{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-anthropic-claude-opus-4.6","slug":"anthropic-claude-opus-4.6","name":"Anthropic: Claude Opus 4.6","type":"model","url":"https://openrouter.ai/models/anthropic~claude-opus-4.6","page_url":"https://unfragile.ai/anthropic-claude-opus-4.6","categories":["ai-agents"],"tags":["anthropic","api-access","text","image"],"pricing":{"model":"paid","free":false,"starting_price":"$5.00e-6 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-anthropic-claude-opus-4.6__cap_0","uri":"capability://code.generation.editing.long.context.code.generation.with.workflow.awareness","name":"long-context code generation with workflow awareness","description":"Claude Opus 4.6 processes extended code contexts (200K token window) while maintaining semantic understanding of multi-file codebases and project structure. The model uses transformer-based attention mechanisms optimized for long-range dependencies, enabling it to generate code that respects existing patterns, imports, and architectural constraints across an entire codebase rather than isolated snippets. This is particularly effective for agents that need to modify or extend code across multiple files in a single reasoning pass.","intents":["Generate code changes that span multiple files while maintaining consistency with existing codebase patterns","Refactor large codebases by understanding cross-file dependencies and architectural relationships","Build multi-step coding agents that can reason about entire project structure in a single context window","Debug complex issues by analyzing full stack traces and related code files simultaneously"],"best_for":["teams building AI-powered code agents for enterprise refactoring","developers automating multi-file code generation workflows","solo developers working on large monorepos who need context-aware completions"],"limitations":["200K token limit still requires careful context selection for very large codebases (>1M LOC)","Long context processing adds latency (~2-5 seconds per request) compared to shorter-context models","Attention mechanisms scale quadratically, making extremely long contexts (>150K tokens) slower than shorter ones","No built-in caching of parsed ASTs — each request re-processes the full context"],"requires":["Anthropic API key or OpenRouter API key","HTTP client capable of handling streaming responses","Minimum 30KB request payload capacity for typical code contexts"],"input_types":["text (code files, documentation, requirements)","structured prompts with file paths and line numbers","multi-turn conversation history with code snippets"],"output_types":["code (Python, JavaScript, Go, Rust, Java, etc.)","structured diffs or patch formats","explanations with inline code comments"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-opus-4.6__cap_1","uri":"capability://planning.reasoning.agentic.reasoning.with.extended.planning.horizons","name":"agentic reasoning with extended planning horizons","description":"Claude Opus 4.6 implements chain-of-thought reasoning patterns optimized for multi-step agent workflows, using internal reasoning tokens to decompose complex tasks before execution. The model can maintain state across multiple reasoning steps, backtrack when encountering contradictions, and adjust strategy mid-task based on intermediate results. This is achieved through training on reinforcement learning from human feedback (RLHF) specifically tuned for agent behavior rather than single-turn chat.","intents":["Build autonomous agents that can plan, execute, and adapt across 5+ step workflows","Implement multi-turn reasoning loops where the agent evaluates its own outputs and iterates","Create agents that can handle ambiguous requirements by asking clarifying questions and reasoning through trade-offs","Develop systems where the model must choose between multiple tool calls and reason about their order"],"best_for":["teams building autonomous coding agents or research assistants","developers implementing complex decision-making systems with LLMs","organizations deploying agents that must operate without human intervention for extended periods"],"limitations":["Extended reasoning increases latency by 3-10x compared to direct generation","Reasoning tokens are billed at the same rate as output tokens, increasing cost for complex tasks","No guaranteed determinism — same input may produce different reasoning paths on different calls","Reasoning quality degrades on tasks outside the model's training distribution (e.g., novel domains)"],"requires":["Anthropic API key with access to extended thinking features","Ability to handle streaming responses with multiple reasoning segments","Timeout configuration of 60+ seconds for complex reasoning tasks"],"input_types":["natural language task descriptions","structured task specifications with constraints","multi-turn conversation history with feedback"],"output_types":["reasoning traces (internal thought process)","structured action plans","final outputs with justifications"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-opus-4.6__cap_10","uri":"capability://code.generation.editing.test.case.generation.with.coverage.awareness","name":"test case generation with coverage awareness","description":"Claude Opus 4.6 can generate unit tests, integration tests, and edge case tests by analyzing code structure and understanding what scenarios need to be tested. The model generates tests in the appropriate framework (Jest, pytest, JUnit, etc.) with assertions that verify expected behavior. It can identify edge cases and error conditions that should be tested, producing more comprehensive test coverage than manual test writing.","intents":["Automatically generate unit tests for existing code","Create edge case tests that cover error conditions","Generate integration tests that verify component interactions","Improve test coverage by identifying untested code paths"],"best_for":["development teams automating test generation","developers improving test coverage on legacy code","teams implementing test-driven development workflows"],"limitations":["Generated tests may not cover all edge cases or business logic requirements","Tests require manual review to ensure they test the right things","Cannot generate tests for code that requires external services or complex setup","Test quality depends on code clarity and function signatures"],"requires":["Anthropic API key","Code to test (as text in context window)","Test framework specification (Jest, pytest, etc.)"],"input_types":["code (functions or classes to test)","test framework specification"],"output_types":["test code (in specified framework)","test descriptions (explaining what each test verifies)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-opus-4.6__cap_11","uri":"capability://safety.moderation.content.moderation.and.safety.filtering","name":"content moderation and safety filtering","description":"Claude Opus 4.6 includes built-in safety mechanisms that filter harmful content, refuse requests for illegal activities, and decline to generate content that violates usage policies. The model uses learned safety constraints from RLHF training to identify and refuse harmful requests. This is implemented at the model level, not as a post-processing filter, making it more reliable and harder to circumvent.","intents":["Ensure generated content complies with usage policies","Prevent the model from generating illegal or harmful content","Maintain safety in multi-turn conversations","Audit and monitor model outputs for policy violations"],"best_for":["organizations deploying models in regulated industries","teams building customer-facing AI applications","companies implementing content moderation workflows"],"limitations":["Safety filtering may refuse legitimate requests if they appear similar to harmful ones","No transparency into why specific requests are refused","Safety constraints are not customizable — cannot adjust for specific use cases","Adversarial prompts may occasionally bypass safety mechanisms"],"requires":["Anthropic API key","Acceptance of usage policies"],"input_types":["text (any user input)"],"output_types":["text (with safety filtering applied)","refusal messages (when content violates policies)"],"categories":["safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-opus-4.6__cap_12","uri":"capability://code.generation.editing.multilingual.code.generation.and.translation","name":"multilingual code generation and translation","description":"Claude Opus 4.6 can generate code in 50+ programming languages and can translate code between languages while preserving functionality and idioms. The model understands language-specific patterns, libraries, and best practices, generating code that follows conventions for each language. It can also translate code from one language to another while maintaining semantic equivalence.","intents":["Generate code in multiple programming languages from the same specification","Translate code between languages (e.g., Python to JavaScript)","Generate language-specific implementations of algorithms","Support polyglot development teams with code generation"],"best_for":["teams working with multiple programming languages","developers migrating code between languages","organizations building language-agnostic code generation systems"],"limitations":["Code quality varies by language — better for popular languages (Python, JavaScript, Go) than niche ones","Language-specific idioms may not be perfectly captured","Generated code may not use the most efficient language-specific features","No access to language-specific linters or type checkers"],"requires":["Anthropic API key","Language specification (which language to generate or translate to)"],"input_types":["code (in any supported language)","natural language specification"],"output_types":["code (in specified language)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-opus-4.6__cap_13","uri":"capability://automation.workflow.batch.processing.for.high.volume.code.generation","name":"batch processing for high-volume code generation","description":"Claude Opus 4.6 supports batch API processing for high-volume code generation tasks, where multiple requests are submitted together and processed asynchronously. This enables cost-effective processing of large numbers of code generation tasks (e.g., generating tests for 1000 functions) at a 50% discount compared to real-time API calls. Batch processing is optimized for throughput rather than latency.","intents":["Generate tests or documentation for entire codebases in batch","Process large numbers of code generation requests cost-effectively","Analyze multiple code repositories for issues or patterns","Generate code variants or implementations for A/B testing"],"best_for":["teams processing large volumes of code generation tasks","organizations optimizing costs for batch code generation","developers automating large-scale code analysis or generation"],"limitations":["Batch processing is asynchronous — results are not available immediately","Minimum batch size requirements may apply","No real-time feedback or streaming responses in batch mode","Batch jobs may take hours to complete depending on queue"],"requires":["Anthropic API key with batch processing enabled","Batch job submission in JSONL format","Polling or webhook mechanism to retrieve results"],"input_types":["JSONL (batch of requests in JSON Lines format)"],"output_types":["JSONL (batch of responses)"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-opus-4.6__cap_2","uri":"capability://image.visual.vision.based.code.understanding.and.documentation.generation","name":"vision-based code understanding and documentation generation","description":"Claude Opus 4.6 accepts image inputs (screenshots, diagrams, UI mockups) and can extract code structure, architecture diagrams, or UI specifications from visual representations. The model uses multimodal transformer layers to align visual and textual understanding, enabling it to generate code from wireframes, understand architecture from hand-drawn diagrams, or extract code from screenshots. This capability bridges visual design and code generation in a single model call.","intents":["Generate React/Vue components from UI mockups or screenshots","Extract code structure and architecture from hand-drawn diagrams or whiteboard photos","Document existing code by analyzing screenshots of the codebase","Convert design system specifications (visual) into code templates"],"best_for":["design-to-code workflows in frontend development teams","developers documenting legacy systems from screenshots","teams prototyping UI components from designer mockups"],"limitations":["Vision accuracy degrades on low-resolution images (<200px width) or heavily compressed screenshots","Cannot extract code from images with syntax highlighting or non-standard fonts reliably","No OCR optimization for handwritten code — printed text only","Image processing adds 500ms-2s latency per request"],"requires":["Anthropic API key with vision model access","Image input in JPEG, PNG, GIF, or WebP format","Maximum image size of 20MB per request"],"input_types":["image (screenshots, mockups, diagrams, photos)","text (prompts describing what to extract or generate)"],"output_types":["code (HTML, CSS, JavaScript, React, Vue, etc.)","structured descriptions of architecture or UI layout","documentation with visual references"],"categories":["image-visual","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-opus-4.6__cap_3","uri":"capability://data.processing.analysis.structured.data.extraction.with.schema.validation","name":"structured data extraction with schema validation","description":"Claude Opus 4.6 can extract structured data from unstructured text or images using JSON schema constraints, with built-in validation that ensures outputs conform to specified schemas. The model uses constrained decoding (token-level filtering) to enforce schema compliance, preventing invalid JSON or missing required fields. This enables reliable data extraction pipelines where the model output can be directly consumed by downstream systems without post-processing validation.","intents":["Extract entities, relationships, and metadata from documents or web content into structured formats","Parse semi-structured data (emails, logs, PDFs) into normalized database records","Generate API responses that conform to OpenAPI schemas","Build data pipelines where LLM output feeds directly into databases or APIs"],"best_for":["data engineering teams building LLM-powered ETL pipelines","developers building APIs that use LLMs for content understanding","teams automating document processing workflows"],"limitations":["Schema validation adds 5-15% latency overhead due to constrained decoding","Very large schemas (>500 fields) may cause token overhead and slower generation","Constrained decoding can force the model to truncate or omit information to fit schema","No support for recursive or deeply nested schemas (>10 levels)"],"requires":["Anthropic API key","JSON schema definition for the target structure","Input text or image containing data to extract"],"input_types":["text (documents, emails, logs, web content)","image (scanned documents, screenshots, PDFs)"],"output_types":["JSON (validated against provided schema)","structured records ready for database insertion"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-opus-4.6__cap_4","uri":"capability://tool.use.integration.tool.use.with.multi.provider.function.calling","name":"tool use with multi-provider function calling","description":"Claude Opus 4.6 supports function calling via a standardized schema-based interface that can route to multiple tool providers (APIs, local functions, MCP servers). The model generates structured tool calls with arguments, and the system handles invocation, error handling, and result feeding back into the conversation. This enables agents to orchestrate external tools, APIs, and services as part of their reasoning loop.","intents":["Build agents that can call APIs, databases, or local functions to gather information","Create multi-step workflows where the model decides which tools to call and in what order","Implement agents that can recover from tool failures by retrying or using alternative tools","Develop systems where the model can reason about tool capabilities and choose the best one"],"best_for":["teams building autonomous agents with external tool access","developers implementing AI-powered automation workflows","organizations deploying agents that need to interact with multiple APIs or services"],"limitations":["Tool calling adds 200-500ms latency per decision point due to model inference","No built-in retry logic — agents must implement their own error handling","Tool schemas must be manually defined — no automatic schema generation from APIs","Parallel tool calling is supported but sequential execution may be required for dependent tools"],"requires":["Anthropic API key","Tool definitions in JSON schema format","Implementation of tool execution handlers (functions or API endpoints)","Error handling and result formatting logic"],"input_types":["natural language task descriptions","tool definitions (JSON schema)","previous tool results (for multi-turn tool use)"],"output_types":["tool calls (structured function invocations)","final responses incorporating tool results"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-opus-4.6__cap_5","uri":"capability://text.generation.language.conversational.context.management.with.memory","name":"conversational context management with memory","description":"Claude Opus 4.6 maintains conversation history across multiple turns, with support for system prompts that define agent behavior and constraints. The model uses attention mechanisms to weight recent context more heavily while still considering earlier conversation turns for consistency. This enables multi-turn interactions where the model can reference previous statements, build on prior reasoning, and maintain a coherent persona or role.","intents":["Build chatbots or assistants that maintain context across extended conversations","Implement agents that can reference previous decisions and reasoning","Create systems where the model's behavior is constrained by system prompts","Develop interactive debugging or code review workflows with multi-turn feedback"],"best_for":["developers building conversational AI systems","teams implementing multi-turn agent workflows","organizations deploying customer-facing chatbots or assistants"],"limitations":["Context window is shared between input and output — long conversations reduce space for new queries","No automatic memory summarization — very long conversations (>50 turns) may lose early context","System prompts are included in token count, reducing available context for conversation history","No persistent memory across sessions — each conversation starts fresh"],"requires":["Anthropic API key","Conversation history management (client-side or server-side)","System prompt definition for agent behavior"],"input_types":["text (user messages)","conversation history (previous turns)","system prompts (behavior constraints)"],"output_types":["text (assistant responses)","structured outputs (if requested in system prompt)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-opus-4.6__cap_6","uri":"capability://text.generation.language.instruction.following.with.complex.constraints","name":"instruction-following with complex constraints","description":"Claude Opus 4.6 is trained to follow detailed, multi-part instructions with complex constraints and edge cases. The model can parse instructions that specify output format, tone, constraints, and conditional logic, then apply them consistently across generations. This is achieved through RLHF training on instruction-following tasks with varying complexity and ambiguity.","intents":["Generate outputs that conform to complex formatting requirements (e.g., specific code style, documentation format)","Implement systems where the model must follow conditional logic (e.g., 'if X then do Y, else do Z')","Create agents that respect hard constraints (e.g., 'never use deprecated APIs', 'always include error handling')","Build systems where the model must balance multiple competing objectives (e.g., performance vs readability)"],"best_for":["teams building code generation systems with strict style requirements","developers implementing AI-powered linters or code reviewers","organizations deploying agents with complex business logic constraints"],"limitations":["Very complex instructions (>500 words) may be misinterpreted or partially ignored","Conflicting constraints may cause the model to choose one over another without warning","Edge cases not covered in training may not be handled correctly","No guarantee that constraints are followed 100% — requires validation"],"requires":["Anthropic API key","Well-written instructions that are clear and unambiguous","Validation logic to check constraint compliance"],"input_types":["text (instructions with constraints)","context (code, documents, or data to process)"],"output_types":["text (following specified format and constraints)","code (adhering to style and constraint requirements)"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-opus-4.6__cap_7","uri":"capability://code.generation.editing.code.review.and.analysis.with.architectural.understanding","name":"code review and analysis with architectural understanding","description":"Claude Opus 4.6 can analyze code for bugs, security issues, performance problems, and architectural concerns by understanding code structure, dependencies, and design patterns. The model uses its long context window to analyze entire files or modules at once, identifying issues that require understanding multiple functions or classes. It can provide specific recommendations with explanations of why changes are needed.","intents":["Automate code review by analyzing pull requests for bugs and style issues","Identify security vulnerabilities in code (SQL injection, XSS, etc.)","Detect performance problems and suggest optimizations","Analyze architectural issues and suggest refactoring"],"best_for":["development teams automating code review workflows","security teams scanning code for vulnerabilities","developers refactoring large codebases"],"limitations":["Analysis quality depends on code clarity — obfuscated or poorly documented code may be misanalyzed","Cannot detect issues that require runtime context (e.g., race conditions in concurrent code)","May produce false positives on legitimate patterns that look suspicious","No access to external dependencies — cannot check if imported libraries have vulnerabilities"],"requires":["Anthropic API key","Code to analyze (as text or in context window)","Optional: language specification for syntax highlighting"],"input_types":["code (any programming language)","code diffs (for pull request review)"],"output_types":["analysis report (issues found, severity, recommendations)","annotated code (with comments explaining issues)"],"categories":["code-generation-editing","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-opus-4.6__cap_8","uri":"capability://code.generation.editing.natural.language.to.sql.translation.with.schema.awareness","name":"natural language to sql translation with schema awareness","description":"Claude Opus 4.6 can convert natural language queries into SQL statements by understanding database schema, table relationships, and query semantics. The model uses the schema definition (provided in context) to generate syntactically correct SQL that matches the user's intent. This enables non-technical users to query databases using natural language, or developers to quickly generate complex queries.","intents":["Enable non-technical users to query databases using natural language","Automatically generate SQL from business requirements or questions","Translate complex analytical queries from natural language to SQL","Generate SQL for data exploration and analysis tasks"],"best_for":["business intelligence teams building natural language query interfaces","developers building database query tools","organizations enabling non-technical users to access data"],"limitations":["Accuracy depends on schema clarity — poorly documented schemas produce incorrect queries","Cannot handle database-specific SQL dialects reliably (e.g., PostgreSQL vs MySQL vs T-SQL)","May generate inefficient queries that require optimization","No access to query execution — cannot validate that generated SQL actually works"],"requires":["Anthropic API key","Database schema definition (provided in context)","Natural language query from user"],"input_types":["text (natural language query)","schema definition (SQL DDL or structured description)"],"output_types":["SQL (SELECT, INSERT, UPDATE, DELETE statements)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthropic-claude-opus-4.6__cap_9","uri":"capability://text.generation.language.technical.documentation.generation.from.code","name":"technical documentation generation from code","description":"Claude Opus 4.6 can analyze code and generate comprehensive technical documentation including API documentation, architecture guides, and usage examples. The model understands code structure, function signatures, and design patterns, then generates documentation that explains what the code does, how to use it, and why it was designed that way. This capability works across the long context window to document entire modules or projects.","intents":["Automatically generate API documentation from code","Create architecture guides explaining how components interact","Generate usage examples and tutorials from code","Document legacy code that lacks documentation"],"best_for":["development teams automating documentation generation","open source projects maintaining documentation","organizations documenting legacy systems"],"limitations":["Documentation quality depends on code clarity — poorly written code produces poor documentation","May miss important context that exists only in developer's mind","Generated examples may not cover all use cases","Requires manual review to ensure accuracy and completeness"],"requires":["Anthropic API key","Code to document (as text in context window)"],"input_types":["code (any programming language)","optional: existing documentation fragments to build upon"],"output_types":["markdown (API docs, guides, tutorials)","HTML (formatted documentation)"],"categories":["text-generation-language","code-generation-editing"],"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 capable of handling streaming responses","Minimum 30KB request payload capacity for typical code contexts","Anthropic API key with access to extended thinking features","Ability to handle streaming responses with multiple reasoning segments","Timeout configuration of 60+ seconds for complex reasoning tasks","Anthropic API key","Code to test (as text in context window)","Test framework specification (Jest, pytest, etc.)","Acceptance of usage policies"],"failure_modes":["200K token limit still requires careful context selection for very large codebases (>1M LOC)","Long context processing adds latency (~2-5 seconds per request) compared to shorter-context models","Attention mechanisms scale quadratically, making extremely long contexts (>150K tokens) slower than shorter ones","No built-in caching of parsed ASTs — each request re-processes the full context","Extended reasoning increases latency by 3-10x compared to direct generation","Reasoning tokens are billed at the same rate as output tokens, increasing cost for complex tasks","No guaranteed determinism — same input may produce different reasoning paths on different calls","Reasoning quality degrades on tasks outside the model's training distribution (e.g., novel domains)","Generated tests may not cover all edge cases or business logic requirements","Tests require manual review to ensure they test the right things","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.5,"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-4.6","compare_url":"https://unfragile.ai/compare?artifact=anthropic-claude-opus-4.6"}},"signature":"ndg1BRHrboxlBQ5dBs/OLgZE94iIbmlPeigAiFblQR+5Iuvvpi0vg/IWOYGt5j57a42GChWYMp5BMcQFU8ibDg==","signedAt":"2026-06-19T14:18:36.849Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/anthropic-claude-opus-4.6","artifact":"https://unfragile.ai/anthropic-claude-opus-4.6","verify":"https://unfragile.ai/api/v1/verify?slug=anthropic-claude-opus-4.6","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"}}