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The vision encoder converts images into token embeddings that are interleaved with text tokens in the same attention mechanism, allowing cross-modal reasoning without separate vision-language fusion layers.","intents":["I need to ask questions about images and get detailed textual analysis","I want to extract information from screenshots, diagrams, or photographs","I need to describe visual content in natural language for accessibility or documentation"],"best_for":["developers building document analysis pipelines","teams automating visual content understanding workflows","builders creating accessibility tools that describe images"],"limitations":["Vision processing adds ~500-800ms latency per request compared to text-only","Image resolution capped at effective ~2000x2000 pixels; larger images are downsampled","Cannot generate, edit, or manipulate images — only analyze them","Vision understanding trained on data through December 2023; may misinterpret very recent visual trends"],"requires":["OpenAI API key or OpenRouter API key","Images in JPEG, PNG, GIF, or WebP format","Image file size under 20MB per request","HTTP/HTTPS endpoint access to OpenAI or OpenRouter"],"input_types":["text (prompt)","image (JPEG, PNG, GIF, WebP)","mixed text and image in single request"],"output_types":["text (natural language response)","structured text (when used with JSON mode)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4-turbo__cap_1","uri":"capability://data.processing.analysis.structured.output.generation.with.json.mode.for.vision.requests","name":"structured output generation with json mode for vision requests","description":"Enforces JSON schema compliance on model outputs when processing vision inputs, using constrained decoding to guarantee valid JSON structure without post-processing. The model's token generation is guided by a schema validator that prunes invalid tokens at each step, ensuring the output conforms to a user-specified JSON schema while maintaining semantic understanding of image content.","intents":["I need to extract structured data from images (invoices, forms, tables) into JSON","I want to ensure vision analysis results are machine-readable and parseable","I need to validate that image understanding outputs match my application's data model"],"best_for":["developers building document processing pipelines","teams automating data extraction from visual documents","builders creating form recognition systems"],"limitations":["JSON mode with vision adds ~300-400ms overhead due to schema validation at each token","Schema complexity impacts generation speed; deeply nested schemas (>5 levels) may slow output","Cannot guarantee semantic accuracy of extracted data — only structural validity of JSON","Schema must be provided upfront; dynamic schema generation not supported"],"requires":["OpenAI API key with vision model access","Valid JSON schema definition (JSON Schema draft 2020-12 compatible)","Image input in supported format (JPEG, PNG, GIF, WebP)","API call parameter: response_format={type: 'json_object', schema: {...}}"],"input_types":["image (JPEG, PNG, GIF, WebP)","text (prompt describing extraction task)","JSON schema (user-provided structure definition)"],"output_types":["JSON (guaranteed valid, schema-compliant)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4-turbo__cap_2","uri":"capability://tool.use.integration.function.calling.with.vision.context","name":"function calling with vision context","description":"Enables the model to invoke external functions based on visual analysis, using a schema-based function registry that maps image understanding to API calls. The model generates function names and arguments by analyzing image content, with the function calling interface supporting multiple concurrent function invocations and automatic parameter type coercion based on the schema definition.","intents":["I need to analyze an image and automatically trigger downstream actions (e.g., create a ticket from a screenshot)","I want to use vision understanding to determine which API to call and with what parameters","I need to build a workflow where image analysis drives function execution"],"best_for":["developers building vision-driven automation workflows","teams creating intelligent document routing systems","builders implementing image-triggered API orchestration"],"limitations":["Function calling with vision adds ~400-600ms latency due to schema matching overhead","Model may hallucinate function calls that don't exist if schema is ambiguous","Parallel function calling supported but sequential execution required on client side","No built-in retry logic for failed function invocations — must be implemented by caller"],"requires":["OpenAI API key with function calling support","Function schema definitions in OpenAI format (name, description, parameters)","Image input in supported format","Client-side function implementation to handle invocations"],"input_types":["image (JPEG, PNG, GIF, WebP)","text (prompt describing desired action)","function schema array (function definitions)"],"output_types":["function call objects (name, arguments)","text (fallback response if no function matches)"],"categories":["tool-use-integration","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4-turbo__cap_3","uri":"capability://text.generation.language.long.context.text.generation.with.128k.token.window","name":"long-context text generation with 128k token window","description":"Processes up to 128,000 tokens (approximately 96,000 words) in a single request, enabling analysis of entire documents, codebases, or conversation histories without truncation. The model uses a sliding window attention mechanism with sparse attention patterns to manage the computational cost of long sequences, allowing efficient processing of multi-document inputs and maintaining coherence across extended contexts.","intents":["I need to analyze or summarize entire books, research papers, or long documents","I want to maintain conversation history across hundreds of exchanges without losing context","I need to process entire codebases for refactoring or analysis tasks"],"best_for":["developers building document analysis systems","teams creating long-form content generation tools","builders implementing stateful conversational agents"],"limitations":["Latency increases non-linearly with context length; 128K tokens may take 10-15 seconds vs 1-2 seconds for 4K tokens","Cost scales linearly with input tokens; 128K context is 32x more expensive than 4K baseline","Model attention may dilute on very long contexts; information in the middle of long documents receives less focus than beginning/end","Output tokens still limited to 4,096 per request regardless of input length"],"requires":["OpenAI API key with GPT-4 Turbo access","Text input up to 128,000 tokens","Sufficient API quota for higher token usage","Client-side token counting to avoid exceeding limits"],"input_types":["text (up to 128,000 tokens)","code (entire files or multiple files concatenated)","structured text (markdown, JSON, XML)"],"output_types":["text (up to 4,096 tokens)","code (up to 4,096 tokens)","structured data (JSON, markdown)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4-turbo__cap_4","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 valid code across 40+ programming languages using transformer-based token prediction trained on public code repositories and documentation. The model understands language-specific idioms, frameworks, and best practices, producing code that follows conventions for each language rather than generic templates. Completion works both for inline suggestions and full function/class generation based on context and docstrings.","intents":["I need to generate boilerplate code or complete partial implementations","I want to translate code between programming languages","I need to generate code that follows specific frameworks or libraries"],"best_for":["developers using IDEs or editors without native code completion","teams building code generation tools or linters","builders creating polyglot development environments"],"limitations":["Generated code may contain logical errors or security vulnerabilities; always requires review","Performance degrades for very long functions (>500 lines); tends to lose context and repeat patterns","No real-time linting or type checking; generated code may not compile without fixes","Training data cutoff (December 2023) means unfamiliarity with very recent language versions or frameworks"],"requires":["OpenAI API key","Code context (file content, function signature, or docstring)","Target language specification or inference from file extension"],"input_types":["text (code snippet, function signature, docstring)","code (partial implementation to complete)"],"output_types":["code (generated or completed implementation)","text (explanation of generated code)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4-turbo__cap_5","uri":"capability://planning.reasoning.semantic.reasoning.and.chain.of.thought.explanation","name":"semantic reasoning and chain-of-thought explanation","description":"Generates step-by-step reasoning chains that decompose complex problems into intermediate steps, using a learned pattern of explicit reasoning before final answers. The model produces internal monologue-style outputs that show mathematical derivations, logical deductions, or multi-step problem solving, improving accuracy on reasoning-heavy tasks by forcing the model to articulate intermediate conclusions rather than jumping to answers.","intents":["I need the model to show its work and explain reasoning for complex problems","I want to verify that the model's logic is sound before trusting the answer","I need to debug why the model arrived at a particular conclusion"],"best_for":["developers building educational or tutoring systems","teams creating explainable AI systems for high-stakes decisions","builders implementing verification or audit trails for model outputs"],"limitations":["Chain-of-thought reasoning adds 2-5x latency and token cost compared to direct answers","Reasoning chains may contain errors or circular logic that aren't caught by the model","Model may generate plausible-sounding but incorrect reasoning (confabulation)","Reasoning quality degrades on tasks outside the model's training distribution"],"requires":["OpenAI API key","Prompt engineering to request step-by-step reasoning (e.g., 'Let's think step by step')","Sufficient token budget for longer outputs"],"input_types":["text (problem statement, question, or prompt requesting reasoning)"],"output_types":["text (step-by-step reasoning chain followed by final answer)","structured reasoning (when combined with JSON mode)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4-turbo__cap_6","uri":"capability://text.generation.language.knowledge.cutoff.aware.response.generation.with.uncertainty.signaling","name":"knowledge cutoff-aware response generation with uncertainty signaling","description":"Generates responses while explicitly acknowledging knowledge limitations based on a December 2023 training cutoff, signaling uncertainty when asked about recent events, newly released products, or evolving information. The model learned to distinguish between stable knowledge (mathematics, historical facts) and time-sensitive information, producing appropriate caveats rather than hallucinating recent information.","intents":["I need the model to acknowledge when it doesn't have information about recent events","I want to avoid receiving confidently stated false information about current topics","I need to know when to supplement the model's knowledge with external data sources"],"best_for":["developers building systems that need to handle current events or real-time data","teams creating news analysis or research tools","builders implementing hybrid systems that combine LLM reasoning with external data"],"limitations":["Model may still hallucinate about recent topics despite training to avoid it","Uncertainty signals are learned patterns, not formal guarantees","No mechanism to update knowledge without retraining; requires external data integration for current information","Cutoff date (December 2023) means missing ~12+ months of developments in fast-moving fields"],"requires":["OpenAI API key","Understanding that responses about post-December 2023 events may be unreliable","External data sources for current information (web search, APIs, databases)"],"input_types":["text (question or prompt about any topic)"],"output_types":["text (response with appropriate uncertainty caveats for recent topics)"],"categories":["text-generation-language","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4-turbo__cap_7","uri":"capability://text.generation.language.multilingual.text.generation.and.translation","name":"multilingual text generation and translation","description":"Generates coherent text and performs translation across 100+ languages using a unified multilingual transformer trained on parallel corpora and monolingual text in diverse languages. The model understands language-specific grammar, idioms, and cultural context, producing natural translations rather than word-for-word substitutions. A single model handles all language pairs without requiring separate translation models.","intents":["I need to translate content between languages while preserving meaning and tone","I want to generate content in multiple languages from a single prompt","I need to analyze or summarize text in non-English languages"],"best_for":["developers building multilingual applications or content platforms","teams creating global customer support systems","builders implementing cross-language content analysis"],"limitations":["Translation quality varies significantly by language pair; high-resource pairs (English-Spanish) are better than low-resource pairs (English-Icelandic)","Model may struggle with domain-specific terminology or technical jargon in non-English languages","Cultural context and idioms may be lost or mistranslated, especially for distant language pairs","Performance degrades for languages with non-Latin scripts or complex morphology"],"requires":["OpenAI API key","Text input in supported language","Language specification in prompt (e.g., 'Translate to Spanish')"],"input_types":["text (in any of 100+ supported languages)"],"output_types":["text (in target language)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4-turbo__cap_8","uri":"capability://text.generation.language.prompt.optimization.and.instruction.following","name":"prompt optimization and instruction following","description":"Interprets and follows complex, multi-step instructions with high fidelity, including nested conditionals, format specifications, and role-based prompting. The model learned instruction-following patterns from RLHF (reinforcement learning from human feedback) training, enabling it to parse detailed system prompts and user instructions and adapt its behavior accordingly without requiring explicit programming.","intents":["I need the model to follow specific output formats (JSON, markdown, tables, etc.)","I want to define custom behavior through system prompts without code changes","I need the model to adopt a specific role or persona for a task"],"best_for":["developers building prompt-driven applications","teams creating customizable AI workflows","builders implementing role-based AI assistants"],"limitations":["Instruction following degrades with very long or contradictory instructions","Model may misinterpret ambiguous instructions or prioritize recent instructions over earlier ones","No formal verification that instructions are followed; requires testing and validation","Complex nested instructions may cause the model to lose track of requirements"],"requires":["OpenAI API key","Well-crafted system prompt or instruction set","Clear specification of desired output format"],"input_types":["text (system prompt defining behavior)","text (user input or query)"],"output_types":["text (formatted according to instructions)","structured data (JSON, markdown, tables, etc.)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"low","permissions":["OpenAI API key or OpenRouter API key","Images in JPEG, PNG, GIF, or WebP format","Image file size under 20MB per request","HTTP/HTTPS endpoint access to OpenAI or OpenRouter","OpenAI API key with vision model access","Valid JSON schema definition (JSON Schema draft 2020-12 compatible)","Image input in supported format (JPEG, PNG, GIF, WebP)","API call parameter: response_format={type: 'json_object', schema: {...}}","OpenAI API key with function calling support","Function schema definitions in OpenAI format (name, description, parameters)"],"failure_modes":["Vision processing adds ~500-800ms latency per request compared to text-only","Image resolution capped at effective ~2000x2000 pixels; larger images are downsampled","Cannot generate, edit, or manipulate images — only analyze them","Vision understanding trained on data through December 2023; may misinterpret very recent visual trends","JSON mode with vision adds ~300-400ms overhead due to schema validation at each token","Schema complexity impacts generation speed; deeply nested schemas (>5 levels) may slow output","Cannot guarantee semantic accuracy of extracted data — only structural validity of JSON","Schema must be provided upfront; dynamic schema generation not supported","Function calling with vision adds ~400-600ms latency due to schema matching overhead","Model may hallucinate function calls that don't exist if schema is ambiguous","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.43,"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":"2026-05-24T12:16:24.485Z","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","compare_url":"https://unfragile.ai/compare?artifact=openai-gpt-4-turbo"}},"signature":"YvG83hQA3qwmLAcnwRd0gK0BqnrSUgDWPP60ndo470/GzYnoBx4Xw24vUZbh/lFf1Dn9N6XAAg28yYwazawfAQ==","signedAt":"2026-06-22T01:59:55.393Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/openai-gpt-4-turbo","artifact":"https://unfragile.ai/openai-gpt-4-turbo","verify":"https://unfragile.ai/api/v1/verify?slug=openai-gpt-4-turbo","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"}}