{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-anthropic-claude-opus-4","slug":"anthropic-claude-opus-4","name":"Anthropic: Claude Opus 4","type":"model","url":"https://openrouter.ai/models/anthropic~claude-opus-4","page_url":"https://unfragile.ai/anthropic-claude-opus-4","categories":["code-review-security"],"tags":["anthropic","api-access","text","image"],"pricing":{"model":"paid","free":false,"starting_price":"$1.50e-5 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-anthropic-claude-opus-4__cap_0","uri":"capability://code.generation.editing.long.context.code.understanding.and.generation.with.extended.reasoning","name":"long-context code understanding and generation with extended reasoning","description":"Claude Opus 4 processes code files and repositories up to 200K tokens in a single request, enabling analysis of entire codebases without chunking or retrieval. The model uses transformer-based attention mechanisms optimized for long sequences, allowing it to maintain coherence across multi-file dependencies, architectural patterns, and historical context. This enables generation of code that respects existing patterns and avoids conflicts across large projects.","intents":["Analyze a 50-file microservice and generate a new feature that integrates with existing patterns","Review an entire codebase for security vulnerabilities in a single pass","Generate comprehensive refactoring across multiple interdependent modules","Understand complex legacy code with deep call chains and implicit dependencies"],"best_for":["Enterprise teams working with large monorepos or complex codebases","Solo developers building LLM agents that need full-project context","Teams migrating or refactoring legacy systems requiring holistic understanding"],"limitations":["200K token limit still requires careful context selection for projects >10M LOC","Latency increases with context size; full-codebase analysis may take 30-60 seconds","No persistent memory across requests — each call starts fresh without learned patterns from previous interactions"],"requires":["Anthropic API key or OpenRouter proxy with Claude Opus 4 access","HTTP client library (curl, Python requests, JavaScript fetch)","Code files in text format (UTF-8 encoded)"],"input_types":["text (source code in any language)","structured code context (JSON/YAML with file paths and content)","markdown documentation"],"output_types":["source code (multiple languages)","code explanations and documentation","structured analysis (JSON with findings)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-opus-4__cap_1","uri":"capability://planning.reasoning.agentic.reasoning.with.extended.chain.of.thought.for.complex.problem.decomposition","name":"agentic reasoning with extended chain-of-thought for complex problem decomposition","description":"Claude Opus 4 implements extended thinking patterns that allow the model to reason through multi-step problems by explicitly working through intermediate steps before generating final answers. This is achieved through transformer-based token prediction with learned reasoning tokens that don't appear in the output but guide internal computation. The model can decompose ambiguous requirements into sub-tasks, identify dependencies, and validate solutions against constraints before committing to output.","intents":["Break down a vague product requirement into concrete technical tasks with dependencies","Debug a complex system failure by reasoning through multiple hypotheses and elimination","Design a system architecture by reasoning through trade-offs and constraints","Validate a proposed solution against multiple criteria before implementation"],"best_for":["Technical leads and architects designing systems","Developers debugging complex, multi-system failures","Teams building LLM agents that need transparent reasoning for audit trails"],"limitations":["Extended reasoning increases latency by 2-5x compared to direct generation","Reasoning tokens consume context budget but don't appear in output, reducing effective usable context","No guarantee of optimal decomposition — reasoning quality depends on problem clarity and model training"],"requires":["Anthropic API key with extended thinking enabled","Client library supporting streaming or full response buffering","Tolerance for 30-120 second response times depending on problem complexity"],"input_types":["text (problem statement, requirements, error logs)","structured data (system diagrams, constraint lists)"],"output_types":["text (reasoning explanation + solution)","structured task lists (JSON with dependencies)","code or architecture diagrams"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-opus-4__cap_10","uri":"capability://safety.moderation.content.moderation.and.safety.filtering.with.custom.policy.enforcement","name":"content moderation and safety filtering with custom policy enforcement","description":"Claude Opus 4 has built-in safety training that reduces generation of harmful content (violence, hate speech, illegal activities), but developers can implement additional custom moderation via system prompts and output filtering. The model's training includes constitutional AI principles that guide it toward helpful, harmless, and honest responses. For applications requiring stricter policies, developers can implement post-generation filtering or use system prompts to enforce domain-specific safety rules. The model will refuse certain requests but may not catch all edge cases.","intents":["Deploy Claude in a customer-facing application with confidence that harmful content is unlikely","Implement custom safety policies for regulated industries (healthcare, finance, legal)","Add content filtering to prevent generation of specific topics or sensitive information","Monitor and audit model outputs for policy violations"],"best_for":["Teams deploying Claude in regulated industries requiring strict content policies","Organizations building customer-facing applications needing safety guarantees","Developers implementing compliance and audit trails for sensitive applications"],"limitations":["Built-in safety is not foolproof — determined users may find jailbreaks or edge cases","Safety training may be overly conservative, refusing legitimate requests (e.g., discussing violence in historical context)","No built-in audit logging or policy violation detection — applications must implement monitoring","Custom safety policies via system prompts are not guaranteed to be enforced"],"requires":["Anthropic API key","System prompts defining custom safety policies (optional)","Output filtering or moderation pipeline (optional but recommended)","Audit logging infrastructure for compliance"],"input_types":["text (user input that may contain harmful requests)"],"output_types":["text (response, or refusal if harmful content requested)"],"categories":["safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-opus-4__cap_2","uri":"capability://image.visual.vision.based.code.analysis.and.documentation.generation.from.screenshots.and.diagrams","name":"vision-based code analysis and documentation generation from screenshots and diagrams","description":"Claude Opus 4 accepts images as input and can analyze screenshots of code editors, architecture diagrams, UI mockups, and system designs to extract information and generate corresponding code or documentation. The model uses vision transformer architecture to parse visual elements, recognize code syntax highlighting patterns, and understand spatial relationships in diagrams. This enables workflows where developers can screenshot a design and have the model generate implementation code or documentation.","intents":["Convert a whiteboard architecture diagram photo into a system design document","Extract code from a screenshot of a legacy system and refactor it","Generate HTML/CSS from a UI mockup screenshot","Analyze a database schema diagram and generate migration code"],"best_for":["Teams using visual design tools (Figma, Lucidchart) who want to automate code generation","Developers documenting legacy systems by photographing existing code","Non-technical stakeholders who can sketch designs but need technical implementation"],"limitations":["OCR accuracy on code screenshots degrades with poor lighting, small fonts, or syntax highlighting artifacts","Cannot reliably extract code from images with resolution <300 DPI or font size <10pt","Vision processing adds 500-1000ms latency compared to text-only requests","No ability to interact with live UI — only static screenshots"],"requires":["Image input in JPEG, PNG, WebP, or GIF format","Maximum image size 20MB per Anthropic API limits","Anthropic API key with vision capability enabled"],"input_types":["image (screenshots, diagrams, mockups, photographs)","text (optional context or instructions)"],"output_types":["source code (HTML, CSS, JavaScript, etc.)","documentation (markdown, structured descriptions)","structured data (JSON schema extracted from diagrams)"],"categories":["image-visual","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-opus-4__cap_3","uri":"capability://text.generation.language.multi.turn.conversation.with.persistent.context.and.instruction.refinement","name":"multi-turn conversation with persistent context and instruction refinement","description":"Claude Opus 4 maintains conversation state across multiple API calls, allowing developers to build interactive workflows where each turn builds on previous context. The model implements a message history mechanism where prior exchanges inform subsequent responses, enabling iterative refinement of code, requirements, or solutions. This is achieved through explicit message passing in the API (not implicit session state), requiring the client to manage conversation history and resend context on each request.","intents":["Iteratively refine a code solution through multiple rounds of feedback and revision","Have a multi-turn conversation about system design with evolving requirements","Build a chatbot that remembers previous context and user preferences","Conduct a technical interview or code review with back-and-forth discussion"],"best_for":["Developers building interactive LLM applications and chatbots","Teams using Claude as a collaborative coding partner","Educational tools requiring multi-turn tutoring interactions"],"limitations":["No server-side session persistence — client must manage and resend full conversation history, increasing token usage","Context window is shared between conversation history and new input, so long conversations reduce space for new requests","No built-in conversation summarization — developers must manually implement context compression for long chats","Each turn incurs full API latency; no streaming of intermediate reasoning across turns"],"requires":["Anthropic API key","Client library supporting message history (Python SDK, JavaScript SDK, or raw HTTP)","Application-level conversation state management (database or in-memory store)"],"input_types":["text (user messages, code snippets, feedback)","images (in multi-turn context)"],"output_types":["text (responses, code, explanations)","structured data (JSON responses for programmatic handling)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-opus-4__cap_4","uri":"capability://data.processing.analysis.structured.output.generation.with.json.schema.validation.and.type.safety","name":"structured output generation with json schema validation and type safety","description":"Claude Opus 4 supports constrained output generation where developers provide a JSON schema and the model generates responses guaranteed to conform to that schema. This is implemented via token-level constraints during decoding — the model's output tokens are filtered at generation time to only allow tokens that maintain schema validity. This enables reliable extraction of structured data (entities, relationships, classifications) without post-processing or validation logic.","intents":["Extract structured entities (names, dates, amounts) from unstructured text with guaranteed JSON output","Generate API responses that conform to a specific OpenAPI schema","Classify text into predefined categories with confidence scores in a fixed format","Extract database records from documents with guaranteed field types and required fields"],"best_for":["Backend developers building APIs that need reliable structured output from LLMs","Data extraction pipelines requiring guaranteed schema compliance","Teams building LLM-powered form-filling or data entry automation"],"limitations":["Schema complexity is limited — deeply nested or recursive schemas may cause generation failures","Constrained decoding adds 10-20% latency overhead compared to unconstrained generation","Model may refuse to generate output if schema is too restrictive for the input (e.g., required field that cannot be inferred)","No support for conditional schemas or complex validation rules beyond JSON Schema Draft 7"],"requires":["Anthropic API key with structured output support enabled","Valid JSON Schema (Draft 7 compatible) provided in API request","Client library supporting schema parameter (Python SDK 0.7+, JavaScript SDK 0.9+)"],"input_types":["text (unstructured content to extract from)","JSON schema (defines output structure)"],"output_types":["JSON (guaranteed to match provided schema)","structured data (parsed into application objects)"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-opus-4__cap_5","uri":"capability://tool.use.integration.function.calling.and.tool.use.with.multi.provider.api.orchestration","name":"function calling and tool use with multi-provider api orchestration","description":"Claude Opus 4 implements function calling via a schema-based tool registry where developers define available functions as JSON schemas and the model generates structured tool-use requests indicating which function to call with what parameters. The model's output includes tool-use blocks that applications parse to invoke actual functions, enabling agentic workflows where the model decides when and how to use external tools. This is distinct from simple prompt-based tool description — the model's training includes explicit tool-use tokens that guide generation toward valid function calls.","intents":["Build an agent that autonomously decides when to call APIs (weather, database, search) to answer questions","Create a code execution environment where Claude can run code and see results iteratively","Implement a multi-step workflow where Claude orchestrates calls to multiple services","Build a chatbot that can fetch real-time data or perform actions on behalf of users"],"best_for":["Developers building autonomous LLM agents","Teams implementing agentic workflows with external tool dependencies","Builders creating AI assistants that need to interact with APIs and databases"],"limitations":["Tool use requires explicit function definition and client-side execution — no built-in function execution","Model may hallucinate tool calls that don't exist or use incorrect parameters despite schema validation","Parallel tool calling (multiple tools in one turn) increases latency and context usage","No built-in error handling or retry logic — applications must implement fallback strategies"],"requires":["Anthropic API key","Client library supporting tool_use blocks (Python SDK 0.7+, JavaScript SDK 0.9+)","Application-level function registry and execution engine","HTTP endpoints or local functions to invoke when model requests tool use"],"input_types":["text (user query or instruction)","tool definitions (JSON schemas describing available functions)"],"output_types":["tool-use blocks (structured requests to invoke functions)","text (final response after tool execution)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-opus-4__cap_6","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 Opus 4 supports batch processing via Anthropic's Batch API, where developers submit multiple requests in a single batch job that processes asynchronously with 50% cost reduction compared to real-time API calls. Requests are queued and processed during off-peak hours, with results returned via webhook or polling. This is implemented as a separate API endpoint that accepts JSONL-formatted request batches and returns results in the same format, enabling cost-effective processing of large volumes of data without real-time latency requirements.","intents":["Process thousands of customer support tickets for sentiment analysis and categorization overnight","Generate code documentation for an entire codebase in a single batch job","Analyze historical logs or datasets for patterns and anomalies at scale","Fine-tune or evaluate model performance on large test datasets"],"best_for":["Teams processing large volumes of data with flexible latency requirements","Cost-conscious organizations running daily/weekly analysis jobs","Data processing pipelines that can tolerate 24-hour turnaround"],"limitations":["Batch processing has 24-hour maximum turnaround — not suitable for real-time applications","Minimum batch size requirements may apply; very small batches don't benefit from cost savings","No streaming responses — results are returned in full after processing completes","Debugging failed requests requires polling or webhook handling; no real-time error feedback"],"requires":["Anthropic API key with batch processing enabled","JSONL-formatted request file (one JSON request per line)","Webhook endpoint or polling mechanism to retrieve results","Batch size >100 requests to justify setup overhead"],"input_types":["JSONL (newline-delimited JSON with API requests)"],"output_types":["JSONL (results matching input request structure)","webhook notifications (optional)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-opus-4__cap_7","uri":"capability://text.generation.language.system.prompt.customization.and.instruction.injection.for.domain.specific.behavior","name":"system prompt customization and instruction injection for domain-specific behavior","description":"Claude Opus 4 allows developers to provide custom system prompts that define the model's behavior, personality, and constraints for specific use cases. The system prompt is sent with every API request and shapes how the model interprets user input and generates responses. This enables building domain-specific assistants (legal advisor, medical consultant, code reviewer) by injecting specialized instructions, constraints, and knowledge without fine-tuning. The model respects system-level instructions with higher priority than user input, enabling guardrails and role-based behavior.","intents":["Build a specialized code reviewer that enforces specific coding standards and architectural patterns","Create a domain-specific assistant (legal, medical, financial) with appropriate disclaimers and constraints","Implement role-based behavior where the model acts as a teacher, mentor, or expert in a specific field","Add safety guardrails and content policies specific to your application"],"best_for":["Teams building specialized AI assistants for specific domains","Developers implementing role-based or persona-driven chatbots","Organizations needing to enforce custom safety policies or compliance requirements"],"limitations":["System prompts are not persistent — must be resent with every API call","Very long system prompts (>10K tokens) consume context budget, reducing space for user input","Model may not perfectly adhere to system instructions if they conflict with training or user input is very explicit","No built-in versioning or A/B testing framework for system prompt iterations"],"requires":["Anthropic API key","Client library supporting system parameter (all official SDKs)","Well-crafted system prompt (typically 100-2000 tokens)"],"input_types":["text (system prompt defining behavior)","text (user input/query)"],"output_types":["text (response shaped by system prompt)"],"categories":["text-generation-language","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-opus-4__cap_8","uri":"capability://code.generation.editing.code.execution.and.debugging.with.iterative.feedback.loops","name":"code execution and debugging with iterative feedback loops","description":"Claude Opus 4 can generate code and reason about execution results when integrated with code execution environments (Jupyter, sandboxed Python, Node.js). The model generates code, receives execution output or errors, and iteratively refines the code based on feedback. This is not a built-in capability but is enabled by tool-use integration where code execution is a tool the model can invoke. The model learns from error messages and stack traces to fix bugs and improve solutions across multiple iterations.","intents":["Generate and debug Python scripts iteratively, fixing errors based on execution feedback","Build data analysis workflows where Claude writes code, sees results, and refines queries","Create interactive coding tutorials where Claude explains code and fixes student submissions","Develop and test algorithms with iterative refinement based on test results"],"best_for":["Educational platforms teaching programming with AI assistance","Data science teams using Claude for exploratory analysis and prototyping","Developers building code generation tools with quality assurance"],"limitations":["Requires sandboxed code execution environment — cannot execute arbitrary code safely","Model may generate code with security vulnerabilities or inefficient algorithms","Iteration latency compounds with each feedback loop — 5-10 iterations can take 1-2 minutes","No built-in timeout or resource limits — long-running code can consume execution resources"],"requires":["Code execution environment (Jupyter, Docker sandbox, AWS Lambda, etc.)","Tool-use integration to invoke code execution as a function","Error handling and output capture to feed back to the model","Security sandbox to prevent malicious code execution"],"input_types":["text (problem description or code to debug)","execution output (stdout, stderr, error messages)"],"output_types":["code (generated or fixed)","execution results (data, visualizations, test results)"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-opus-4__cap_9","uri":"capability://memory.knowledge.semantic.search.and.retrieval.augmented.generation.rag.integration","name":"semantic search and retrieval-augmented generation (rag) integration","description":"Claude Opus 4 can be integrated with vector databases and semantic search systems to implement RAG workflows where relevant documents are retrieved and injected into the prompt before generation. The model processes retrieved context and generates responses grounded in that context, reducing hallucination on factual questions. This is not a built-in capability but is enabled through prompt engineering and tool-use integration where document retrieval is a tool the model can invoke. The model can reason about which documents are relevant and request additional retrieval if needed.","intents":["Build a customer support chatbot that retrieves relevant documentation before answering questions","Create a research assistant that searches a knowledge base and synthesizes findings","Implement a question-answering system over proprietary documents or databases","Build a legal or compliance assistant that grounds answers in specific policies or regulations"],"best_for":["Teams building knowledge-base-driven chatbots","Organizations with proprietary documents needing AI-powered search and synthesis","Developers implementing fact-grounded QA systems"],"limitations":["Retrieval quality depends on vector database and embedding model — poor embeddings lead to irrelevant context","Retrieved context consumes token budget, reducing space for user input and model reasoning","Model may ignore retrieved context if user input is very explicit or contradicts context","No built-in feedback loop to improve retrieval based on answer quality"],"requires":["Vector database (Pinecone, Weaviate, Milvus, etc.) with embedded documents","Embedding model (OpenAI, Sentence Transformers, etc.) for semantic search","Tool-use integration to invoke document retrieval","Document preprocessing and chunking pipeline"],"input_types":["text (user query)","retrieved documents (from vector database)"],"output_types":["text (answer grounded in retrieved context)","structured data (citations, confidence scores)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["Anthropic API key or OpenRouter proxy with Claude Opus 4 access","HTTP client library (curl, Python requests, JavaScript fetch)","Code files in text format (UTF-8 encoded)","Anthropic API key with extended thinking enabled","Client library supporting streaming or full response buffering","Tolerance for 30-120 second response times depending on problem complexity","Anthropic API key","System prompts defining custom safety policies (optional)","Output filtering or moderation pipeline (optional but recommended)","Audit logging infrastructure for compliance"],"failure_modes":["200K token limit still requires careful context selection for projects >10M LOC","Latency increases with context size; full-codebase analysis may take 30-60 seconds","No persistent memory across requests — each call starts fresh without learned patterns from previous interactions","Extended reasoning increases latency by 2-5x compared to direct generation","Reasoning tokens consume context budget but don't appear in output, reducing effective usable context","No guarantee of optimal decomposition — reasoning quality depends on problem clarity and model training","Built-in safety is not foolproof — determined users may find jailbreaks or edge cases","Safety training may be overly conservative, refusing legitimate requests (e.g., discussing violence in historical context)","No built-in audit logging or policy violation detection — applications must implement monitoring","Custom safety policies via system prompts are not guaranteed to be enforced","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-opus-4","compare_url":"https://unfragile.ai/compare?artifact=anthropic-claude-opus-4"}},"signature":"qYiDeHhTKGezFt0AwKWDArbpKi+uEAugzKQ1PMR8NnJ2UtD0Y5JhKyVySzCUht7tJfxqqtJmhV9UkeBB5GFeCA==","signedAt":"2026-06-20T04:54:28.382Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/anthropic-claude-opus-4","artifact":"https://unfragile.ai/anthropic-claude-opus-4","verify":"https://unfragile.ai/api/v1/verify?slug=anthropic-claude-opus-4","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"}}