{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-openai-gpt-4o-mini-2024-07-18","slug":"openai-gpt-4o-mini-2024-07-18","name":"OpenAI: GPT-4o-mini (2024-07-18)","type":"model","url":"https://openrouter.ai/models/openai~gpt-4o-mini-2024-07-18","page_url":"https://unfragile.ai/openai-gpt-4o-mini-2024-07-18","categories":["image-generation"],"tags":["openai","api-access","text","image"],"pricing":{"model":"paid","free":false,"starting_price":"$1.50e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-openai-gpt-4o-mini-2024-07-18__cap_0","uri":"capability://image.visual.multimodal.text.and.image.understanding.with.unified.transformer.architecture","name":"multimodal text and image understanding with unified transformer architecture","description":"GPT-4o mini processes both text and image inputs through a single unified transformer backbone that natively handles vision and language tokens, eliminating separate vision encoders. The model uses a hybrid token representation where image patches are converted to embeddings and interleaved with text tokens in a single sequence, enabling fine-grained cross-modal reasoning without explicit fusion layers. This architecture allows the model to understand spatial relationships, text within images, and semantic connections between visual and textual content in a single forward pass.","intents":["I need to extract text and data from screenshots, PDFs, or documents with embedded images","I want to analyze charts, diagrams, or infographics and get structured insights","I need to answer questions about images that require reading text overlays or understanding context","I want to build a chatbot that can process user-uploaded images alongside text queries"],"best_for":["developers building document processing pipelines that mix text and visual content","teams creating multimodal chatbots or customer support systems","builders prototyping vision-language applications with cost constraints"],"limitations":["Image resolution capped at effective ~768x768 tokens; very high-resolution images are downsampled, losing fine detail","No video input support — only static images","Latency increases with image complexity; dense documents with small text may require multiple API calls","No native batch processing for images — each image requires a separate API request"],"requires":["OpenAI API key or OpenRouter API key","Images in JPEG, PNG, GIF, or WebP format","HTTP/2 or HTTP/1.1 connection to OpenAI or OpenRouter endpoints","Base64 encoding or URL hosting for image transmission"],"input_types":["text (UTF-8 strings, up to ~128k tokens context)","image (JPEG, PNG, GIF, WebP; max ~20MB per image in practice)"],"output_types":["text (UTF-8 strings, up to ~4096 tokens per response)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-mini-2024-07-18__cap_1","uri":"capability://text.generation.language.dense.context.reasoning.with.128k.token.window","name":"dense context reasoning with 128k token window","description":"GPT-4o mini maintains a 128,000 token context window that allows processing of entire documents, codebases, or conversation histories in a single request without summarization or chunking. The model uses a sliding-window attention mechanism with sparse attention patterns to manage computational cost while preserving long-range dependencies. This enables the model to reference information from the beginning of a document while generating output at the end, maintaining coherence across extended sequences.","intents":["I need to analyze a full codebase or multiple source files in one request without losing context","I want to process entire research papers, books, or long documents for summarization or Q&A","I need to maintain conversation history across 50+ turns without losing early context","I want to perform code review on large pull requests with full file context"],"best_for":["developers working with large codebases who need full-file context for refactoring","researchers and analysts processing long documents or datasets","teams building stateful chatbots that need to remember extended conversation history"],"limitations":["Token counting is approximate; actual token usage may vary by ±5% due to tokenizer edge cases","Latency scales linearly with context size; 128k tokens may take 10-15 seconds depending on output length","Attention quality degrades slightly in the middle of very long contexts (lost-in-the-middle effect)","Cost scales with input tokens; processing 128k tokens costs ~$0.15 USD, making it expensive for high-volume use"],"requires":["OpenAI API key or OpenRouter API key","Ability to tokenize input using cl100k_base tokenizer (OpenAI's standard)","HTTP connection to OpenAI or OpenRouter endpoints","Patience for 10-30 second response times on maximum-length inputs"],"input_types":["text (UTF-8 strings, up to 128,000 tokens)","code (any programming language, treated as text)","structured data (JSON, CSV, XML as text)"],"output_types":["text (UTF-8 strings, up to ~4096 tokens per response)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-mini-2024-07-18__cap_2","uri":"capability://text.generation.language.structured.output.generation.with.json.schema.validation","name":"structured output generation with json schema validation","description":"GPT-4o mini can be constrained to generate output matching a user-provided JSON schema, using guided decoding to enforce token-level constraints during generation. The model uses a constraint-satisfaction approach where at each token position, only tokens that maintain schema validity are allowed, preventing invalid JSON or schema violations. This enables reliable extraction of structured data without post-processing or retry logic, as the model cannot generate malformed output.","intents":["I need to extract structured data from unstructured text and guarantee valid JSON output","I want to generate function arguments for tool calling without parsing errors","I need to create consistent API responses with guaranteed schema compliance","I want to build data pipelines that extract entities, relationships, or classifications with zero invalid outputs"],"best_for":["developers building data extraction pipelines that require 100% valid output","teams implementing function-calling agents where schema compliance is critical","builders creating APIs that need deterministic, schema-validated responses"],"limitations":["Schema complexity is limited; deeply nested schemas (>10 levels) may cause latency spikes","Enum constraints with >100 values may reduce generation quality as the model optimizes for constraint satisfaction","Floating-point precision is limited to ~6 decimal places due to tokenizer constraints","Cannot enforce semantic constraints (e.g., 'date must be in the future') — only syntactic JSON schema validation"],"requires":["OpenAI API key or OpenRouter API key","JSON schema definition in JSON Schema Draft 2020-12 format","Understanding of which schema constraints are enforceable (type, enum, required, pattern for strings)","HTTP connection to OpenAI or OpenRouter endpoints"],"input_types":["text (UTF-8 strings with unstructured data)","JSON schema (as a JSON object defining output structure)"],"output_types":["JSON (valid JSON object or array matching provided schema)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-mini-2024-07-18__cap_3","uri":"capability://text.generation.language.cost.optimized.inference.with.50.smaller.model.size.than.gpt.4o","name":"cost-optimized inference with 50% smaller model size than gpt-4o","description":"GPT-4o mini achieves 50% parameter reduction compared to full GPT-4o through knowledge distillation and architectural optimization, maintaining competitive performance while reducing computational requirements. The model uses a more efficient attention mechanism and reduced hidden dimensions, enabling faster inference and lower memory footprint. This translates to ~60% lower API costs and ~2-3x faster response times compared to GPT-4o, making it suitable for high-volume applications where latency and cost are constraints.","intents":["I need to run high-volume inference (1000+ requests/day) without exceeding budget","I want to build real-time applications where sub-second latency is required","I need to deploy an LLM-powered service with tight cost-per-request constraints","I want to prototype and iterate quickly without incurring large API bills"],"best_for":["startups and indie developers with limited budgets","teams building high-volume customer-facing applications (chatbots, support systems)","builders prototyping LLM features before committing to larger models"],"limitations":["Performance gap on complex reasoning tasks (math, logic puzzles) is ~5-10% vs GPT-4o","Slightly lower quality on specialized domains (medical, legal) where GPT-4o has more training data","Context window is same as GPT-4o (128k) but effective utilization is slightly lower due to smaller model capacity","No fine-tuning support available (as of 2024-07-18); cannot customize for specific domains"],"requires":["OpenAI API key or OpenRouter API key","HTTP connection to OpenAI or OpenRouter endpoints","No special hardware or local deployment required"],"input_types":["text (UTF-8 strings, up to 128,000 tokens)","image (JPEG, PNG, GIF, WebP for multimodal inputs)"],"output_types":["text (UTF-8 strings, up to ~4096 tokens per response)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-mini-2024-07-18__cap_4","uri":"capability://tool.use.integration.function.calling.with.native.schema.binding.for.tool.orchestration","name":"function calling with native schema binding for tool orchestration","description":"GPT-4o mini supports function calling through a schema-based interface where developers define tool signatures as JSON schemas, and the model generates structured function calls that can be directly executed. The model uses a special token sequence to indicate function calls, allowing the API to parse and route calls without additional parsing logic. This enables seamless integration with external APIs, databases, and custom tools through a standardized calling convention that works across OpenAI, Anthropic, and other providers via OpenRouter.","intents":["I need to build an agent that can call APIs, databases, or custom functions based on user requests","I want to create a chatbot that can search the web, fetch data, or perform actions in response to queries","I need to orchestrate multi-step workflows where the model decides which tools to call and in what order","I want to integrate LLM reasoning with deterministic business logic and external systems"],"best_for":["developers building LLM agents and autonomous systems","teams creating chatbots that need to interact with external APIs or databases","builders implementing AI-powered automation workflows"],"limitations":["Function calls are generated sequentially; parallel function execution requires manual orchestration","No built-in retry logic; failed function calls must be handled by the application layer","Schema complexity is limited; deeply nested or recursive schemas may cause generation failures","No native support for streaming function calls; entire call must be generated before execution"],"requires":["OpenAI API key or OpenRouter API key","Function definitions as JSON schemas in OpenAI's format","Application logic to execute functions and return results to the model","HTTP connection to OpenAI or OpenRouter endpoints"],"input_types":["text (UTF-8 strings with user queries)","JSON schemas (function definitions)"],"output_types":["function calls (JSON objects with function name and arguments)","text (model reasoning or final response)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-mini-2024-07-18__cap_5","uri":"capability://image.visual.vision.based.document.and.table.extraction.with.ocr.level.accuracy","name":"vision-based document and table extraction with ocr-level accuracy","description":"GPT-4o mini can extract text, tables, and structured data from images of documents, forms, and tables with near-OCR accuracy, using its unified vision-language architecture to understand layout, formatting, and semantic relationships. The model recognizes table structure, preserves formatting, and can extract data into structured formats (JSON, CSV, Markdown tables) without separate OCR preprocessing. This enables end-to-end document processing where images are converted to structured data in a single API call.","intents":["I need to extract data from scanned documents, invoices, or receipts without using a separate OCR service","I want to convert tables in images or PDFs into structured data (JSON, CSV)","I need to read handwritten or printed forms and extract field values","I want to build a document processing pipeline that handles mixed formats (images, PDFs, screenshots)"],"best_for":["teams building document processing and data entry automation systems","developers creating invoice or receipt processing pipelines","builders implementing form digitization or data extraction workflows"],"limitations":["Handwriting recognition is limited to printed or clearly written text; cursive or poor handwriting may fail","Very small text (<8pt) may be misread due to image resolution limits","Complex multi-page documents require separate API calls per page; no native batch processing","Table extraction works best for structured tables; irregular layouts or merged cells may cause errors"],"requires":["OpenAI API key or OpenRouter API key","Images in JPEG, PNG, GIF, or WebP format","Reasonable image quality (minimum ~150 DPI for readable text)","HTTP connection to OpenAI or OpenRouter endpoints"],"input_types":["image (JPEG, PNG, GIF, WebP of documents, forms, or tables)","text (optional instructions for extraction format or fields to extract)"],"output_types":["text (extracted text, Markdown tables, or JSON structured data)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-mini-2024-07-18__cap_6","uri":"capability://planning.reasoning.reasoning.aware.response.generation.with.chain.of.thought.capability","name":"reasoning-aware response generation with chain-of-thought capability","description":"GPT-4o mini can generate step-by-step reasoning before producing final answers, using an internal chain-of-thought mechanism that improves accuracy on complex tasks. The model can be prompted to 'think through' problems before responding, which increases latency but improves correctness on reasoning-heavy tasks like math, logic, and multi-step problem solving. This capability is implemented through prompt engineering rather than a separate reasoning model, making it lightweight and cost-effective.","intents":["I need to solve math problems or logic puzzles with step-by-step working shown","I want to generate explanations that show reasoning process, not just final answers","I need to improve accuracy on complex tasks by encouraging the model to think through problems","I want to build educational tools that teach problem-solving methodology"],"best_for":["developers building educational or tutoring applications","teams creating reasoning-heavy applications (math solvers, logic engines)","builders implementing explainable AI systems that show working"],"limitations":["Chain-of-thought increases latency by 30-50%; not suitable for real-time applications","Reasoning quality degrades on very complex problems (>5 steps); GPT-4o full model performs better","No native support for verifying reasoning correctness; hallucinations can occur in intermediate steps","Token usage increases by 2-3x when using chain-of-thought due to longer output"],"requires":["OpenAI API key or OpenRouter API key","Prompt engineering to request step-by-step reasoning","Tolerance for increased latency (10-30 seconds for complex problems)","HTTP connection to OpenAI or OpenRouter endpoints"],"input_types":["text (UTF-8 strings with problems or questions)"],"output_types":["text (step-by-step reasoning followed by final answer)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-mini-2024-07-18__cap_7","uri":"capability://text.generation.language.multilingual.text.generation.and.understanding.across.100.languages","name":"multilingual text generation and understanding across 100+ languages","description":"GPT-4o mini supports input and output in 100+ languages including low-resource languages, using a shared multilingual token space that enables cross-lingual transfer and code-switching. The model was trained on diverse language corpora and can handle language mixing within a single prompt, making it suitable for multilingual applications. Performance is consistent across major languages (English, Spanish, French, German, Chinese, Japanese) with graceful degradation for less common languages.","intents":["I need to build a chatbot or application that supports multiple languages without separate models","I want to translate content between languages while preserving meaning and tone","I need to process user input in mixed languages (code-switching) and respond appropriately","I want to create global applications that serve non-English speaking users"],"best_for":["teams building global applications with multilingual user bases","developers creating translation or localization tools","builders serving non-English markets without language-specific model deployment"],"limitations":["Performance on low-resource languages (e.g., Icelandic, Swahili) is 10-20% lower than English","Transliteration and script conversion (e.g., Latin to Cyrillic) may have errors","Cultural context and idioms may not translate perfectly; human review recommended for critical content","No native support for right-to-left languages (Arabic, Hebrew) in terms of special formatting"],"requires":["OpenAI API key or OpenRouter API key","UTF-8 encoding support for non-Latin scripts","HTTP connection to OpenAI or OpenRouter endpoints","No language-specific configuration needed; model auto-detects language"],"input_types":["text (UTF-8 strings in any of 100+ supported languages)"],"output_types":["text (UTF-8 strings in requested language)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"low","permissions":["OpenAI API key or OpenRouter API key","Images in JPEG, PNG, GIF, or WebP format","HTTP/2 or HTTP/1.1 connection to OpenAI or OpenRouter endpoints","Base64 encoding or URL hosting for image transmission","Ability to tokenize input using cl100k_base tokenizer (OpenAI's standard)","HTTP connection to OpenAI or OpenRouter endpoints","Patience for 10-30 second response times on maximum-length inputs","JSON schema definition in JSON Schema Draft 2020-12 format","Understanding of which schema constraints are enforceable (type, enum, required, pattern for strings)","No special hardware or local deployment required"],"failure_modes":["Image resolution capped at effective ~768x768 tokens; very high-resolution images are downsampled, losing fine detail","No video input support — only static images","Latency increases with image complexity; dense documents with small text may require multiple API calls","No native batch processing for images — each image requires a separate API request","Token counting is approximate; actual token usage may vary by ±5% due to tokenizer edge cases","Latency scales linearly with context size; 128k tokens may take 10-15 seconds depending on output length","Attention quality degrades slightly in the middle of very long contexts (lost-in-the-middle effect)","Cost scales with input tokens; processing 128k tokens costs ~$0.15 USD, making it expensive for high-volume use","Schema complexity is limited; deeply nested schemas (>10 levels) may cause latency spikes","Enum constraints with >100 values may reduce generation quality as the model optimizes for constraint satisfaction","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.41,"ecosystem":0.27,"match_graph":0.25,"freshness":0.9,"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":"2026-05-03T15:20:45.776Z","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=openai-gpt-4o-mini-2024-07-18","compare_url":"https://unfragile.ai/compare?artifact=openai-gpt-4o-mini-2024-07-18"}},"signature":"CKodhyqzZuUI6d/zGSeTiPeosJJZhYSag1AUF5jYxJG3hUtCk6SFC9+yjURgBwITnILN7ouQ9/rMNXfxbknTDQ==","signedAt":"2026-06-16T07:42:53.252Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/openai-gpt-4o-mini-2024-07-18","artifact":"https://unfragile.ai/openai-gpt-4o-mini-2024-07-18","verify":"https://unfragile.ai/api/v1/verify?slug=openai-gpt-4o-mini-2024-07-18","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"}}