{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-anthropic-claude-3.7-sonnet","slug":"anthropic-claude-3.7-sonnet","name":"Anthropic: Claude 3.7 Sonnet","type":"model","url":"https://openrouter.ai/models/anthropic~claude-3.7-sonnet","page_url":"https://unfragile.ai/anthropic-claude-3.7-sonnet","categories":["llm-apis"],"tags":["anthropic","api-access","text","image"],"pricing":{"model":"paid","free":false,"starting_price":"$3.00e-6 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-anthropic-claude-3.7-sonnet__cap_0","uri":"capability://text.generation.language.multi.turn.conversational.reasoning.with.extended.context.windows","name":"multi-turn conversational reasoning with extended context windows","description":"Claude 3.7 Sonnet maintains coherent multi-turn conversations through a transformer-based architecture with 200K token context window, enabling it to track conversation history, reference earlier statements, and build on prior reasoning without losing context. The model uses attention mechanisms to weight relevant historical context while managing computational complexity through efficient token batching and caching strategies.","intents":["Build chatbots that maintain conversation state across 50+ exchanges without losing context","Create interactive debugging sessions where the model references earlier code snippets and error traces","Develop document analysis workflows that process 100K+ token documents in a single request"],"best_for":["Teams building conversational AI applications requiring long-context reasoning","Developers creating document-heavy workflows (legal review, research synthesis, codebase analysis)","Builders prototyping multi-step reasoning agents with persistent memory needs"],"limitations":["Context window of 200K tokens may still be insufficient for multi-document analysis at scale (>500K tokens)","Latency increases with context length; typical response time is 2-5 seconds for 100K token contexts","No built-in conversation persistence — requires external database to store and retrieve conversation history","Token counting for billing purposes requires manual tracking; no native cost estimation API"],"requires":["Anthropic API key or OpenRouter API key with Anthropic model access","HTTP client library (curl, Python requests, JavaScript fetch, etc.)","Understanding of token counting for cost estimation (Claude tokenizer available via API)"],"input_types":["text","code snippets","structured prompts with system instructions"],"output_types":["text","code","structured JSON (via prompt engineering)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-3.7-sonnet__cap_1","uri":"capability://planning.reasoning.hybrid.reasoning.mode.with.configurable.inference.speed.accuracy.tradeoff","name":"hybrid reasoning mode with configurable inference speed-accuracy tradeoff","description":"Claude 3.7 Sonnet introduces a hybrid reasoning approach allowing users to toggle between fast-response mode (optimized for latency) and extended-reasoning mode (optimized for accuracy on complex problems). This is implemented through conditional computation paths in the model architecture where extended reasoning mode activates additional transformer layers and iterative refinement steps, while fast mode uses a streamlined inference path with fewer decoding steps.","intents":["Choose between rapid responses for simple queries and deep reasoning for complex problem-solving","Optimize API costs by using fast mode for straightforward tasks and reasoning mode only when needed","Build applications that adapt inference strategy based on query complexity or user preferences"],"best_for":["Product teams needing to balance latency and accuracy across different use cases","Cost-conscious builders who want to minimize token consumption for simple queries","Developers building adaptive AI systems that route queries to appropriate inference modes"],"limitations":["Extended reasoning mode consumes 2-3x more tokens than fast mode, significantly increasing API costs","No automatic detection of query complexity — requires explicit user selection or heuristic-based routing logic","Extended reasoning mode latency can exceed 10-15 seconds for complex problems, unsuitable for real-time applications","Reasoning mode output format is not standardized; reasoning steps are embedded in text rather than structured format"],"requires":["Anthropic API key with access to Claude 3.7 Sonnet model","API parameter support for reasoning mode selection (check current API documentation)","Token budget planning to account for 2-3x token consumption in reasoning mode"],"input_types":["text prompts","code with complex logic","mathematical problems","multi-step reasoning tasks"],"output_types":["text with reasoning steps embedded","code solutions","structured explanations"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-3.7-sonnet__cap_10","uri":"capability://code.generation.editing.fine.tuning.capability.for.domain.specific.model.adaptation","name":"fine-tuning capability for domain-specific model adaptation","description":"Claude 3.7 Sonnet supports fine-tuning on custom datasets to adapt the model for specific domains, writing styles, or specialized tasks. Fine-tuning uses parameter-efficient techniques (likely LoRA or similar) that update a small subset of model weights while keeping the base model frozen, reducing computational cost and enabling rapid iteration. Fine-tuned models are deployed as separate endpoints, allowing users to maintain both base and specialized versions.","intents":["Adapt Claude to specific domain terminology, writing style, or task patterns (e.g., legal document generation, medical coding)","Improve performance on specialized tasks by training on domain-specific examples","Create white-label versions of Claude with custom behavior and knowledge"],"best_for":["Enterprise teams with domain-specific use cases and sufficient training data (100+ examples)","Organizations building specialized AI products for niche markets","Teams with budget for fine-tuning infrastructure and ongoing model management"],"limitations":["Requires minimum training dataset size (typically 100-1000 examples) to be effective","Fine-tuning cost is significant (often $1000+) and requires careful ROI analysis","Fine-tuned models are separate endpoints; no automatic fallback to base model","Fine-tuning results are unpredictable; may improve some tasks while degrading others","No built-in evaluation framework; requires external benchmarking to measure improvement"],"requires":["Anthropic API key with fine-tuning access","Training dataset in specified format (typically JSONL with examples)","Minimum 100-1000 training examples depending on task complexity","Budget for fine-tuning costs and separate model endpoint"],"input_types":["training dataset (JSONL format)","examples of desired behavior"],"output_types":["fine-tuned model endpoint","performance metrics"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-3.7-sonnet__cap_2","uri":"capability://code.generation.editing.code.generation.and.analysis.with.multi.language.support.and.structural.awareness","name":"code generation and analysis with multi-language support and structural awareness","description":"Claude 3.7 Sonnet generates and analyzes code across 40+ programming languages using transformer-based code understanding trained on diverse codebases. The model recognizes syntactic and semantic patterns, maintains consistency with existing code style, and can perform tasks like refactoring, bug detection, and test generation. Implementation leverages learned representations of Abstract Syntax Trees (ASTs) and common design patterns without explicit parsing, enabling it to understand code structure implicitly.","intents":["Generate boilerplate code, API integrations, and utility functions in any major language","Review code for bugs, security vulnerabilities, and style inconsistencies","Refactor legacy code while preserving functionality and adapting to modern patterns","Generate unit tests, integration tests, and test fixtures for existing code"],"best_for":["Individual developers and small teams seeking code generation assistance","Engineering teams using Claude for code review and quality assurance workflows","Polyglot teams working across multiple programming languages and frameworks"],"limitations":["Generated code may contain logical errors or security vulnerabilities; requires human review before production use","Performance optimization and low-level systems programming (C, Rust) are less reliable than high-level languages","No real-time compilation or execution feedback; cannot validate generated code against actual runtime behavior","Context-dependent code generation (e.g., matching internal framework conventions) requires explicit examples in prompt","Large codebases (>50K lines) may exceed context window, requiring selective file inclusion"],"requires":["Anthropic API key","Code snippets or full files as text input (no binary or compiled code)","Clear context about target language, framework, and coding standards"],"input_types":["code snippets","full source files","pseudocode","natural language specifications","error messages and stack traces"],"output_types":["code","code explanations","refactoring suggestions","test cases","documentation"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-3.7-sonnet__cap_3","uri":"capability://image.visual.vision.based.image.understanding.and.analysis","name":"vision-based image understanding and analysis","description":"Claude 3.7 Sonnet processes images through a multimodal transformer architecture that encodes visual information alongside text, enabling it to describe images, extract text via OCR, answer questions about visual content, and analyze diagrams. The vision component uses a vision encoder (similar to CLIP-style architectures) that converts images into token embeddings, which are then processed by the same transformer backbone as text, enabling seamless vision-language reasoning.","intents":["Extract text from screenshots, documents, and photographs (OCR functionality)","Analyze charts, graphs, and diagrams to extract insights and data","Answer questions about image content, spatial relationships, and visual elements","Describe images for accessibility purposes or content moderation workflows"],"best_for":["Developers building document processing pipelines that include scanned PDFs and images","Teams automating visual content analysis and quality assurance workflows","Accessibility-focused teams generating alt text and image descriptions at scale"],"limitations":["OCR accuracy is lower than specialized OCR engines (Tesseract, AWS Textract) for low-resolution or heavily stylized text","Image understanding is limited to 2D visual content; cannot process 3D models, video, or animated content","No image generation capability — can only analyze and describe images, not create them","Image processing adds ~500ms-1s latency per request compared to text-only requests","Maximum image resolution and file size constraints (typically 20MB, exact limits vary by API version)"],"requires":["Anthropic API key with vision model access","Images in supported formats (JPEG, PNG, GIF, WebP)","Base64 encoding or URL-accessible image hosting for API submission"],"input_types":["images (JPEG, PNG, GIF, WebP)","screenshots","diagrams","charts","photographs"],"output_types":["text descriptions","extracted text (OCR)","structured data (via prompt engineering)","analysis and insights"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-3.7-sonnet__cap_4","uri":"capability://data.processing.analysis.structured.output.generation.with.json.schema.validation","name":"structured output generation with json schema validation","description":"Claude 3.7 Sonnet can generate structured outputs (JSON, XML, YAML) that conform to user-specified schemas through constrained decoding techniques. The model uses a schema-aware decoding process that restricts token generation to valid continuations according to the provided schema, ensuring output is always parseable and matches the expected structure. This is implemented via a token-masking layer that filters invalid tokens at each generation step.","intents":["Extract structured data from unstructured text (e.g., parse customer feedback into predefined fields)","Generate API responses in exact JSON format matching OpenAPI specifications","Create structured knowledge bases by converting documents into consistent data formats","Validate and enforce data consistency in LLM-generated outputs"],"best_for":["Developers building data extraction pipelines that feed into databases or APIs","Teams integrating Claude into existing systems requiring strict output format compliance","Data engineering teams automating ETL workflows with LLM-based transformation steps"],"limitations":["Schema validation adds ~100-300ms latency due to token masking overhead at each generation step","Complex nested schemas with many optional fields may reduce generation quality or increase token consumption","Schema must be provided in exact format (JSON Schema, etc.); no automatic schema inference from examples","Constrained decoding may prevent the model from expressing nuanced information that doesn't fit schema constraints","No support for conditional schemas or dynamic schema selection based on input content"],"requires":["Anthropic API key with structured output support (check API version)","JSON Schema or equivalent schema definition in supported format","Clear understanding of required vs optional fields and valid value types"],"input_types":["text","unstructured documents","natural language descriptions"],"output_types":["JSON","XML","YAML","other structured formats"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-3.7-sonnet__cap_5","uri":"capability://tool.use.integration.function.calling.with.multi.provider.schema.support","name":"function calling with multi-provider schema support","description":"Claude 3.7 Sonnet supports tool/function calling through a schema-based interface that accepts function definitions and returns structured function calls with arguments. The model learns to recognize when a function should be invoked based on user intent, generates the function name and parameters as structured output, and can chain multiple function calls in sequence. Implementation uses the same constrained decoding as structured output to ensure valid function call syntax.","intents":["Build agents that autonomously call APIs, databases, or local functions to accomplish tasks","Create multi-step workflows where the model decides which tools to use and in what order","Integrate Claude into existing tool ecosystems (Zapier, Make, custom APIs) via function calling"],"best_for":["Developers building autonomous agents and agentic workflows","Teams integrating Claude into tool-calling frameworks (LangChain, LlamaIndex, etc.)","Builders creating no-code/low-code automation platforms with LLM decision-making"],"limitations":["Model cannot execute functions directly; requires external runtime to handle function execution and return results","No built-in error handling or retry logic; failed function calls require explicit error messages fed back to the model","Function calling adds latency due to multiple round-trips (model → function execution → model)","Complex tool ecosystems (>20 functions) may exceed context window or confuse the model about which tool to use","No native support for streaming function calls; requires polling or callback-based architectures"],"requires":["Anthropic API key","Function definitions in JSON Schema format","External runtime to execute functions and return results","Mechanism to feed function results back to the model for continued reasoning"],"input_types":["natural language requests","function definitions (JSON Schema)","function execution results"],"output_types":["function calls (structured JSON)","function arguments","final text response after tool use"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-3.7-sonnet__cap_6","uri":"capability://text.generation.language.instruction.following.and.system.prompt.customization","name":"instruction-following and system prompt customization","description":"Claude 3.7 Sonnet accepts system prompts that define custom behavior, tone, constraints, and role-playing scenarios. The model uses the system prompt as a high-priority context that influences all subsequent responses, implemented through special token handling that weights system instructions higher in the attention mechanism. This enables fine-grained control over model behavior without fine-tuning, allowing users to create specialized versions for specific domains or use cases.","intents":["Create domain-specific AI assistants (legal advisor, medical consultant, technical expert) via system prompts","Enforce safety constraints and content policies through system-level instructions","Customize tone, formality, and communication style for different audiences","Build role-playing scenarios and interactive fiction experiences"],"best_for":["Teams building white-label AI products with customized behavior","Developers creating specialized assistants for specific domains or industries","Builders implementing content moderation and safety guardrails via prompt engineering"],"limitations":["System prompt effectiveness depends on clarity and specificity; vague instructions may be ignored or misinterpreted","No guarantee that system prompts will override user instructions in adversarial scenarios (prompt injection risk)","System prompt changes require API calls to update; no persistent configuration storage","Very long system prompts (>10K tokens) consume significant context window, reducing space for user input","No A/B testing framework built-in; requires external experimentation infrastructure to measure system prompt effectiveness"],"requires":["Anthropic API key","Clear understanding of desired behavior and constraints","Iterative testing and refinement of system prompts"],"input_types":["system prompts (text)","user messages","conversation history"],"output_types":["customized responses","role-specific outputs"],"categories":["text-generation-language","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-3.7-sonnet__cap_7","uri":"capability://automation.workflow.batch.processing.api.for.cost.optimized.high.volume.inference","name":"batch processing api for cost-optimized high-volume inference","description":"Claude 3.7 Sonnet supports batch processing through an asynchronous API that accepts multiple requests in a single batch job, processes them with lower priority but significantly reduced pricing (typically 50% discount), and returns results asynchronously. Batches are processed during off-peak hours using spare capacity, implemented through a job queue system that prioritizes real-time requests while batching non-urgent work. This enables cost-effective processing of large volumes without impacting real-time API performance.","intents":["Process thousands of documents or data points at lower cost for non-urgent analysis","Run daily/weekly batch jobs for content moderation, classification, or summarization","Optimize API costs for applications with flexible latency requirements"],"best_for":["Data teams processing large datasets with flexible timelines","Cost-conscious startups and enterprises processing high volumes","Teams with batch processing workflows (nightly jobs, weekly reports, etc.)"],"limitations":["Results are returned asynchronously, typically within 24 hours; unsuitable for real-time applications","Batch jobs have lower priority and may be delayed during peak usage periods","Minimum batch size requirements (typically 10-100 requests) may not suit small-scale use cases","No streaming or incremental result delivery; must wait for entire batch to complete","Batch API has separate rate limits and quota management from real-time API"],"requires":["Anthropic API key with batch processing access","Batch job submission in specified JSON format","Polling mechanism or webhook handler to retrieve results","Flexible timeline (24+ hour turnaround acceptable)"],"input_types":["batch job JSON with multiple requests","text","code","images"],"output_types":["batch results JSON","text responses","structured data"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-3.7-sonnet__cap_8","uri":"capability://memory.knowledge.prompt.caching.for.reduced.latency.and.cost.on.repeated.contexts","name":"prompt caching for reduced latency and cost on repeated contexts","description":"Claude 3.7 Sonnet supports prompt caching, which stores frequently-used context (system prompts, documents, code files) in a cache layer that persists across multiple API calls. Cached content is processed once and reused, reducing both latency and token consumption for subsequent requests using the same context. Implementation uses a content-addressable cache keyed by context hash, with automatic cache invalidation when content changes.","intents":["Analyze the same large document multiple times with different questions without re-processing","Build conversational interfaces with large static context (knowledge bases, system prompts) that persists across sessions","Reduce costs for applications that repeatedly use the same code files or documents as context"],"best_for":["Teams building document Q&A systems with repeated queries against the same documents","Developers creating specialized assistants with large static system prompts or knowledge bases","Applications with high query volume against stable context"],"limitations":["Cache hits require identical context; any change to cached content invalidates the cache","Cache TTL is typically 5 minutes; longer sessions require periodic cache refresh","Minimum cache size (typically 1024 tokens) means small contexts don't benefit from caching","Cache management adds complexity; requires tracking cache keys and invalidation logic","Pricing benefits are modest (10-25% reduction) unless context is very large (>10K tokens)"],"requires":["Anthropic API key with prompt caching support","Stable, reusable context (documents, system prompts, code files)","API implementation that tracks cache keys and manages invalidation"],"input_types":["large static context (documents, code, system prompts)","user queries"],"output_types":["text responses","analysis results"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-3.7-sonnet__cap_9","uri":"capability://safety.moderation.safety.and.content.moderation.with.constitutional.ai.principles","name":"safety and content moderation with constitutional ai principles","description":"Claude 3.7 Sonnet is trained using Constitutional AI (CAI) principles that embed safety and ethical guidelines directly into the model through reinforcement learning from AI feedback (RLHF). The model learns to refuse harmful requests, avoid generating toxic content, and provide balanced perspectives on controversial topics. Safety is implemented through learned behavioral patterns rather than post-hoc filtering, enabling nuanced refusals that explain why a request cannot be fulfilled.","intents":["Deploy AI assistants in production with built-in safety guardrails","Reduce manual content moderation overhead by leveraging the model's learned safety behaviors","Build applications that handle sensitive topics (mental health, legal advice, medical information) responsibly"],"best_for":["Teams deploying AI in regulated industries (healthcare, finance, legal) requiring safety compliance","Developers building consumer-facing AI products with content moderation requirements","Organizations prioritizing responsible AI and ethical AI deployment"],"limitations":["Safety behaviors are learned patterns, not absolute guarantees; adversarial prompts may still elicit harmful content","Refusals are sometimes overly cautious, declining legitimate requests (e.g., educational content about sensitive topics)","No customizable safety levels; all users receive the same safety training","Safety training may introduce biases or limitations in certain domains (e.g., creative writing, fiction)","No transparency into specific safety rules or decision boundaries; safety is implicit in model weights"],"requires":["Anthropic API key","Understanding of model limitations and potential failure modes","Additional application-level safety measures for high-risk use cases"],"input_types":["any user input"],"output_types":["safe responses","refusals with explanations"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["Anthropic API key or OpenRouter API key with Anthropic model access","HTTP client library (curl, Python requests, JavaScript fetch, etc.)","Understanding of token counting for cost estimation (Claude tokenizer available via API)","Anthropic API key with access to Claude 3.7 Sonnet model","API parameter support for reasoning mode selection (check current API documentation)","Token budget planning to account for 2-3x token consumption in reasoning mode","Anthropic API key with fine-tuning access","Training dataset in specified format (typically JSONL with examples)","Minimum 100-1000 training examples depending on task complexity","Budget for fine-tuning costs and separate model endpoint"],"failure_modes":["Context window of 200K tokens may still be insufficient for multi-document analysis at scale (>500K tokens)","Latency increases with context length; typical response time is 2-5 seconds for 100K token contexts","No built-in conversation persistence — requires external database to store and retrieve conversation history","Token counting for billing purposes requires manual tracking; no native cost estimation API","Extended reasoning mode consumes 2-3x more tokens than fast mode, significantly increasing API costs","No automatic detection of query complexity — requires explicit user selection or heuristic-based routing logic","Extended reasoning mode latency can exceed 10-15 seconds for complex problems, unsuitable for real-time applications","Reasoning mode output format is not standardized; reasoning steps are embedded in text rather than structured format","Requires minimum training dataset size (typically 100-1000 examples) to be effective","Fine-tuning cost is significant (often $1000+) and requires careful ROI analysis","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.47,"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-3.7-sonnet","compare_url":"https://unfragile.ai/compare?artifact=anthropic-claude-3.7-sonnet"}},"signature":"IjViXZMM7WY7/pgRo/GTdFLwbK/OrB8EvvYpGj+PWBHS5TRyZI5xs5ZEJwXkSpS+Y9N31ovzoBCW8hLmfxX6DQ==","signedAt":"2026-06-20T09:29:20.654Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/anthropic-claude-3.7-sonnet","artifact":"https://unfragile.ai/anthropic-claude-3.7-sonnet","verify":"https://unfragile.ai/api/v1/verify?slug=anthropic-claude-3.7-sonnet","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"}}