JPEG, PNG, GIF, WebP)","Base64 encoding capability or ability to provide image URLs","Network connectivity for URL-based images"],"input_types":["images (JPEG, PNG, GIF, WebP via base64 or URL)","text queries about images","multiple images in a single request"],"output_types":["text descriptions and analysis","extracted text (OCR results)","structured data (JSON) when prompted to extract specific information","answers to visual questions"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-3.5-haiku__cap_4","uri":"capability://automation.workflow.batch.processing.with.cost.optimization","name":"batch processing with cost optimization","description":"Processes multiple API requests in a single batch job, enabling asynchronous execution with 50% cost reduction compared to standard API calls. Requests are queued, processed in batches during off-peak hours, and results are retrieved via polling or webhook callbacks. Implements request deduplication and result caching to further reduce redundant processing, ideal for non-time-sensitive workloads like data analysis, content generation, and report generation.","intents":["I want to process thousands of documents or queries at lower cost","I need to generate content in bulk (emails, summaries, translations) without real-time constraints","I want to analyze large datasets using Claude without paying full API prices","I need to schedule batch jobs that run overnight or during off-peak hours"],"best_for":["teams processing large datasets or bulk content generation","startups optimizing API costs for non-time-sensitive workloads","data analysts and researchers using Claude for batch analysis"],"limitations":["Results are not immediately available — typical latency is 1-24 hours depending on queue depth and batch size","No real-time feedback or streaming — results are returned as complete responses only","Batch API requires explicit request formatting (JSONL) and polling for results, adding application complexity","Failed requests in a batch may not be retried automatically — requires manual error handling and resubmission"],"requires":["Anthropic Batch API access (requires separate enablement)","Requests formatted as JSONL (JSON Lines) with specific schema","Application code to submit batches, poll for results, or handle webhooks","Storage for batch IDs and result retrieval logic"],"input_types":["JSONL-formatted batch requests (one JSON object per line)","each request contains a prompt, model, and optional parameters"],"output_types":["batch job ID (for polling)","results file (JSONL format) containing responses for each request","webhook callbacks with result URLs"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-3.5-haiku__cap_5","uri":"capability://memory.knowledge.context.window.management.with.200k.token.capacity","name":"context window management with 200k token capacity","description":"Maintains a 200,000-token context window, enabling processing of long documents, multi-turn conversations, and large code repositories in a single API call. Implements efficient token counting and context packing to maximize information density within the window. Supports conversation history preservation across multiple turns without explicit summarization, allowing the model to reference earlier messages and maintain coherent long-form interactions.","intents":["I want to analyze entire codebases or long documents without chunking or summarization","I need to maintain multi-turn conversations with full history without losing context","I want to provide extensive examples or reference materials in a single prompt","I need to process long transcripts, books, or technical specifications in one request"],"best_for":["developers analyzing large codebases or documentation","teams building long-form conversational applications","researchers processing lengthy documents or transcripts"],"limitations":["Larger context windows increase latency — processing 200K tokens takes ~2-3x longer than 10K tokens, making it unsuitable for real-time applications","Cost scales linearly with context size — using full 200K window is expensive; applications should minimize unnecessary context","Token counting overhead — applications must accurately count tokens to avoid exceeding limits; off-by-one errors can cause request failures","Model reasoning quality may degrade with very long contexts — relevant information can be buried in noise, requiring careful prompt engineering"],"requires":["Anthropic API key","Token counting library (Anthropic provides reference implementations)","Application code to manage context and avoid exceeding 200K limit","Awareness of token pricing (input tokens cost less than output tokens)"],"input_types":["long text documents (up to 200K tokens)","multi-turn conversation history","code repositories or technical documentation","images (which consume tokens in the context window)"],"output_types":["text responses with references to earlier context","structured analysis of long documents","code reviews or suggestions based on full codebase context"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-3.5-haiku__cap_6","uri":"capability://code.generation.editing.code.generation.and.technical.problem.solving","name":"code generation and technical problem-solving","description":"Generates, analyzes, and debugs code across 40+ programming languages using transformer-based pattern recognition trained on vast code repositories. Implements syntax-aware generation that respects language-specific conventions, indentation, and idioms. Supports code completion, refactoring suggestions, bug detection, and explanation of existing code. The model understands context from surrounding code and project structure, enabling coherent multi-file code generation and architectural suggestions.","intents":["I want Claude to write functions or complete code snippets in my preferred language","I need help debugging code or understanding why a specific error is occurring","I want to refactor code for better performance, readability, or maintainability","I need to understand how existing code works or generate documentation for it"],"best_for":["solo developers and small teams building applications","developers learning new programming languages or frameworks","teams using Claude for code review and technical debt reduction"],"limitations":["Code generation accuracy varies by language — works well for Python, JavaScript, Java but less reliably for niche or domain-specific languages","No execution environment — generated code must be tested separately; Claude cannot run code to verify correctness","Context limitations — understanding of large codebases is limited to what fits in the context window; architectural decisions may miss project-wide patterns","Security awareness is limited — generated code may contain security vulnerabilities; requires manual review before production use"],"requires":["Anthropic API key","Code snippets or file contents as text input","Knowledge of the target programming language","Manual testing and security review of generated code"],"input_types":["code snippets or full files","natural language descriptions of desired functionality","error messages or stack traces","existing code to refactor or analyze"],"output_types":["generated code (functions, classes, scripts)","refactored code with explanations","bug reports and fix suggestions","code explanations and documentation"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-3.5-haiku__cap_7","uri":"capability://data.processing.analysis.structured.data.extraction.with.schema.validation","name":"structured data extraction with schema validation","description":"Extracts structured information from unstructured text using JSON schema definitions, enabling reliable parsing of documents, emails, and web content into machine-readable formats. The model receives a schema definition and returns JSON-formatted output that conforms to the schema, with optional validation to ensure all required fields are present. Supports complex nested structures, arrays, and conditional fields, enabling extraction of hierarchical data from documents.","intents":["I want to extract key information from documents (invoices, contracts, emails) into structured JSON","I need to parse user input and convert it to a standardized data format for database storage","I want to extract entities (names, dates, amounts) from unstructured text reliably","I need to validate that extracted data conforms to a specific schema before processing"],"best_for":["teams building document processing and data extraction pipelines","developers creating form-filling or data entry automation","data teams converting unstructured sources into structured databases"],"limitations":["Extraction accuracy depends on schema clarity — ambiguous or overly complex schemas may confuse the model, leading to missing or incorrect fields","No built-in validation — extracted JSON may not conform to the schema; requires post-processing validation","Hallucination risk — model may invent data for missing fields rather than returning null; requires careful schema design and prompt engineering","Performance degrades with very large schemas — schemas with 50+ fields may result in slower inference and lower accuracy"],"requires":["Anthropic API key","JSON schema definition for the target data structure","Unstructured text input (documents, emails, web content)","JSON validation library for post-processing (optional but recommended)"],"input_types":["unstructured text (documents, emails, web pages)","JSON schema definitions (OpenAPI 3.0 compatible)","optional: examples of desired output format"],"output_types":["JSON objects conforming to the provided schema","validation errors if output doesn't match schema","null or empty values for missing information"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-3.5-haiku__cap_8","uri":"capability://text.generation.language.multi.turn.conversation.with.memory.and.context.preservation","name":"multi-turn conversation with memory and context preservation","description":"Maintains coherent multi-turn conversations by preserving conversation history within the context window, enabling the model to reference previous messages, learn from corrections, and maintain consistent personas or knowledge across turns. Implements automatic context management where earlier messages are included in each API call, allowing the model to build on prior reasoning without explicit summarization. Supports system prompts to define conversation behavior and constraints.","intents":["I want to build a chatbot that remembers earlier messages and maintains context across turns","I need Claude to learn from user feedback and adjust its behavior in subsequent responses","I want to define a specific persona or role that Claude maintains throughout a conversation","I need to implement multi-turn workflows where each response depends on previous context"],"best_for":["developers building chat applications and conversational interfaces","teams creating customer support bots with context awareness","builders prototyping interactive AI agents and tutoring systems"],"limitations":["Context window is finite — very long conversations will exceed the 200K token limit, requiring truncation or summarization of earlier messages","No persistent memory — conversation history must be stored and managed by the application; Claude has no built-in database","Token costs accumulate with conversation length — each turn includes all previous messages, increasing costs for long conversations","Context ordering matters — the model may over-weight recent messages and under-weight earlier context, potentially losing important information"],"requires":["Anthropic API key","Application code to store and manage conversation history","Token counting to monitor context window usage","Database or session storage for conversation persistence"],"input_types":["system prompt (optional, defines conversation behavior)","conversation history (array of user and assistant messages)","current user message"],"output_types":["assistant response (text, code, or structured data)","conversation metadata (tokens used, stop reason)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-3.5-haiku__cap_9","uri":"capability://planning.reasoning.reasoning.and.planning.with.chain.of.thought.decomposition","name":"reasoning and planning with chain-of-thought decomposition","description":"Breaks down complex problems into step-by-step reasoning chains, enabling the model to work through multi-step logic, mathematical problems, and decision-making tasks. Implements chain-of-thought prompting patterns where the model explicitly shows intermediate reasoning steps before arriving at conclusions. Supports planning and task decomposition for workflows that require breaking large problems into smaller, manageable subtasks with clear dependencies.","intents":["I want Claude to solve complex math or logic problems by showing its work step-by-step","I need to break down a large project into smaller tasks with clear dependencies and ordering","I want Claude to explain its reasoning for a decision or recommendation","I need to verify that Claude's logic is sound by examining intermediate steps"],"best_for":["developers building AI agents that need to solve complex problems","teams using Claude for planning and task decomposition","educators and researchers studying AI reasoning capabilities"],"limitations":["Chain-of-thought reasoning increases token usage — showing intermediate steps consumes more tokens, increasing costs","Reasoning quality is limited by model size — Haiku's smaller size means less sophisticated reasoning compared to Sonnet; struggles with very complex multi-step problems","No formal verification — intermediate steps may contain logical errors; the model's reasoning is not guaranteed to be correct","Latency increases with reasoning depth — longer chains of thought take longer to generate, making it unsuitable for real-time applications"],"requires":["Anthropic API key","Prompts designed to encourage step-by-step reasoning (e.g., 'Let's think step by step')","Application code to parse and validate intermediate reasoning steps","Acceptance that reasoning may be imperfect and requires human review"],"input_types":["complex problems or questions","prompts encouraging step-by-step reasoning","optional: examples of desired reasoning format"],"output_types":["step-by-step reasoning chains","intermediate conclusions and logic","final answer with supporting reasoning"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["Anthropic API key or OpenRouter API key","HTTP client library (curl, Python requests, Node.js fetch)","Base64 encoding capability for image inputs","Network connectivity to Anthropic or OpenRouter endpoints","Anthropic API key with tool-use capability enabled","JSON schema definitions for each tool (OpenAPI 3.0 compatible format)","Application code to execute tools and inject results back into conversation","Error handling logic to manage tool execution failures","Anthropic API key","Content to evaluate (text input)"],"failure_modes":["Context window of 200K tokens is smaller than Claude 3.5 Sonnet (200K) but adequate for most use cases; very long document processing may require chunking","Image understanding is less detailed than larger models — struggles with dense technical diagrams or fine-grained visual reasoning","No native file upload support — images must be base64-encoded or passed via URL, adding preprocessing overhead","Inference latency is ~500-800ms for typical requests, acceptable for chat but not sub-100ms real-time applications","Tool calling adds ~100-200ms latency per decision cycle due to model inference and tool execution overhead","No built-in error recovery — if a tool call fails, the model must be explicitly told the error and asked to retry; requires manual error handling in application code","Schema complexity is limited — deeply nested or recursive schemas may confuse the model's tool selection","Tool results must fit within remaining context window; very large tool outputs require truncation or summarization","Safety filtering is not foolproof — adversarial prompts can sometimes bypass safety guidelines; requires layered defense with human review","False positives and negatives — the model may flag benign content as harmful or miss actual violations; accuracy depends on training data","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.49,"ecosystem":0.27,"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":"2026-05-24T12:16:24.483Z","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=anthropic-claude-3.5-haiku","compare_url":"https://unfragile.ai/compare?artifact=anthropic-claude-3.5-haiku"}},"signature":"J9x1XfaKzws9ph7jO04J7kFkj3WZmgwvhb/DpxL8w/oGD+2zud96C1oQvhQyE2zjT/ZvuHhv+62NVyIkEcozDQ==","signedAt":"2026-06-20T14:17:13.633Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/anthropic-claude-3.5-haiku","artifact":"https://unfragile.ai/anthropic-claude-3.5-haiku","verify":"https://unfragile.ai/api/v1/verify?slug=anthropic-claude-3.5-haiku","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"}}