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The model learns to spend more computational budget on harder problems through RLHF training, enabling deeper exploration of complex logical, mathematical, and algorithmic problems before committing to an answer.","intents":["I need to solve a complex multi-step math or logic problem with verified reasoning","I want the model to show its work and explore edge cases before answering","I need to handle problems that require deep reasoning over multiple domains","I want higher accuracy on hard reasoning tasks even if response time increases"],"best_for":["researchers and engineers solving complex algorithmic problems","teams building reasoning-heavy AI applications (theorem proving, formal verification)","developers needing high-confidence answers on ambiguous or multi-step problems"],"limitations":["Significantly higher latency than standard models (30-120 seconds typical for complex problems vs 1-5 seconds for GPT-4)","Higher token consumption and API costs due to extended reasoning tokens not visible to user","Reasoning process is opaque — internal chain-of-thought not exposed or controllable by users","Not optimized for real-time applications or high-throughput inference","Reasoning budget allocation is automatic and non-configurable"],"requires":["OpenAI API key with o1 model access","Patience for 30-120 second response times on complex queries","Understanding that costs scale with reasoning complexity, not just output length"],"input_types":["text","natural language problem descriptions","code snippets for debugging or optimization","mathematical problem statements"],"output_types":["text","reasoning-validated solutions","code with explanations","mathematical proofs or derivations"],"categories":["planning-reasoning","advanced-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o1__cap_1","uri":"capability://planning.reasoning.multi.domain.complex.problem.decomposition","name":"multi-domain-complex-problem-decomposition","description":"Leverages reinforcement-learning-trained reasoning to automatically decompose complex problems spanning multiple domains (mathematics, physics, coding, logic) into sub-problems, solve each with domain-specific reasoning patterns, and synthesize solutions. The model learns through RLHF which decomposition strategies lead to correct answers, enabling it to handle problems that require reasoning across traditionally separate domains.","intents":["I need to solve a physics problem that requires both calculus and coding simulation","I want the model to break down an ambiguous real-world problem into solvable sub-tasks","I need to verify a complex system design that spans multiple technical domains","I want reasoning that crosses between abstract theory and practical implementation"],"best_for":["academic researchers and students tackling interdisciplinary problems","engineers designing complex systems requiring multi-domain validation","teams building AI systems that need to reason about hybrid problems"],"limitations":["Decomposition strategy is learned but not explicitly controllable or inspectable","May over-decompose simple problems, adding unnecessary latency","No guarantee of optimal decomposition — learned heuristics may miss better strategies","Reasoning across domains is probabilistic, not deterministic"],"requires":["OpenAI API key with o1 access","Problems that genuinely require multi-domain reasoning (simple single-domain problems won't benefit)"],"input_types":["text","problem statements mixing multiple technical domains","code with mathematical constraints","physics/engineering specifications"],"output_types":["text","structured solutions with sub-problem breakdowns","code with mathematical validation","reasoning traces across domains"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o1__cap_2","uri":"capability://code.generation.editing.code.generation.with.formal.verification.reasoning","name":"code-generation-with-formal-verification-reasoning","description":"Generates code while internally reasoning about correctness, edge cases, and potential bugs through extended chain-of-thought before producing output. The model explores multiple implementation approaches and validates logic against problem constraints during the reasoning phase, producing code with higher correctness rates on complex algorithmic problems. Integration via OpenAI API accepts code problem descriptions and returns verified implementations.","intents":["I need to generate code for a complex algorithm and want the model to verify correctness before responding","I want code that handles edge cases without requiring extensive manual testing","I need to implement a solution to a competitive programming or interview-style problem","I want to catch bugs in my code logic before deployment"],"best_for":["competitive programmers and interview candidates","teams building safety-critical algorithms","developers working on complex data structure implementations"],"limitations":["Reasoning time adds 30-120 seconds latency — not suitable for real-time code generation","Verification reasoning is internal and not exposed — can't inspect why a particular approach was chosen","Higher API costs due to hidden reasoning tokens","Still produces occasional bugs on extremely novel or domain-specific problems"],"requires":["OpenAI API key with o1 model access","Clear problem specification (ambiguous requirements reduce reasoning effectiveness)","Tolerance for 30-120 second generation times"],"input_types":["text","algorithm problem descriptions","code snippets to debug or optimize","specification of constraints and edge cases"],"output_types":["code","implementation with comments","code with test case validation"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o1__cap_3","uri":"capability://planning.reasoning.mathematical.reasoning.and.proof.generation","name":"mathematical-reasoning-and-proof-generation","description":"Applies extended reasoning to mathematical problem-solving, including symbolic manipulation, proof construction, and numerical validation. The model learns through RLHF to apply appropriate mathematical techniques (induction, contradiction, calculus, linear algebra) and verify intermediate steps before producing final answers. Integrates via OpenAI API to accept mathematical problem statements and return step-by-step solutions with reasoning.","intents":["I need to solve a multi-step calculus or linear algebra problem with verified steps","I want to generate a mathematical proof with intermediate step validation","I need to verify that a mathematical derivation is correct","I want to understand the reasoning behind a complex mathematical solution"],"best_for":["mathematics students and educators","researchers needing symbolic computation and proof verification","teams building mathematical reasoning systems or tutoring platforms"],"limitations":["Reasoning is opaque — can't inspect which mathematical technique was chosen or why","High latency (30-120 seconds) makes it unsuitable for real-time tutoring or interactive problem-solving","Symbolic computation is limited compared to specialized tools like Mathematica or Sage","Proofs may be correct but not elegant or pedagogically clear"],"requires":["OpenAI API key with o1 access","Well-specified mathematical problems (ambiguous notation or missing context reduces effectiveness)","Acceptance of 30-120 second response times"],"input_types":["text","mathematical problem statements","equations and constraints","proof sketches to complete or verify"],"output_types":["text","step-by-step mathematical solutions","formal proofs","derivations with intermediate validation"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o1__cap_4","uri":"capability://planning.reasoning.long.context.reasoning.over.extended.documents","name":"long-context-reasoning-over-extended-documents","description":"Processes extended text contexts (up to model's maximum token limit) while applying reasoning to understand relationships, contradictions, and implications across the full document. The model uses learned reasoning patterns to identify relevant sections, synthesize information across distant parts of the context, and reason about document structure. Integrates via OpenAI API to accept long documents and reasoning queries.","intents":["I need to analyze a long research paper or technical document and answer complex questions about it","I want to identify contradictions or inconsistencies across a long document","I need to synthesize information from multiple sections of a lengthy specification","I want to reason about how changes in one section affect other parts of a document"],"best_for":["researchers analyzing long academic papers or technical specifications","legal professionals reviewing lengthy contracts or regulatory documents","teams building document analysis systems requiring deep reasoning"],"limitations":["Latency scales with document length and reasoning complexity (can exceed 2 minutes for very long documents)","Reasoning process is opaque — can't see which sections were prioritized or how synthesis occurred","Token costs are high due to both input length and hidden reasoning tokens","May miss subtle relationships in extremely long or complex documents"],"requires":["OpenAI API key with o1 access","Documents within model's context window (exact limit depends on o1 variant)","Tolerance for high latency and API costs"],"input_types":["text","long documents (research papers, specifications, contracts)","reasoning queries about document content"],"output_types":["text","analysis with cross-document synthesis","answers grounded in specific document sections","reasoning about document structure and relationships"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o1__cap_5","uri":"capability://planning.reasoning.adversarial.reasoning.and.edge.case.exploration","name":"adversarial-reasoning-and-edge-case-exploration","description":"During extended reasoning, the model explores potential edge cases, adversarial inputs, and failure modes before responding. The RLHF training teaches the model to consider 'what could go wrong' and validate solutions against edge cases, producing more robust answers. This is particularly effective for security-sensitive code, mathematical proofs, and system design where edge cases are critical.","intents":["I need to generate code that handles edge cases and potential security issues","I want to verify a system design by exploring failure modes and edge cases","I need to find bugs or vulnerabilities in my code before deployment","I want to ensure a mathematical proof is complete and handles all cases"],"best_for":["security engineers and teams building security-critical systems","developers writing production code that must handle edge cases","researchers verifying proofs and formal specifications"],"limitations":["Edge case exploration is learned heuristically, not exhaustive — may miss rare edge cases","Reasoning about edge cases adds latency (30-120 seconds typical)","Can't guarantee all edge cases are found — still requires manual testing and review","Over-exploration of edge cases on simple problems adds unnecessary latency"],"requires":["OpenAI API key with o1 access","Clear specification of what constitutes an edge case or failure mode","Acceptance of higher latency for more robust solutions"],"input_types":["text","code to analyze for edge cases","system specifications to verify","problem statements with constraints"],"output_types":["text","code with edge case handling","analysis of potential failure modes","robust solutions with validation"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o1__cap_6","uri":"capability://tool.use.integration.api.based.inference.with.streaming.reasoning.tokens","name":"api-based-inference-with-streaming-reasoning-tokens","description":"Exposes o1 reasoning capabilities through OpenAI's REST API with support for streaming reasoning tokens (in preview/beta), allowing developers to integrate extended reasoning into applications. The API accepts standard chat completion requests and returns responses with internal reasoning tokens optionally exposed for transparency. Supports both synchronous and asynchronous inference patterns with configurable reasoning budgets (in some variants).","intents":["I want to integrate o1 reasoning into my application via API","I need to stream reasoning tokens to show users the model's thinking process","I want to build a reasoning-powered chatbot or agent","I need to call o1 from my backend service with standard API patterns"],"best_for":["developers building reasoning-powered applications","teams integrating o1 into existing LLM pipelines","builders creating transparency-focused AI products"],"limitations":["Streaming reasoning tokens are in preview/beta — API contract may change","Higher costs than standard models due to reasoning token consumption","Reasoning budget is not user-configurable in most variants","API rate limits may be stricter than standard models due to computational cost","No local inference option — all computation happens on OpenAI servers"],"requires":["OpenAI API key with o1 model access","Python 3.7+ or Node.js 14+ (for official SDKs)","HTTP client library for REST API calls","Understanding of OpenAI's chat completion API format"],"input_types":["text","chat messages in OpenAI format","system prompts and user queries"],"output_types":["text","streaming tokens (including reasoning tokens in preview)","structured API responses with usage metadata"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-o1__cap_7","uri":"capability://planning.reasoning.multi.turn.conversation.with.persistent.reasoning.context","name":"multi-turn-conversation-with-persistent-reasoning-context","description":"Maintains reasoning context across multiple conversation turns, allowing the model to build on previous reasoning and avoid re-deriving conclusions. Each turn applies extended reasoning to new queries while leveraging learned patterns from prior turns. The API maintains conversation history and applies reasoning to understand how new queries relate to previous context.","intents":["I want to have a multi-turn conversation where the model remembers and builds on previous reasoning","I need to iteratively refine a solution through multiple rounds of reasoning","I want to ask follow-up questions that build on previous complex reasoning","I need a reasoning-powered chatbot that maintains context across turns"],"best_for":["teams building interactive reasoning assistants","developers creating iterative problem-solving applications","users needing to refine solutions through multiple rounds of reasoning"],"limitations":["Latency compounds across turns — each turn adds 30-120 seconds","Reasoning context is not explicitly controllable — model decides what to carry forward","Token costs accumulate across turns due to context window growth","May re-derive conclusions from earlier turns if context becomes too long"],"requires":["OpenAI API key with o1 access","Application logic to maintain conversation history","Tolerance for cumulative latency across multiple turns"],"input_types":["text","chat messages in OpenAI format","follow-up queries building on previous context"],"output_types":["text","responses that reference and build on previous reasoning","iteratively refined solutions"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"low","permissions":["OpenAI API key with o1 model access","Patience for 30-120 second response times on complex queries","Understanding that costs scale with reasoning complexity, not just output length","OpenAI API key with o1 access","Problems that genuinely require multi-domain reasoning (simple single-domain problems won't benefit)","Clear problem specification (ambiguous requirements reduce reasoning effectiveness)","Tolerance for 30-120 second generation times","Well-specified mathematical problems (ambiguous notation or missing context reduces effectiveness)","Acceptance of 30-120 second response times","Documents within model's context window (exact limit depends on o1 variant)"],"failure_modes":["Significantly higher latency than standard models (30-120 seconds typical for complex problems vs 1-5 seconds for GPT-4)","Higher token consumption and API costs due to extended reasoning tokens not visible to user","Reasoning process is opaque — internal chain-of-thought not exposed or controllable by users","Not optimized for real-time applications or high-throughput inference","Reasoning budget allocation is automatic and non-configurable","Decomposition strategy is learned but not explicitly controllable or inspectable","May over-decompose simple problems, adding unnecessary latency","No guarantee of optimal decomposition — learned heuristics may miss better strategies","Reasoning across domains is probabilistic, not deterministic","Reasoning time adds 30-120 seconds latency — not suitable for real-time code generation","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":0.75,"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-o1","compare_url":"https://unfragile.ai/compare?artifact=openai-o1"}},"signature":"eR9OQKbmQLMDloJIrZEGVye7JTETwubPf3tmN03WnFaBzqCJNnFiUODvBX58EkZOcZLrYWqkGpMqehpxhhhiCg==","signedAt":"2026-06-22T04:12:54.202Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/openai-o1","artifact":"https://unfragile.ai/openai-o1","verify":"https://unfragile.ai/api/v1/verify?slug=openai-o1","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"}}