{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-openai-o4-mini-high","slug":"openai-o4-mini-high","name":"OpenAI: o4 Mini High","type":"model","url":"https://openrouter.ai/models/openai~o4-mini-high","page_url":"https://unfragile.ai/openai-o4-mini-high","categories":["model-training"],"tags":["openai","api-access","text","image"],"pricing":{"model":"paid","free":false,"starting_price":"$1.10e-6 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-openai-o4-mini-high__cap_0","uri":"capability://planning.reasoning.extended.chain.of.thought.reasoning.with.configurable.effort.levels","name":"extended-chain-of-thought reasoning with configurable effort levels","description":"Implements OpenAI's o-series reasoning architecture with a high reasoning_effort parameter that allocates extended computational budget to internal chain-of-thought processing before generating responses. The model uses a two-stage inference pipeline: first, an internal reasoning phase that explores multiple solution paths and validates logic chains, then a response generation phase that synthesizes conclusions. This approach enables deeper problem decomposition and error correction within the reasoning trace without exposing intermediate steps to the user.","intents":["I need to solve complex multi-step problems that require deep logical reasoning and verification","I want the model to catch its own mistakes during reasoning rather than producing confident wrong answers","I need to tackle problems where the solution path is non-obvious and requires exploring multiple approaches"],"best_for":["developers building reasoning-heavy AI agents for technical problem-solving","teams working on math, logic, and code verification tasks","researchers prototyping advanced reasoning capabilities with cost constraints"],"limitations":["High reasoning_effort mode increases latency significantly (typically 5-30 seconds per request) compared to standard models","Reasoning budget is opaque to users — no visibility into internal reasoning traces or token allocation","Cost per request is substantially higher than standard models due to extended compute allocation","Not optimized for real-time applications or high-throughput scenarios requiring sub-second responses"],"requires":["OpenAI API key with access to o4-mini or o4-mini-high model tier","HTTP client capable of handling long-polling or streaming responses (typical timeout 60+ seconds)","Understanding of reasoning_effort parameter semantics (low/medium/high) to tune cost vs quality tradeoff"],"input_types":["text","code snippets","mathematical problems","logic puzzles","multi-step instructions"],"output_types":["text","code","structured explanations","step-by-step solutions"],"categories":["planning-reasoning","advanced-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o4-mini-high__cap_1","uri":"capability://planning.reasoning.compact.model.inference.with.cost.efficiency.optimization","name":"compact model inference with cost-efficiency optimization","description":"Implements a lightweight variant of the o-series reasoning architecture optimized for reduced parameter count and inference cost while maintaining reasoning capabilities. The model uses knowledge distillation and architectural pruning techniques to compress the full o-series model into a 'mini' form factor that runs faster and cheaper. This enables reasoning-grade problem-solving on a budget suitable for high-volume or resource-constrained applications, trading some reasoning depth for 3-5x cost reduction.","intents":["I need reasoning capabilities but my application has tight cost constraints or high request volume","I want to use advanced reasoning for non-critical tasks where perfect accuracy isn't essential","I need to prototype reasoning-based features before committing to full-size model costs"],"best_for":["startups and small teams with limited API budgets","applications requiring reasoning on high-volume datasets","developers building cost-sensitive reasoning agents for production use"],"limitations":["Reduced reasoning depth compared to full o-series models — may miss complex multi-step logical chains","Performance degrades on problems requiring very long reasoning traces or extensive backtracking","Still slower than standard GPT-4 class models despite cost reduction","No fine-tuning or custom training available for mini variants"],"requires":["OpenAI API key with access to o4-mini tier","Cost monitoring infrastructure to track per-request expenses at scale","Acceptance of slightly lower reasoning quality vs full o-series models"],"input_types":["text","code","structured problems"],"output_types":["text","code","solutions"],"categories":["planning-reasoning","cost-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o4-mini-high__cap_2","uri":"capability://image.visual.multi.modal.text.and.image.understanding.with.reasoning","name":"multi-modal text and image understanding with reasoning","description":"Integrates vision processing capabilities into the reasoning architecture, allowing the model to analyze images, diagrams, charts, and screenshots as part of its reasoning process. The model uses a vision encoder that converts images into a token representation compatible with the reasoning pipeline, enabling the model to reason about visual content, extract information from diagrams, and solve problems that require both visual and logical analysis. This supports use cases like code review from screenshots, diagram interpretation, and visual problem-solving.","intents":["I need to analyze code screenshots and reason about potential bugs or improvements","I want to extract information from diagrams or charts and perform calculations based on visual data","I need to solve problems that require understanding both text descriptions and visual representations"],"best_for":["developers debugging code from screenshots or screen recordings","teams analyzing visual documentation or architecture diagrams","applications requiring visual reasoning for technical problem-solving"],"limitations":["Image processing adds latency on top of reasoning latency — total response time can exceed 30+ seconds","Vision encoding has token overhead — images consume significant token budget reducing text context window","Accuracy on small text in images or low-resolution diagrams may be degraded","No support for video input despite mention of video processing in broader o-series capabilities"],"requires":["OpenAI API key with vision-enabled o4-mini access","Images in supported formats (JPEG, PNG, WebP, GIF)","Base64 encoding or URL hosting for image transmission to API"],"input_types":["text","images (JPEG, PNG, WebP, GIF)","code screenshots","diagrams","charts"],"output_types":["text","code","analysis","structured insights"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o4-mini-high__cap_3","uri":"capability://tool.use.integration.api.based.inference.with.streaming.and.non.streaming.response.modes","name":"api-based inference with streaming and non-streaming response modes","description":"Exposes the o4-mini-high model through OpenAI's REST API with support for both streaming and non-streaming response modes. The implementation uses HTTP POST requests to the completions endpoint with configurable parameters (reasoning_effort, temperature, max_tokens) that control inference behavior. Streaming mode returns tokens incrementally via server-sent events, enabling real-time response display; non-streaming mode returns the complete response after reasoning completes. The API handles request queuing, rate limiting, and error recovery transparently.","intents":["I need to integrate advanced reasoning into my application via a standard REST API","I want to stream reasoning responses to users in real-time rather than waiting for complete inference","I need to programmatically control reasoning depth and response length via API parameters"],"best_for":["backend developers integrating reasoning into web applications or services","teams building chatbots or assistants that need reasoning capabilities","developers prototyping with OpenAI models via standard API patterns"],"limitations":["Streaming mode does not expose internal reasoning traces — only final response tokens are streamed","API rate limits apply (varies by account tier) — high-volume applications may hit quota limits","No local inference option — all requests route through OpenAI's infrastructure with associated latency","Reasoning_effort parameter is coarse-grained (low/medium/high) — no fine-grained control over computational budget"],"requires":["OpenAI API key with billing enabled","HTTP client library (Python requests, Node.js fetch, etc.)","Understanding of OpenAI API authentication and request formatting","Network connectivity to api.openai.com"],"input_types":["text","images (when multi-modal enabled)","structured prompts"],"output_types":["text (streamed or buffered)","structured JSON (with response_format parameter)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o4-mini-high__cap_4","uri":"capability://data.processing.analysis.structured.output.generation.with.json.schema.validation","name":"structured output generation with json schema validation","description":"Supports response_format parameter to constrain model outputs to valid JSON matching a user-provided schema. The implementation uses the reasoning pipeline to generate responses that conform to specified JSON structures, with built-in validation ensuring the output is parseable and schema-compliant. This enables reliable extraction of structured data (e.g., parsed code, categorized analysis, extracted entities) from reasoning processes without post-processing or regex parsing. The schema validation happens during generation, not after, reducing latency and ensuring 100% valid JSON output.","intents":["I need to extract structured data from complex reasoning without manual parsing","I want to ensure API responses are always valid JSON matching my application's data model","I need to generate code or configuration files with guaranteed syntactic correctness"],"best_for":["developers building data extraction pipelines with reasoning","teams generating structured outputs (code, configs, reports) from reasoning","applications requiring guaranteed JSON compatibility for downstream processing"],"limitations":["Schema complexity is limited — very large or deeply nested schemas may reduce reasoning quality","Structured output mode adds overhead to reasoning computation — slightly increases latency","Schema validation is strict — model cannot generate outputs that partially violate the schema","No support for custom validation logic beyond JSON schema — complex business rules must be post-processed"],"requires":["Valid JSON schema definition (JSON Schema draft 7 or later)","OpenAI API key with structured output support enabled","Client library that supports response_format parameter"],"input_types":["text","JSON schema definitions"],"output_types":["JSON (guaranteed schema-compliant)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o4-mini-high__cap_5","uri":"capability://memory.knowledge.context.window.management.with.token.counting","name":"context window management with token counting","description":"Manages a fixed context window (typically 128K tokens for o4-mini) with built-in token counting to help developers track usage and optimize prompts. The implementation provides a tokens_per_message parameter and token counting utilities that estimate prompt and completion token consumption before making API calls. This enables developers to fit large documents, code repositories, or conversation histories within the context window without trial-and-error. Token counting accounts for special tokens, message formatting, and reasoning overhead.","intents":["I need to understand how many tokens my prompt will consume before submitting it","I want to fit a large codebase or document into the context window for analysis","I need to optimize my prompts to stay within token limits while maximizing reasoning depth"],"best_for":["developers working with large documents or codebases that need reasoning analysis","teams optimizing API costs by tracking token consumption","applications with variable input sizes that need to adapt context usage dynamically"],"limitations":["Token counting is approximate — actual token consumption may vary by 1-5% due to tokenizer edge cases","Reasoning tokens are not pre-counted — actual reasoning token usage is unknown until after inference completes","Context window is fixed at 128K tokens — no dynamic expansion for larger inputs","No automatic context pruning or summarization — developers must manually manage what fits in the window"],"requires":["OpenAI API key","Token counting library or API endpoint access","Understanding of token economics and how special tokens affect counts"],"input_types":["text","code","documents"],"output_types":["token count estimates","cost projections"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"low","permissions":["OpenAI API key with access to o4-mini or o4-mini-high model tier","HTTP client capable of handling long-polling or streaming responses (typical timeout 60+ seconds)","Understanding of reasoning_effort parameter semantics (low/medium/high) to tune cost vs quality tradeoff","OpenAI API key with access to o4-mini tier","Cost monitoring infrastructure to track per-request expenses at scale","Acceptance of slightly lower reasoning quality vs full o-series models","OpenAI API key with vision-enabled o4-mini access","Images in supported formats (JPEG, PNG, WebP, GIF)","Base64 encoding or URL hosting for image transmission to API","OpenAI API key with billing enabled"],"failure_modes":["High reasoning_effort mode increases latency significantly (typically 5-30 seconds per request) compared to standard models","Reasoning budget is opaque to users — no visibility into internal reasoning traces or token allocation","Cost per request is substantially higher than standard models due to extended compute allocation","Not optimized for real-time applications or high-throughput scenarios requiring sub-second responses","Reduced reasoning depth compared to full o-series models — may miss complex multi-step logical chains","Performance degrades on problems requiring very long reasoning traces or extensive backtracking","Still slower than standard GPT-4 class models despite cost reduction","No fine-tuning or custom training available for mini variants","Image processing adds latency on top of reasoning latency — total response time can exceed 30+ seconds","Vision encoding has token overhead — images consume significant token budget reducing text context window","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.37,"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":"2026-05-03T15:20:45.776Z","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-o4-mini-high","compare_url":"https://unfragile.ai/compare?artifact=openai-o4-mini-high"}},"signature":"2uMiI4UJbLjCAQmDRf2b57UjBBAnJML0guNXmVnYmT/Q43F0/1peAcHRLcjuWxT0mpGkyrn7IMdIO7hqJMIxBA==","signedAt":"2026-06-22T16:55:42.009Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/openai-o4-mini-high","artifact":"https://unfragile.ai/openai-o4-mini-high","verify":"https://unfragile.ai/api/v1/verify?slug=openai-o4-mini-high","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"}}