{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-openai-o3-pro","slug":"openai-o3-pro","name":"OpenAI: o3 Pro","type":"model","url":"https://openrouter.ai/models/openai~o3-pro","page_url":"https://unfragile.ai/openai-o3-pro","categories":["image-generation"],"tags":["openai","api-access","text","image"],"pricing":{"model":"paid","free":false,"starting_price":"$2.00e-5 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-openai-o3-pro__cap_0","uri":"capability://planning.reasoning.extended.chain.of.thought.reasoning.with.compute.allocation","name":"extended-chain-of-thought reasoning with compute allocation","description":"Implements reinforcement learning-trained reasoning that allocates variable computational budget across thinking phases before generating responses. The model uses an internal chain-of-thought mechanism where it can 'think' for extended periods (up to specified token limits) before committing to an answer, similar to o1/o3 architecture. This enables structured problem decomposition, hypothesis testing, and self-correction within a single inference pass without requiring external orchestration.","intents":["I need the model to work through complex multi-step problems and show its reasoning before answering","I want better accuracy on math, logic, and coding problems by allowing the model more time to think","I need to understand why the model arrived at a particular answer, not just get the final result","I'm solving problems that require exploring multiple solution paths before converging on the best one"],"best_for":["researchers and engineers solving complex reasoning tasks (mathematics, physics, algorithm design)","developers building AI systems that need interpretable decision-making","teams working on code generation and debugging where reasoning transparency matters","applications requiring high accuracy on multi-step logical inference"],"limitations":["Extended thinking increases latency significantly — responses may take 10-60+ seconds depending on problem complexity and allocated thinking budget","Thinking tokens are billed separately and at higher rates than standard tokens, increasing per-request costs for complex problems","No streaming support for thinking phase — full response must complete before any output is available to the client","Thinking budget must be specified upfront; dynamic allocation based on problem difficulty is not supported","Output is deterministic within a session but reasoning paths may vary across identical queries due to RL training"],"requires":["OpenAI API key with o3-pro model access (requires paid tier)","HTTP client supporting long-lived connections (30+ second timeouts recommended)","Understanding of thinking vs output token accounting for cost estimation","Familiarity with structured prompt design to guide reasoning effectively"],"input_types":["text (natural language problem statements, code snippets, mathematical expressions)","structured prompts with explicit reasoning instructions","multi-turn conversation context"],"output_types":["text (final answer with optional reasoning trace)","structured reasoning logs (if explicitly requested in prompt)","code solutions with explanations"],"categories":["planning-reasoning","advanced-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o3-pro__cap_1","uri":"capability://image.visual.multi.modal.input.processing.with.vision.understanding","name":"multi-modal input processing with vision understanding","description":"Accepts both text and image inputs in a single API call, processing visual content through a vision encoder that extracts semantic features before feeding them into the reasoning pipeline. The model can analyze images, diagrams, charts, and screenshots, then apply its extended reasoning capabilities to answer questions about visual content or solve problems that combine textual and visual information.","intents":["I need to analyze a screenshot or diagram and have the model reason through what it shows","I want to ask questions about images (OCR, object detection, spatial reasoning) with deep reasoning","I'm solving problems that require understanding both text descriptions and visual representations","I need the model to extract data from charts, graphs, or technical diagrams and perform calculations"],"best_for":["document analysis and data extraction from PDFs, screenshots, and scanned images","technical diagram interpretation (architecture diagrams, circuit schematics, flowcharts)","educational applications requiring visual problem-solving (geometry, chemistry, physics)","accessibility tools converting visual content to structured descriptions"],"limitations":["Image resolution is limited to ~2000x2000 pixels; larger images are automatically downsampled, potentially losing fine details","No support for video input — only static images and image sequences in multi-turn conversations","Image processing adds 500-1500ms latency before reasoning begins, on top of extended thinking time","Vision encoder is not fine-tunable; visual understanding quality is fixed by OpenAI's training","No explicit bounding box or region-of-interest selection — the model processes entire images holistically"],"requires":["OpenAI API key with vision capability enabled","Images in supported formats: JPEG, PNG, GIF, WebP (max 20MB per image)","Base64 encoding or URL hosting for image transmission via API","Understanding that vision processing is sequential with reasoning, not parallel"],"input_types":["text (questions or prompts about images)","images (JPEG, PNG, GIF, WebP formats)","mixed text-image conversations"],"output_types":["text (descriptions, answers, extracted data)","structured data (JSON with extracted information)","reasoning traces explaining visual analysis"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o3-pro__cap_2","uri":"capability://data.processing.analysis.structured.output.generation.with.schema.validation","name":"structured output generation with schema validation","description":"Supports JSON schema-based output constraints that force the model to generate responses conforming to a specified structure. The model's reasoning process is aware of the output schema, allowing it to plan solutions that fit the required format before generating. This enables reliable extraction of structured data, function arguments, or domain-specific formats without post-processing or retry logic.","intents":["I need the model to return data in a specific JSON schema that my application can parse directly","I want to extract structured information from unstructured text without writing custom parsing logic","I'm building an API where the model must return function arguments in a specific format","I need to ensure the model's output always matches my data model, with no manual validation"],"best_for":["API integrations requiring deterministic response formats","data extraction pipelines that feed into downstream systems","function-calling workflows where argument schemas must be strictly enforced","applications building on model outputs without intermediate validation layers"],"limitations":["Schema complexity is limited — deeply nested or recursive schemas may cause generation failures or timeout","The model may refuse to generate output if the schema is incompatible with the reasoning process or too constrictive","Schema validation adds 100-300ms overhead to response generation, on top of thinking time","No support for conditional schemas or dynamic schema selection based on input","Large schemas (>10KB) may reduce reasoning quality as they consume significant context budget"],"requires":["JSON Schema definition (draft 2020-12 compatible)","Understanding of schema constraints and their impact on model behavior","API integration code to pass schema parameter in request","Fallback handling for cases where schema validation fails"],"input_types":["text (natural language prompts)","JSON schema (as parameter, not input)"],"output_types":["JSON (strictly conforming to provided schema)","structured data with guaranteed format"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o3-pro__cap_3","uri":"capability://planning.reasoning.multi.turn.conversation.with.persistent.reasoning.context","name":"multi-turn conversation with persistent reasoning context","description":"Maintains conversation history across multiple turns, with each turn's reasoning and output contributing to the model's understanding of subsequent queries. The model can reference previous reasoning steps, correct earlier conclusions, and build on prior analysis without requiring explicit context injection. Thinking tokens are computed per-turn, allowing the model to allocate reasoning budget based on conversation state.","intents":["I want to have a back-and-forth conversation where the model remembers and builds on previous reasoning","I need to ask follow-up questions that reference earlier analysis without repeating context","I'm iteratively refining a solution and want the model to learn from corrections across turns","I'm debugging code and need the model to maintain context about previous attempts and failures"],"best_for":["interactive problem-solving sessions (debugging, tutoring, research)","iterative design and refinement workflows","long-form content creation with multiple revision cycles","collaborative reasoning where human and AI alternate contributions"],"limitations":["Conversation history is not persisted server-side — the client must maintain and resend full history with each request","Token limits apply to total conversation length; very long conversations may exceed context windows (200K tokens for o3-pro)","Reasoning budget must be specified per-turn; the model cannot dynamically allocate thinking across turns","Earlier turns' reasoning is not explicitly accessible — only the final outputs are visible in conversation history","Cost scales with conversation length because full history is re-processed with each new turn"],"requires":["Client-side conversation state management (array of messages with roles and content)","Token counting logic to track conversation length and estimate costs","Handling of token limit errors when conversations exceed context windows","Understanding of how thinking tokens accumulate across turns"],"input_types":["text (user messages)","images (in multi-modal conversations)","conversation history (previous turns)"],"output_types":["text (assistant responses)","structured data (if schema is specified)","reasoning traces (if explicitly requested)"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o3-pro__cap_4","uri":"capability://code.generation.editing.code.generation.and.debugging.with.reasoning.guided.synthesis","name":"code generation and debugging with reasoning-guided synthesis","description":"Generates code solutions by reasoning through algorithmic approaches, edge cases, and implementation details before producing output. The model can analyze existing code, identify bugs, suggest optimizations, and generate complete implementations for complex algorithms. Reasoning is applied to understand problem constraints and design decisions before code is written, reducing hallucinations and improving correctness.","intents":["I need to generate code for a complex algorithm and want the model to reason through the approach first","I have buggy code and want the model to debug it by understanding the intended logic","I want code optimization suggestions with explanations of why changes improve performance","I'm learning to code and need the model to explain its reasoning for each implementation choice"],"best_for":["competitive programming and algorithm design","code review and debugging workflows","learning programming with detailed explanations","generating complex multi-file codebases with architectural reasoning"],"limitations":["Generated code may still contain subtle bugs despite reasoning — human review is essential for production code","Code generation latency is high (30-120 seconds) due to extended thinking, making real-time code completion impractical","The model cannot execute code or test it; correctness validation requires external test runners","Large codebases (>10K lines) may exceed context limits, requiring code to be split across multiple requests","No IDE integration or real-time feedback — code is generated in batch mode only"],"requires":["OpenAI API key with o3-pro access","Code context (existing code, requirements, test cases) provided as text input","External test framework or execution environment to validate generated code","Understanding that reasoning time is proportional to code complexity"],"input_types":["text (problem descriptions, algorithm specifications)","code (existing implementations to debug or refactor)","test cases (to guide code generation)"],"output_types":["code (in any programming language)","explanations (reasoning about implementation choices)","bug reports (with fixes and explanations)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o3-pro__cap_5","uri":"capability://planning.reasoning.mathematical.problem.solving.with.step.by.step.verification","name":"mathematical problem solving with step-by-step verification","description":"Solves mathematical problems by reasoning through problem decomposition, intermediate calculations, and solution verification. The model can handle algebra, calculus, number theory, combinatorics, and applied mathematics by explicitly working through each step. Reasoning allows the model to catch calculation errors and verify solutions before output, improving accuracy on complex multi-step problems.","intents":["I need to solve a complex math problem and want to see all intermediate steps","I want the model to verify its own calculations and catch errors before giving the final answer","I'm teaching math and need detailed explanations of solution approaches","I need to solve optimization or constraint satisfaction problems with reasoning about trade-offs"],"best_for":["educational applications and tutoring systems","research requiring symbolic computation and mathematical reasoning","competitive mathematics (AMC, AIME, IMO preparation)","engineering and physics problem-solving"],"limitations":["The model cannot perform arbitrary symbolic computation — it works with numerical approximations and symbolic reasoning, not computer algebra systems","Very large numbers or high-precision calculations may lose accuracy despite reasoning","The model cannot generate plots or visualizations — output is text-based","Reasoning time scales with problem complexity; some problems may timeout if thinking budget is insufficient","No integration with external math libraries or solvers — all computation is done by the model"],"requires":["OpenAI API key with o3-pro access","Mathematical notation in text form (LaTeX or plain text)","Understanding that reasoning time is proportional to problem complexity","Verification of results for critical applications (the model can make mistakes despite reasoning)"],"input_types":["text (mathematical problem statements)","LaTeX notation (for complex equations)","numerical data (for applied math problems)"],"output_types":["text (step-by-step solutions)","numerical answers (with intermediate calculations)","reasoning traces (showing verification steps)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o3-pro__cap_6","uri":"capability://planning.reasoning.complex.reasoning.with.uncertainty.quantification","name":"complex reasoning with uncertainty quantification","description":"Provides confidence assessments and uncertainty estimates alongside reasoning outputs, allowing the model to explicitly acknowledge when it is less certain about conclusions. The reasoning phase includes exploration of alternative interpretations and confidence in different solution paths, which can be surfaced to the user. This enables better decision-making when the model's output will be used in high-stakes contexts.","intents":["I need the model to tell me how confident it is in its answer, not just give a single response","I want to understand which parts of the reasoning are uncertain or have multiple valid interpretations","I'm using the model for decision-support and need to know when to seek human review","I want to identify edge cases or alternative solutions that the model considered"],"best_for":["decision-support systems where confidence matters","research and analysis where uncertainty quantification is important","high-stakes applications (medical, legal, financial) requiring explicit confidence assessment","systems that need to route uncertain queries to human experts"],"limitations":["Confidence estimates are implicit in reasoning traces, not explicitly quantified as probabilities — extraction requires parsing reasoning output","The model may be overconfident or underconfident despite reasoning; confidence is not calibrated to actual accuracy","Extracting uncertainty information requires additional prompt engineering or post-processing of reasoning traces","No formal Bayesian uncertainty quantification — confidence is qualitative, not quantitative","Reasoning traces can be very long (10K+ tokens), making uncertainty extraction computationally expensive"],"requires":["OpenAI API key with o3-pro access","Prompt engineering to explicitly request confidence assessment","Post-processing logic to extract uncertainty from reasoning traces","Understanding that confidence estimates are heuristic, not formally calibrated"],"input_types":["text (questions or problems where uncertainty matters)","prompts requesting explicit confidence assessment"],"output_types":["text (answers with confidence qualifiers)","reasoning traces (showing alternative interpretations)","structured confidence assessments (if explicitly requested)"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o3-pro__cap_7","uri":"capability://tool.use.integration.api.based.inference.with.usage.tracking.and.cost.estimation","name":"api-based inference with usage tracking and cost estimation","description":"Exposes o3-pro through OpenAI's REST API with detailed token accounting that separates thinking tokens from output tokens. Clients can track usage in real-time, estimate costs before making requests, and optimize spending by adjusting thinking budget. The API returns detailed metadata about token consumption, allowing builders to understand the cost-benefit trade-off of extended reasoning.","intents":["I need to integrate o3-pro into my application via API and track costs per request","I want to estimate the cost of a query before submitting it to avoid surprises","I need to optimize my application's spending by adjusting reasoning budget based on problem complexity","I'm building a service that charges users for AI reasoning and need accurate cost tracking"],"best_for":["SaaS applications monetizing AI reasoning","cost-conscious teams needing detailed usage analytics","applications with variable reasoning requirements (some queries need more thinking than others)","enterprise deployments requiring cost allocation and chargeback"],"limitations":["Thinking tokens are billed at a higher rate than output tokens (typically 3-5x), making cost estimation complex","No pre-execution cost estimation — costs are only known after the request completes","API rate limits apply; high-volume applications may need to implement queuing and backoff logic","No built-in cost controls or spending caps — applications must implement their own budget management","Token counting is approximate for input; actual usage may vary by 5-10% due to tokenization edge cases"],"requires":["OpenAI API key with billing enabled","HTTP client library (Python, Node.js, Go, etc.)","Token counting library to estimate costs before requests (optional but recommended)","Cost tracking and analytics infrastructure for production deployments"],"input_types":["JSON request bodies with model, messages, and thinking budget parameters"],"output_types":["JSON responses with usage metadata (thinking_tokens, output_tokens, total_tokens)","structured data (if schema is specified)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["OpenAI API key with o3-pro model access (requires paid tier)","HTTP client supporting long-lived connections (30+ second timeouts recommended)","Understanding of thinking vs output token accounting for cost estimation","Familiarity with structured prompt design to guide reasoning effectively","OpenAI API key with vision capability enabled","Images in supported formats: JPEG, PNG, GIF, WebP (max 20MB per image)","Base64 encoding or URL hosting for image transmission via API","Understanding that vision processing is sequential with reasoning, not parallel","JSON Schema definition (draft 2020-12 compatible)","Understanding of schema constraints and their impact on model behavior"],"failure_modes":["Extended thinking increases latency significantly — responses may take 10-60+ seconds depending on problem complexity and allocated thinking budget","Thinking tokens are billed separately and at higher rates than standard tokens, increasing per-request costs for complex problems","No streaming support for thinking phase — full response must complete before any output is available to the client","Thinking budget must be specified upfront; 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