{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"claude-sonnet-4","slug":"claude-sonnet-4","name":"Claude Sonnet 4","type":"model","url":"https://www.anthropic.com/claude/sonnet","page_url":"https://unfragile.ai/claude-sonnet-4","categories":["llm-apis","deployment-infra"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"claude-sonnet-4__cap_0","uri":"capability://planning.reasoning.extended.thinking.with.user.controlled.reasoning.effort","name":"extended thinking with user-controlled reasoning effort","description":"Claude Sonnet 4.6 implements a hybrid reasoning architecture where users can explicitly trigger extended thinking mode to enable step-by-step problem decomposition before generating responses. The model performs internal chain-of-thought reasoning (hidden from users) and can be configured with fine-grained thinking effort levels via API parameters, trading off latency and cost for reasoning depth. This differs from standard token-by-token generation by allocating compute budget to pre-response deliberation rather than streaming output.","intents":["I need the model to work through complex multi-step problems before answering","I want to control how much reasoning effort the model applies to different queries","I need to understand the model's reasoning process for debugging or validation","I'm solving problems that benefit from explicit step-by-step decomposition"],"best_for":["Teams building reasoning-heavy agents for finance, cybersecurity, or research","Developers optimizing cost-to-quality tradeoffs in production systems","Solo developers prototyping complex problem-solving workflows"],"limitations":["Extended thinking increases latency and token costs (specific overhead not documented by Anthropic)","Thinking tokens are billed at same rate as output tokens ($15/M for Sonnet 4.6), making reasoning-heavy queries expensive at scale","Adaptive thinking mode (automatic) behavior is opaque — users cannot inspect or control when/how it activates","No fine-tuning available to customize reasoning patterns for domain-specific problems"],"requires":["Claude API access with Sonnet 4.6 model","API parameter support for thinking_type and budget_tokens (varies by SDK)","Understanding of token counting to estimate reasoning costs"],"input_types":["text","code","structured prompts with reasoning requirements"],"output_types":["text with reasoning steps (if thinking_type='extended')","text without visible reasoning (if thinking_type='adaptive' or disabled)"],"categories":["planning-reasoning","cost-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"claude-sonnet-4__cap_1","uri":"capability://code.generation.editing.codebase.aware.code.generation.and.multi.file.refactoring","name":"codebase-aware code generation and multi-file refactoring","description":"Claude Sonnet 4.6 achieves 'frontier coding performance' through transformer-based understanding of code structure, context, and intent across multiple files. The model can analyze entire codebases (up to 1M context window in beta), generate code that respects existing patterns and dependencies, and perform refactoring operations that maintain semantic correctness. Implementation leverages the full context window to maintain awareness of imports, type definitions, and architectural constraints without requiring explicit AST parsing or language-specific plugins.","intents":["I need to generate code that fits seamlessly into my existing codebase","I want to refactor multiple files while maintaining type safety and dependencies","I need code review feedback that understands my project's architecture and conventions","I'm debugging issues that span multiple files and need context-aware suggestions"],"best_for":["Full-stack developers building production applications","Teams migrating codebases or performing large-scale refactoring","Solo developers working on complex multi-module projects","Engineering teams using Claude for code review automation"],"limitations":["1M context window (Sonnet 4.6 beta on API only) required for true codebase-scale analysis; earlier versions limited to 200K tokens","No built-in IDE integration — requires manual context passing or third-party tools (VS Code extensions, GitHub Copilot-style integrations)","Code generation quality depends on prompt clarity and context ordering; no automatic dependency resolution or import management","No persistent codebase indexing — each request must re-pass relevant context, increasing latency and cost for large projects"],"requires":["Claude API access with Sonnet 4.6 model for 1M context window","Ability to pass code files as text input (via API or web interface)","Understanding of token counting to estimate context usage for large codebases","Optional: IDE extension or custom tooling for seamless context passing"],"input_types":["code (Python, JavaScript, TypeScript, Go, Java, C++, Rust, etc.)","code comments and docstrings","architecture documentation or design patterns","error messages and stack traces"],"output_types":["generated code (same language as input)","refactored code with explanations","code review comments with specific line references","structured output (JSON) with code suggestions and reasoning"],"categories":["code-generation-editing","developer-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"claude-sonnet-4__cap_10","uri":"capability://automation.workflow.managed.agents.with.stateful.sessions.and.persistent.memory","name":"managed agents with stateful sessions and persistent memory","description":"Claude Sonnet 4.6 offers Claude Managed Agents, a separate infrastructure from the standard Messages API that provides fully managed agent hosting with stateful sessions and persistent event history. Developers define agent behavior via a configuration file (tools, instructions, model), and Anthropic manages session state, tool invocation, and error handling. This differs from the Messages API by providing built-in session management and persistent memory without requiring developers to implement state management logic.","intents":["I need to deploy long-running agents without managing session state myself","I want agents to remember context across multiple user interactions","I need a fully managed agent infrastructure without custom orchestration","I'm building customer support or research agents that maintain conversation history"],"best_for":["Teams deploying production agents without custom orchestration","Non-technical users building agents via configuration files","Developers prioritizing simplicity over customization","Organizations needing managed infrastructure for agent hosting"],"limitations":["Managed agents are in beta — API surface and behavior may change","Limited customization compared to Messages API — developers cannot override core agent behavior","Pricing model for managed agents not clearly documented — cost implications unclear","No explicit control over session persistence duration or memory limits","Vendor lock-in — agents are tightly coupled to Anthropic's infrastructure"],"requires":["Claude API access with Managed Agents enabled (beta)","Agent configuration file (YAML or JSON format)","Tool definitions compatible with managed agent format","Understanding of agent lifecycle and session management"],"input_types":["user messages (text input to agent)","agent configuration (tools, instructions, model selection)"],"output_types":["agent responses (text)","tool invocations and results","session state and event history"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"claude-sonnet-4__cap_11","uri":"capability://text.generation.language.multilingual.understanding.and.translation","name":"multilingual understanding and translation","description":"Claude Sonnet 4.6 supports understanding and generation in multiple languages, enabling translation, multilingual content analysis, and cross-language reasoning. The model can process input in one language and generate output in another, or analyze multilingual documents and extract information across language boundaries. Implementation leverages the transformer's multilingual training to handle language mixing and code-switching without explicit language detection or separate translation models.","intents":["I need to translate content between languages","I want to analyze documents in multiple languages simultaneously","I need to build chatbots that support multiple languages","I'm extracting information from multilingual datasets"],"best_for":["Global teams building multilingual applications","Content platforms supporting multiple languages","Developers building translation or localization tools","Data teams processing multilingual datasets"],"limitations":["Translation quality varies by language pair — less common languages may have lower quality","No explicit language detection — model infers language from context","Cultural nuances and idioms may not translate perfectly — requires human review for sensitive content","Code-switching (mixing languages) may confuse the model in some cases","Specific supported languages not documented — unclear which languages are well-supported"],"requires":["Claude API access with Sonnet 4.6 model","Input in supported language","Understanding that translation quality varies by language pair"],"input_types":["text (in any supported language)","code (with comments in multiple languages)","mixed language content"],"output_types":["text (translated or analyzed in target language)","multilingual analysis (insights across languages)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"claude-sonnet-4__cap_12","uri":"capability://safety.moderation.safety.guardrails.and.content.moderation","name":"safety guardrails and content moderation","description":"Claude Sonnet 4.6 includes built-in safety features to reduce harmful outputs, including guardrails for hallucination reduction, jailbreak mitigation, and content filtering. These are implemented at the model level (training-time alignment) and optionally at the API level (request-time filtering). Developers can configure safety settings per-request, and Anthropic provides documentation on responsible use patterns. The model refuses harmful requests and explains why, rather than generating harmful content.","intents":["I need to ensure my application doesn't generate harmful or illegal content","I want to reduce hallucinations in factual outputs","I need to prevent users from jailbreaking the model","I'm building applications for regulated industries (healthcare, finance) with compliance requirements"],"best_for":["Teams building customer-facing applications with safety requirements","Developers in regulated industries (healthcare, finance, legal)","Organizations with content moderation policies","Solo developers prioritizing responsible AI use"],"limitations":["Safety guardrails are probabilistic — determined users can still elicit harmful outputs through prompt engineering","Hallucination reduction is not perfect — factual errors still occur, especially on niche topics","No explicit control over safety level — developers cannot adjust strictness of guardrails","Safety behavior may be inconsistent across similar requests — no guarantee of deterministic refusal","Documentation on specific safety mechanisms is limited — unclear exactly how guardrails work"],"requires":["Claude API access with Sonnet 4.6 model","Understanding of responsible AI practices","Optional: custom safety policies or content filtering logic"],"input_types":["any text input (model evaluates for safety)"],"output_types":["text response (filtered for safety)","refusal message (if request violates safety policy)"],"categories":["safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"claude-sonnet-4__cap_13","uri":"capability://memory.knowledge.context.editing.and.conversation.management","name":"context editing and conversation management","description":"Claude Sonnet 4.6 supports context editing capabilities that allow developers to modify conversation history, remove messages, or adjust context mid-conversation without restarting. This is implemented via API parameters that allow selective message deletion or replacement, enabling dynamic conversation management. Developers can use context editing to remove sensitive information, correct errors, or optimize token usage by removing less relevant messages.","intents":["I need to remove sensitive information from conversation history","I want to correct mistakes in previous messages without restarting the conversation","I need to optimize token usage by removing less relevant context","I'm building applications where users can edit or delete previous messages"],"best_for":["Teams building privacy-focused applications","Developers optimizing token usage in long conversations","Applications with user-controlled message editing","Systems requiring conversation history cleanup"],"limitations":["Context editing API surface not fully documented — unclear which editing operations are supported","Editing may affect model's reasoning if context is interdependent — removing messages may cause inconsistencies","No automatic consistency checking — developers must ensure edited context remains coherent","Editing overhead not documented — unclear if editing adds latency","Limited to message-level editing — cannot edit individual tokens or partial messages"],"requires":["Claude API access with Sonnet 4.6 model","API client library supporting context editing parameters","Understanding of conversation structure and dependencies"],"input_types":["conversation history (messages to edit)","edit operations (delete, replace, reorder)"],"output_types":["modified conversation history","model response based on edited context"],"categories":["memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"claude-sonnet-4__cap_14","uri":"capability://data.processing.analysis.token.counting.and.cost.estimation","name":"token counting and cost estimation","description":"Claude Sonnet 4.6 provides a token counting API that allows developers to estimate costs before making API requests. The count_tokens endpoint accepts text, images, and tool definitions and returns the exact token count that would be billed. This enables budget forecasting, cost optimization, and request planning without making actual API calls. Token counting is implemented as a separate, low-cost API endpoint (typically free or minimal cost).","intents":["I need to estimate costs before making API requests","I want to optimize prompts to reduce token usage","I need to budget for large-scale API usage","I'm building applications that need to warn users about costs"],"best_for":["Teams managing API budgets and cost optimization","Developers building cost-aware applications","Data engineering teams processing large datasets","Solo developers prototyping with cost constraints"],"limitations":["Token counting is approximate for images — actual tokens may vary based on image compression","Cached tokens are counted differently — token count API may not reflect cache savings","Tool definitions add tokens — developers must account for tool overhead in estimates","Token counting latency not documented — unclear if counting adds significant overhead"],"requires":["Claude API access with token counting enabled","API client library supporting count_tokens endpoint","Understanding of token pricing for different model versions"],"input_types":["text (to count tokens)","images (to estimate image token cost)","tool definitions (to estimate tool overhead)"],"output_types":["token count (integer)","cost estimate (based on current pricing)"],"categories":["data-processing-analysis","cost-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"claude-sonnet-4__cap_2","uri":"capability://tool.use.integration.computer.use.and.gui.automation.via.visual.understanding","name":"computer use and gui automation via visual understanding","description":"Claude Sonnet 4.6 can analyze screenshots and execute browser/desktop automation tasks by understanding visual layouts, identifying UI elements, and generating appropriate actions (clicks, text input, navigation). The model receives image input of the current screen state, reasons about the task, and outputs structured commands (via built-in computer-use tool) to interact with the GUI. This enables autonomous task execution in digital environments without requiring explicit element selectors or DOM access.","intents":["I need to automate repetitive browser tasks like form filling or data entry","I want to build agents that can navigate websites and extract information","I need to test web applications by simulating user interactions","I'm automating desktop workflows that require visual understanding of UI state"],"best_for":["Teams building RPA (robotic process automation) solutions","QA engineers automating browser-based testing workflows","Developers prototyping autonomous agents for web scraping or data collection","Non-technical users building no-code automation workflows via Claude.ai"],"limitations":["Computer use tool is in beta — behavior and API surface may change","Requires explicit screenshot input; no built-in screen capture — integration with automation frameworks (Selenium, Playwright) needed for production use","Visual understanding is probabilistic — model may misidentify UI elements or fail on complex/custom UI designs","No persistent session state — each request requires fresh screenshot, increasing latency for multi-step workflows","Rate limits and execution timeouts not documented; unclear maximum task complexity or duration"],"requires":["Claude API access with Sonnet 4.6 model","Ability to capture and pass screenshots as image input (PNG/JPEG)","Integration with browser automation framework (Selenium, Playwright, Puppeteer) or desktop automation tool","Understanding of task decomposition to break complex workflows into screenshot-action cycles"],"input_types":["image (screenshot of current screen state)","text (task description or instructions)","structured context (previous actions, task history)"],"output_types":["structured actions (click, type, scroll, navigate)","extracted data from UI (text, form values)","task completion status and reasoning"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"claude-sonnet-4__cap_3","uri":"capability://search.retrieval.integrated.web.search.and.real.time.information.retrieval","name":"integrated web search and real-time information retrieval","description":"Claude Sonnet 4.6 includes a built-in web search tool that allows the model to query the internet and retrieve current information during conversation. When enabled, the model can autonomously decide when to search, fetch web content, and synthesize results into responses. This is implemented as a native tool in the Messages API (alongside code execution and computer use), allowing developers to enable/disable web search per-request without additional API calls or external search service integration.","intents":["I need answers about current events, recent news, or time-sensitive information","I want the model to verify claims against real-time data","I'm building a research assistant that needs to cite sources","I need up-to-date pricing, availability, or product information"],"best_for":["Teams building research or journalism tools","Developers creating customer support chatbots with real-time information","Solo developers prototyping fact-checking or verification workflows","Non-technical users using Claude.ai for current events research"],"limitations":["Web search quality depends on query formulation — model may not search for optimal keywords or may miss relevant sources","Search results are not deterministic — same query may return different results over time, affecting reproducibility","No control over search depth, number of results, or source prioritization — model autonomously decides search strategy","Latency overhead: web search adds network round-trip time (typically 2-5 seconds per search)","No built-in source attribution or citation formatting — developers must implement custom parsing of search results"],"requires":["Claude API access with Sonnet 4.6 model","Enable web search tool via API parameter (tool_choice or tools array)","Internet connectivity for API requests (search happens server-side)","Optional: custom parsing logic to extract and format citations from search results"],"input_types":["text (user query or conversation context)","implicit trigger (model decides when to search based on query intent)"],"output_types":["text response with integrated search results","structured output with citations (if custom parsing implemented)","search queries executed (visible in API response metadata)"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"claude-sonnet-4__cap_4","uri":"capability://data.processing.analysis.structured.output.generation.with.schema.enforcement","name":"structured output generation with schema enforcement","description":"Claude Sonnet 4.6 supports structured output mode where developers define a JSON schema and the model is constrained to generate responses matching that schema exactly. This is implemented via the Messages API's response_format parameter with json_schema specification, ensuring outputs are valid JSON that can be directly parsed and used in downstream systems without additional validation or parsing logic. The model's token generation is constrained to only produce valid schema-compliant outputs.","intents":["I need to extract structured data from unstructured text reliably","I want to generate JSON that integrates directly into my application without parsing errors","I need to enforce specific fields and types in model outputs for downstream processing","I'm building data pipelines where output format must be deterministic"],"best_for":["Data engineering teams building ETL pipelines with LLM-powered extraction","Developers building APIs that use Claude for structured data generation","Teams automating form filling or document generation with guaranteed format","Solo developers prototyping data extraction workflows"],"limitations":["Schema must be defined upfront — no dynamic schema generation based on input","Complex nested schemas may increase latency due to constraint checking overhead (specific impact not documented)","Schema enforcement may reduce model creativity or force suboptimal outputs if schema is too restrictive","No built-in schema validation or error handling — invalid schemas cause API errors","Streaming responses with structured output have limited support (may not be available for all schema types)"],"requires":["Claude API access with Sonnet 4.6 model","JSON schema definition (JSON Schema format)","API client library supporting response_format parameter (Python, TypeScript, etc.)","Understanding of JSON Schema syntax and constraints"],"input_types":["text (unstructured data to extract from)","code (for code analysis and transformation)","images (for visual data extraction)"],"output_types":["JSON (guaranteed to match provided schema)","structured data (arrays, objects, nested structures)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"claude-sonnet-4__cap_5","uri":"capability://memory.knowledge.prompt.caching.for.cost.reduction.on.repeated.context","name":"prompt caching for cost reduction on repeated context","description":"Claude Sonnet 4.6 implements prompt caching where frequently-used context (system prompts, documents, code files, instructions) is cached server-side after the first request. Subsequent requests reusing the same cached context are charged at 90% discount on cached tokens (input tokens cost $0.30/M instead of $3/M for Sonnet 4.6). Caching is transparent to developers — any repeated input tokens are automatically cached and reused without explicit cache management.","intents":["I need to reduce API costs when processing multiple queries against the same large document or codebase","I'm building a chatbot with a fixed system prompt and want to avoid re-processing it","I need to analyze multiple files with the same analysis instructions","I'm running batch processing where context is reused across many requests"],"best_for":["Teams processing high-volume queries with repeated context (customer support, document analysis)","Developers building multi-turn conversations with large system prompts","Data engineering teams running batch analysis jobs with fixed instructions","Cost-sensitive applications where context reuse is common"],"limitations":["Cache hits require exact token-for-token match of input — any variation breaks cache (e.g., different whitespace, punctuation)","Minimum cache size is 1,024 tokens — small prompts don't benefit from caching","Cache TTL is 5 minutes — cached context expires if not reused within window, requiring re-processing","No explicit cache management API — developers cannot manually invalidate or inspect cache state","Caching overhead adds ~25ms latency to first request (cache write), reducing benefit for one-off queries"],"requires":["Claude API access with Sonnet 4.6 model","Repeated requests with identical input context (system prompt, documents, instructions)","Understanding of token counting to identify cacheable context","API client library supporting cache_control parameter (Python, TypeScript, etc.)"],"input_types":["text (system prompts, instructions, documents)","code (for repeated code analysis)","images (for repeated image analysis)"],"output_types":["text responses (same as non-cached requests)","API metadata indicating cache hit/miss and cost savings"],"categories":["memory-knowledge","cost-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"claude-sonnet-4__cap_6","uri":"capability://automation.workflow.batch.processing.api.for.cost.optimization.at.scale","name":"batch processing api for cost optimization at scale","description":"Claude Sonnet 4.6 offers a Batch API that processes multiple requests asynchronously in a single batch, providing up to 50% cost reduction compared to standard API calls. Developers submit a batch of requests (JSONL format) and receive results after processing completes (typically within 24 hours). Batch processing is implemented as a separate API endpoint with different pricing and SLA, allowing developers to trade latency for cost on non-urgent workloads.","intents":["I need to process thousands of documents or queries but don't need real-time responses","I want to reduce API costs for large-scale analysis or data processing jobs","I'm running nightly batch jobs for content generation or data extraction","I need to analyze a large dataset with consistent prompts"],"best_for":["Data engineering teams processing large datasets","Content generation platforms with non-real-time requirements","Research teams analyzing document collections","Cost-sensitive applications where latency is not critical"],"limitations":["Asynchronous processing with 24-hour typical completion time — not suitable for real-time applications","Batch requests must be submitted in JSONL format — requires custom tooling or SDK support for batch formatting","No streaming responses in batch mode — full response buffered before return","Limited error handling and retry logic — failed requests in batch require resubmission","No real-time progress tracking — developers must poll for batch completion status"],"requires":["Claude API access with Batch API enabled","Ability to format requests as JSONL (newline-delimited JSON)","Tolerance for 24-hour latency (typical completion time)","API client library supporting batch submission (Python, TypeScript, etc.)"],"input_types":["text (queries, documents, instructions)","code (for batch code analysis)","images (for batch image analysis)"],"output_types":["text responses (same as standard API)","batch results file (JSONL format)","batch status and metadata"],"categories":["automation-workflow","cost-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"claude-sonnet-4__cap_7","uri":"capability://image.visual.vision.understanding.and.image.analysis","name":"vision understanding and image analysis","description":"Claude Sonnet 4.6 can analyze images (PNG, JPEG, GIF, WebP formats) to extract information, answer questions about visual content, and perform OCR-like text extraction. The model receives image input as base64-encoded data or URLs and generates text descriptions, answers to visual questions, or structured data extracted from images. Vision capability is integrated into the standard Messages API without separate endpoints, allowing seamless mixing of text and image inputs in conversations.","intents":["I need to extract text from screenshots, documents, or photos","I want to analyze charts, diagrams, or visual data","I need to describe images or answer questions about visual content","I'm building a document processing pipeline that handles scanned PDFs or images"],"best_for":["Teams building document processing or OCR applications","Developers creating visual search or image analysis tools","Data extraction teams processing scanned documents or screenshots","Solo developers prototyping image understanding workflows"],"limitations":["Image quality and resolution affect accuracy — low-quality or small text may not be extracted reliably","No built-in document layout understanding — complex multi-column documents may have text extraction errors","Image input is billed as tokens (approximately 170 tokens per image, plus additional tokens for high-resolution images)","No batch image processing — each image requires separate API request","Vision performance on specialized domains (medical imaging, technical diagrams) not documented"],"requires":["Claude API access with Sonnet 4.6 model","Images in supported formats (PNG, JPEG, GIF, WebP)","Base64 encoding or URL access to images","Understanding of image token costs for budget estimation"],"input_types":["image (PNG, JPEG, GIF, WebP)","text (questions or instructions about images)","mixed (text and images in same request)"],"output_types":["text (image descriptions, answers, extracted text)","structured data (JSON with extracted information)","code (generated from visual diagrams or screenshots)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"claude-sonnet-4__cap_8","uri":"capability://code.generation.editing.code.execution.and.sandbox.environment","name":"code execution and sandbox environment","description":"Claude Sonnet 4.6 can execute Python code in a sandboxed environment, allowing the model to run scripts, perform calculations, and validate outputs. Code execution is implemented as a built-in tool in the Messages API (alongside web search and computer use), enabling the model to autonomously write and run code to solve problems or verify results. The sandbox provides a Python runtime with common libraries (NumPy, Pandas, Matplotlib, etc.) and file system access within the sandbox.","intents":["I need the model to execute code to verify solutions or perform calculations","I want to use Claude for data analysis with live code execution","I'm building a coding assistant that can test generated code","I need to visualize data or generate plots as part of problem-solving"],"best_for":["Data scientists using Claude for exploratory analysis","Developers building coding assistants with code validation","Teams using Claude for mathematical problem-solving","Solo developers prototyping data analysis workflows"],"limitations":["Sandbox is ephemeral — no persistent state between requests (each request gets fresh Python environment)","Limited library availability — only pre-installed libraries available (NumPy, Pandas, Matplotlib, etc.); no pip install","Execution timeout not documented — unclear maximum runtime for code execution","No network access from sandbox — cannot make external API calls or fetch remote data","File system access limited to sandbox directory — cannot access user's local files"],"requires":["Claude API access with Sonnet 4.6 model","Enable code execution tool via API parameter","Python knowledge to understand generated code","Tolerance for sandbox limitations (no persistence, no external network)"],"input_types":["text (problem description or data analysis request)","code (for code review or execution)","data (as text or structured format for analysis)"],"output_types":["code (generated and executed)","execution results (stdout, stderr, plots)","visualizations (PNG images from Matplotlib)"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"claude-sonnet-4__cap_9","uri":"capability://tool.use.integration.parallel.tool.use.and.multi.step.task.execution","name":"parallel tool use and multi-step task execution","description":"Claude Sonnet 4.6 supports parallel tool use where the model can invoke multiple tools (web search, code execution, computer use, etc.) simultaneously in a single response, rather than sequentially. This is implemented via the Messages API's tool_use capability, allowing the model to parallelize independent operations (e.g., search multiple queries, execute multiple code blocks) and combine results. Developers can also enable strict tool use mode to enforce that responses only contain tool calls without additional text.","intents":["I need to parallelize independent operations to reduce total latency","I want to build agents that can execute multiple tasks in a single step","I need to enforce that the model uses specific tools without generating free-form text","I'm building workflows where multiple data sources must be queried simultaneously"],"best_for":["Teams building agentic systems with complex multi-step workflows","Developers optimizing latency in tool-heavy applications","Builders of autonomous agents that coordinate multiple services","Data engineering teams parallelizing data collection tasks"],"limitations":["Parallel execution latency depends on slowest tool — no speedup if tools have sequential dependencies","Tool results are returned in sequence, not parallel — model must wait for all results before continuing","Strict tool use mode prevents free-form reasoning — model cannot explain decisions or provide context outside tool calls","No explicit control over parallelization — model autonomously decides which tools to parallelize","Tool result ordering may affect model's reasoning — no guarantee of deterministic result ordering"],"requires":["Claude API access with Sonnet 4.6 model","Multiple tools defined (web search, code execution, computer use, custom tools, etc.)","API client library supporting parallel tool invocation","Understanding of tool dependencies to avoid sequential bottlenecks"],"input_types":["text (task description with multiple independent subtasks)","structured context (tool definitions and capabilities)"],"output_types":["multiple tool calls (executed in parallel)","combined results from all tools","structured response with tool outputs"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"claude-sonnet-4__headline","uri":"capability://text.generation.language.balanced.llm.for.production.applications","name":"balanced llm for production applications","description":"Claude Sonnet 4 is an advanced language model designed for production use, offering excellent reasoning, coding capabilities, and instruction following at a moderate cost and latency, making it a top choice for developers seeking a reliable AI solution.","intents":["best LLM for production","LLM for coding tasks","best AI model for reasoning","affordable AI model for businesses","high-performance LLM for multi-step reasoning"],"best_for":["production applications","coding and debugging","multi-step reasoning tasks"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":56,"verified":false,"data_access_risk":"high","permissions":["Claude API access with Sonnet 4.6 model","API parameter support for thinking_type and budget_tokens (varies by SDK)","Understanding of token counting to estimate reasoning costs","Claude API access with Sonnet 4.6 model for 1M context window","Ability to pass code files as text input (via API or web interface)","Understanding of token counting to estimate context usage for large codebases","Optional: IDE extension or custom tooling for seamless context passing","Claude API access with Managed Agents enabled (beta)","Agent configuration file (YAML or JSON format)","Tool definitions compatible with managed agent format"],"failure_modes":["Extended thinking increases latency and token costs (specific overhead not documented by Anthropic)","Thinking tokens are billed at same rate as output tokens ($15/M for Sonnet 4.6), making reasoning-heavy queries expensive at scale","Adaptive thinking mode (automatic) behavior is opaque — users cannot inspect or control when/how it activates","No fine-tuning available to customize reasoning patterns for domain-specific problems","1M context window (Sonnet 4.6 beta on API only) required for true codebase-scale analysis; 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