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Supports up to 128K tokens of context (approximately 96K input + 32K output), enabling analysis of entire documents, codebases, or conversation histories in a single request without context truncation or sliding-window approximations.","intents":["I need to analyze a full 50-page technical document and ask follow-up questions without losing context","I want to maintain conversation state across 20+ turns without the model forgetting earlier instructions","I need to process an entire codebase file (up to 50KB) and ask the model to refactor it with consistent style"],"best_for":["developers building document analysis pipelines","teams implementing multi-turn AI assistants for customer support","researchers analyzing long-form text or code repositories"],"limitations":["Latency increases with context length — 128K token requests may take 30-60 seconds vs 2-5 seconds for 4K token requests","Attention computation is O(n²) in sequence length, making extremely long contexts slower than shorter ones","Training data cutoff at December 2023 means no knowledge of events, API changes, or library versions after that date","Rate limited by OpenAI during preview period — may experience 429 errors under high concurrent load"],"requires":["OpenAI API key with GPT-4 Turbo access","HTTP client library (Python requests, Node.js axios, etc.)","Network connectivity to api.openai.com","Account with sufficient API credits (pricing ~$0.01-0.03 per 1K tokens depending on input/output ratio)"],"input_types":["text (natural language prompts)","code (any programming language)","structured text (markdown, JSON, XML)","concatenated documents (plain text, code files)"],"output_types":["text (natural language responses)","code (any programming language)","structured text (markdown, JSON, XML)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4-turbo-preview__cap_1","uri":"capability://data.processing.analysis.json.mode.structured.output.generation","name":"json mode structured output generation","description":"Constrains model output to valid JSON format through post-processing validation and beam search constraints during token generation. When enabled, the model generates only syntactically valid JSON that matches a provided schema, eliminating the need for regex parsing or output repair logic in downstream applications.","intents":["I need to extract structured data from unstructured text and guarantee valid JSON output for database insertion","I want to generate API responses that conform to a specific OpenAPI schema without manual validation","I need to parse natural language into structured form (e.g., convert 'book a flight from NYC to LA on Dec 25' into a JSON booking request)"],"best_for":["backend engineers building LLM-powered APIs with strict schema requirements","data engineers extracting structured information from documents at scale","teams building form-filling or data-entry automation systems"],"limitations":["JSON mode does not validate against a provided schema — it only guarantees syntactically valid JSON, not semantic correctness","Complex nested structures may cause the model to hallucinate or truncate output if the JSON becomes too deeply nested","No support for JSON Schema validation — you must validate the output structure in your application code","Approximately 5-10% performance overhead compared to unconstrained text generation due to constraint checking during decoding"],"requires":["OpenAI API key with GPT-4 Turbo access","HTTP client library supporting streaming or non-streaming responses","JSON parser in your application language (built-in for most languages)","Optional: JSON Schema validator library (e.g., jsonschema for Python, ajv for Node.js)"],"input_types":["text (natural language prompts with extraction instructions)","unstructured data (documents, emails, chat messages)","semi-structured data (HTML, markdown)"],"output_types":["JSON (guaranteed syntactically valid, structure depends on prompt)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4-turbo-preview__cap_2","uri":"capability://tool.use.integration.parallel.function.calling.with.multi.tool.orchestration","name":"parallel function calling with multi-tool orchestration","description":"Enables the model to invoke multiple functions simultaneously in a single response through a structured function-calling protocol. The model generates a list of function calls with arguments, which are executed in parallel by the client, and results are fed back to the model for synthesis — supporting complex workflows that require coordinating multiple APIs or tools.","intents":["I need to fetch user data, check inventory, and calculate shipping costs in parallel, then synthesize results into a single response","I want to build an agent that can call multiple APIs (weather, calendar, email) to answer 'what should I wear tomorrow and do I have time for a walk'","I need to execute multiple database queries or API calls concurrently and have the model reason over the combined results"],"best_for":["developers building AI agents with multi-step workflows","teams implementing autonomous systems that coordinate multiple services","backend engineers creating LLM-powered orchestration layers"],"limitations":["Function calls are generated sequentially by the model, not truly parallel — the model must decide which functions to call before seeing their results","No built-in error handling or retry logic — if a function call fails, you must implement fallback logic and re-prompt the model","Token overhead for function definitions and call results — each function call adds ~50-200 tokens to the context, limiting the number of tools you can register","No support for conditional logic within function calls — the model cannot say 'call function A only if function B returns X'"],"requires":["OpenAI API key with GPT-4 Turbo access","HTTP client library supporting streaming responses","Function definitions in OpenAI's function schema format (JSON with name, description, parameters)","Application code to execute functions and feed results back to the model"],"input_types":["text (natural language prompts)","function schema definitions (JSON)"],"output_types":["function calls (structured list with function name and arguments)","text (final response after function results are processed)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4-turbo-preview__cap_3","uri":"capability://text.generation.language.reproducible.output.generation.with.seed.control","name":"reproducible output generation with seed control","description":"Provides deterministic model outputs through a seed parameter that controls the random number generator used during token sampling. When the same seed is provided with identical inputs, the model generates identical outputs, enabling reproducible results for testing, debugging, and consistent behavior in production systems.","intents":["I need to generate the same response for the same input during testing to verify model behavior hasn't changed","I want to debug a specific model output by reproducing it exactly with the same seed and input","I need consistent, deterministic responses in production for compliance or audit purposes"],"best_for":["QA engineers testing LLM-powered systems","developers debugging model behavior and output quality","teams with regulatory requirements for reproducible AI decisions"],"limitations":["Reproducibility is only guaranteed within the same model version — upgrading to a new model version may produce different outputs even with the same seed","Seed control does not guarantee identical outputs across different API regions or deployment environments due to floating-point precision differences","Deterministic outputs may be less creative or diverse — using a fixed seed reduces the model's ability to generate varied responses","Seed parameter is not available in all OpenAI API endpoints — check documentation for availability"],"requires":["OpenAI API key with GPT-4 Turbo access","HTTP client library supporting seed parameter in request body","Knowledge of seed value to use (any integer, typically 0-2^32)"],"input_types":["text (natural language prompts)","seed value (integer)"],"output_types":["text (deterministic response matching the seed)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4-turbo-preview__cap_4","uri":"capability://image.visual.vision.capable.multimodal.understanding.with.image.analysis","name":"vision-capable multimodal understanding with image analysis","description":"Processes images alongside text prompts to answer questions about visual content, perform OCR, analyze diagrams, and describe scenes. The model encodes images into visual tokens using a vision transformer backbone, then fuses them with text embeddings in the transformer for joint reasoning about image and text content.","intents":["I need to extract text from a screenshot or scanned document (OCR) and ask follow-up questions about it","I want to analyze a diagram, chart, or graph and have the model explain what it shows","I need to describe the contents of an image or answer questions about what's visible in a photo"],"best_for":["developers building document processing pipelines","teams implementing visual search or image analysis features","researchers analyzing diagrams, charts, or scientific images"],"limitations":["Image resolution is limited to approximately 2048x2048 pixels — higher resolution images are downsampled, potentially losing fine details","OCR accuracy is lower than specialized OCR tools (Tesseract, AWS Textract) — expect 85-95% accuracy depending on image quality and font","No support for video input — only static images are supported","Image processing adds latency — requests with images take 2-3x longer than text-only requests","Images are converted to tokens, consuming context window space — a single high-resolution image may use 500-1000 tokens"],"requires":["OpenAI API key with GPT-4 Turbo vision access","Image in base64-encoded format or publicly accessible URL","HTTP client library supporting multipart form data or JSON with base64 encoding","Image format support: JPEG, PNG, GIF, WebP"],"input_types":["image (JPEG, PNG, GIF, WebP, up to 20MB)","text (natural language prompts asking about the image)"],"output_types":["text (description, analysis, or answers about the image content)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4-turbo-preview__cap_5","uri":"capability://code.generation.editing.code.generation.and.completion.with.multi.language.support","name":"code generation and completion with multi-language support","description":"Generates syntactically correct code in 40+ programming languages based on natural language descriptions, code comments, or partial code. Uses transformer-based code understanding trained on public repositories to predict the next tokens in a code sequence, supporting both completion (filling in missing code) and generation (writing code from scratch).","intents":["I need to generate a Python function that implements a specific algorithm based on a description","I want to complete a partially written code snippet and have the model suggest the next lines","I need to convert code from one language to another (e.g., JavaScript to Python)"],"best_for":["developers accelerating coding tasks and reducing boilerplate","teams building code generation features into IDEs or development tools","educators teaching programming by having students refine AI-generated code"],"limitations":["Generated code may contain logical errors or security vulnerabilities — always review and test generated code before using in production","Code generation quality varies significantly by language — popular languages (Python, JavaScript) have higher quality than niche languages","No guarantee of best practices or idiomatic code — generated code may not follow language conventions or performance optimizations","Context window limits prevent generating very large functions (>500 lines) in a single request","No built-in testing or validation — you must write tests to verify generated code correctness"],"requires":["OpenAI API key with GPT-4 Turbo access","HTTP client library","Code editor or IDE integration (optional, for inline suggestions)"],"input_types":["text (natural language description of desired code)","code (partial code snippet to complete)","comments (code comments describing intended behavior)"],"output_types":["code (generated or completed code in the specified language)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4-turbo-preview__cap_6","uri":"capability://planning.reasoning.semantic.reasoning.and.chain.of.thought.planning","name":"semantic reasoning and chain-of-thought planning","description":"Decomposes complex problems into step-by-step reasoning chains through prompting techniques that encourage the model to 'think aloud' before providing answers. The model generates intermediate reasoning steps, which improve accuracy on multi-step problems by allowing the transformer to allocate more computation to reasoning rather than direct answer prediction.","intents":["I need to solve a complex math problem and want the model to show its work step-by-step","I want to analyze a scenario with multiple constraints and have the model reason through the implications","I need the model to explain its reasoning for a decision, not just provide the answer"],"best_for":["developers building reasoning-heavy AI systems (math solvers, logic puzzles, decision support)","teams implementing explainable AI where reasoning transparency is required","researchers studying model reasoning capabilities"],"limitations":["Chain-of-thought reasoning increases token consumption by 2-5x — longer reasoning chains consume more context and cost more","Reasoning quality depends heavily on prompt engineering — poorly structured prompts may lead to circular or incorrect reasoning","No guarantee of correct reasoning — the model may generate plausible-sounding but incorrect intermediate steps","Latency increases significantly — reasoning chains add 5-20 seconds to response time depending on problem complexity","Not all problems benefit from chain-of-thought — simple factual questions may be slower with explicit reasoning"],"requires":["OpenAI API key with GPT-4 Turbo access","Prompting technique (e.g., 'Let's think step by step' or 'Show your reasoning')","Ability to parse and validate reasoning steps in the response"],"input_types":["text (problem description or question)","structured data (constraints, parameters)"],"output_types":["text (reasoning steps followed by final answer)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4-turbo-preview__cap_7","uri":"capability://memory.knowledge.knowledge.cutoff.and.temporal.reasoning.limitations","name":"knowledge cutoff and temporal reasoning limitations","description":"The model has training data only up to December 2023, meaning it lacks knowledge of events, product releases, API changes, and research published after that date. Requests about current events or recent developments will produce outdated or hallucinated information, as the model cannot distinguish between pre-cutoff knowledge and post-cutoff speculation.","intents":["I need to understand what the model knows and doesn't know about recent events","I want to augment the model with current information using retrieval-augmented generation (RAG)","I need to identify when the model's knowledge is stale and supplement it with external data"],"best_for":["developers building systems that require current information (news analysis, stock prices, API documentation)","teams implementing RAG systems to augment the model with real-time data","researchers studying model knowledge boundaries and hallucination patterns"],"limitations":["No built-in mechanism to update knowledge — the model cannot access the internet or external databases","The model may confidently generate false information about post-cutoff events — it cannot distinguish between training data and speculation","Temporal reasoning is limited — the model cannot accurately reason about time-dependent facts (e.g., 'who is the current president')","No way to query the model's knowledge cutoff date programmatically — you must document it externally"],"requires":["Awareness of the December 2023 knowledge cutoff date","External data sources for current information (APIs, databases, web search)","RAG implementation if you need to augment the model with real-time data"],"input_types":["text (questions about current events or recent developments)"],"output_types":["text (potentially outdated or hallucinated information)"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4-turbo-preview__cap_8","uri":"capability://automation.workflow.rate.limiting.and.availability.constraints.during.preview","name":"rate limiting and availability constraints during preview","description":"The model is heavily rate-limited by OpenAI during the preview period, meaning requests may be throttled or rejected with 429 (Too Many Requests) errors. Rate limits vary by account tier and usage patterns, and the model may become temporarily unavailable during peak usage periods.","intents":["I need to understand the rate limits and plan my API usage accordingly","I want to implement exponential backoff and retry logic to handle rate limit errors","I need to estimate throughput and latency for production deployments"],"best_for":["developers planning production deployments and need to understand availability constraints","teams implementing robust error handling and retry logic","organizations evaluating whether the model is suitable for their throughput requirements"],"limitations":["Rate limits are not publicly documented — you must discover them through testing or contact OpenAI support","Rate limits may change without notice during the preview period","No guaranteed availability SLA — the model may become unavailable without warning","Concurrent request limits are stricter than for production models — you may not be able to parallelize requests effectively","Quota resets may not be hourly or daily — the exact reset schedule is unclear"],"requires":["OpenAI API key with GPT-4 Turbo preview access","Error handling code to catch 429 responses","Exponential backoff implementation for retries","Monitoring and logging to track rate limit errors"],"input_types":["API requests (any valid GPT-4 Turbo request)"],"output_types":["429 Too Many Requests error responses","successful responses when under rate limit"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["OpenAI API key with GPT-4 Turbo access","HTTP client library (Python requests, Node.js axios, etc.)","Network connectivity to api.openai.com","Account with sufficient API credits (pricing ~$0.01-0.03 per 1K tokens depending on input/output ratio)","HTTP client library supporting streaming or non-streaming responses","JSON parser in your application language (built-in for most languages)","Optional: JSON Schema validator library (e.g., jsonschema for Python, ajv for Node.js)","HTTP client library supporting streaming responses","Function definitions in OpenAI's function schema format (JSON with name, description, parameters)","Application code to execute functions and feed results back to the model"],"failure_modes":["Latency increases with context length — 128K token requests may take 30-60 seconds vs 2-5 seconds for 4K token requests","Attention computation is O(n²) in sequence length, making extremely long contexts slower than shorter ones","Training data cutoff at December 2023 means no knowledge of events, API changes, or library versions after that date","Rate limited by OpenAI during preview period — may experience 429 errors under high concurrent load","JSON mode does not validate against a provided schema — it only guarantees syntactically valid JSON, not semantic correctness","Complex nested structures may cause the model to hallucinate or truncate output if the JSON becomes too deeply nested","No support for JSON Schema validation — you must validate the output structure in your application code","Approximately 5-10% performance overhead compared to unconstrained text generation due to constraint checking during decoding","Function calls are generated sequentially by the model, not truly parallel — the model must decide which functions to call before seeing their results","No built-in error handling or retry logic — if a function call fails, you must implement fallback logic and re-prompt the model","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.43,"ecosystem":0.24,"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":"2026-05-24T12:16:24.485Z","last_scraped_at":"2026-05-03T15:20:45.777Z","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=openai-gpt-4-turbo-preview","compare_url":"https://unfragile.ai/compare?artifact=openai-gpt-4-turbo-preview"}},"signature":"rdrRaza6bh0z3ill/xsLi1nTrGILN73cVjcJT9ld3OsmN+l3z5K1s7eB/TvRjjD5c+/bLJpc5Ei2tjf6ERaBAg==","signedAt":"2026-06-21T09:28:38.360Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/openai-gpt-4-turbo-preview","artifact":"https://unfragile.ai/openai-gpt-4-turbo-preview","verify":"https://unfragile.ai/api/v1/verify?slug=openai-gpt-4-turbo-preview","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"}}