{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"o1","slug":"o1","name":"o1","type":"model","url":"https://openai.com/index/introducing-openai-o1-preview/","page_url":"https://unfragile.ai/o1","categories":["llm-apis"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"o1__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 a two-phase inference architecture where the model allocates additional compute tokens (called 'thinking tokens') to internal reasoning before generating a response. During the thinking phase, the model performs multi-step chain-of-thought reasoning without user visibility, then synthesizes conclusions into a final answer. This is distinct from standard prompt-based CoT because the reasoning is native to the model's inference process rather than instructed via prompts, enabling the model to dynamically allocate compute based on problem complexity.","intents":["I need to solve a complex multi-step math problem and want the model to show its work internally before answering","I'm working on a physics or chemistry problem that requires deep reasoning about physical principles and want higher accuracy than standard models","I need the model to spend more computational effort on hard problems and less on easy ones, rather than using fixed reasoning budgets"],"best_for":["researchers and engineers solving physics, chemistry, and mathematics problems requiring multi-step reasoning","competitive programmers debugging complex algorithmic solutions","teams building reasoning-heavy AI systems where accuracy matters more than latency"],"limitations":["Significantly higher latency than standard models — thinking phase adds 10-60+ seconds per request depending on problem complexity","Thinking tokens are not visible to users by default, limiting transparency into the reasoning process","Extended thinking capability only available in preview/limited access, not yet in production API","Higher token consumption and API costs due to internal reasoning token overhead","May over-allocate compute to simple problems, reducing efficiency on straightforward queries"],"requires":["OpenAI API access with o1 model availability (currently preview/limited)","Acceptance of significantly longer response times (10-120 seconds typical)","Budget for higher token consumption per request"],"input_types":["text","code snippets","mathematical notation","scientific problem descriptions"],"output_types":["text","code","mathematical proofs","structured reasoning explanations"],"categories":["planning-reasoning","inference-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o1__cap_1","uri":"capability://planning.reasoning.phd.level.scientific.problem.solving.across.physics.chemistry.and.biology","name":"phd-level scientific problem solving across physics, chemistry, and biology","description":"Achieves expert-level performance on scientific reasoning tasks through training on domain-specific reasoning patterns and scientific knowledge. The model demonstrates understanding of physical principles, chemical reactions, biological systems, and can solve multi-step scientific problems that require integrating knowledge across domains. This capability emerges from the extended reasoning architecture combined with training data that emphasizes scientific problem-solving patterns.","intents":["I need to solve a complex physics problem involving multiple physical principles and want answers at PhD-level accuracy","I'm designing a chemical synthesis pathway and need the model to reason about reaction mechanisms and selectivity","I need help understanding biological systems and predicting outcomes of biological processes"],"best_for":["academic researchers and graduate students in STEM fields","pharmaceutical and materials science companies designing new compounds","science education platforms building tutoring systems for advanced students"],"limitations":["Performance is benchmark-dependent — excels on structured problems like IMO but may struggle with novel or highly specialized subdomains","Requires clear problem statements; ambiguous or poorly-specified scientific questions may receive lower-quality reasoning","No real-time experimental validation — cannot verify predictions against live lab data","Knowledge cutoff limits ability to reason about very recent scientific discoveries or emerging fields"],"requires":["OpenAI API access with o1 model","Clear, well-formulated scientific problem statements","Understanding of the model's limitations in novel domains"],"input_types":["text descriptions of scientific problems","mathematical equations","chemical formulas and reaction descriptions","code for scientific simulations"],"output_types":["text explanations of scientific reasoning","mathematical derivations","chemical mechanism explanations","code for simulations or calculations"],"categories":["planning-reasoning","domain-expertise"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o1__cap_2","uri":"capability://code.generation.editing.competitive.programming.problem.solving.with.algorithmic.reasoning","name":"competitive programming problem solving with algorithmic reasoning","description":"Solves complex algorithmic and competitive programming problems by reasoning through algorithm design, complexity analysis, and edge case handling. The model achieves 89th percentile on Codeforces (a major competitive programming platform), indicating it can handle problems requiring novel algorithmic insights, optimization techniques, and careful implementation. The extended thinking capability enables the model to explore multiple algorithmic approaches before settling on a solution.","intents":["I need to solve a Codeforces-style algorithmic problem and want the model to reason through the algorithm design","I'm stuck on a competitive programming problem and need help thinking through the approach without just getting the answer","I want to understand why a particular algorithmic approach works for a complex problem"],"best_for":["competitive programmers training for contests","software engineers preparing for technical interviews with algorithmic problems","computer science educators building problem-solving tutoring systems"],"limitations":["Performance varies by problem type — excels on algorithmic reasoning but may struggle with problems requiring deep domain knowledge","Cannot execute code to verify solutions in real-time; relies on reasoning correctness","May not handle very recent contest problems if they fall outside training data","Latency (10-60+ seconds) makes it unsuitable for real-time contest environments"],"requires":["OpenAI API access with o1 model","Clear problem statement in text or code format","Patience for extended reasoning time"],"input_types":["text problem descriptions","code snippets","mathematical problem formulations","input/output examples"],"output_types":["code solutions","algorithmic explanations","complexity analysis","step-by-step reasoning"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o1__cap_3","uri":"capability://memory.knowledge.200k.context.window.with.extended.thinking.token.management","name":"200k context window with extended thinking token management","description":"Provides a 200,000 token context window that can accommodate large codebases, long documents, or extensive conversation histories. The model manages both regular tokens and extended thinking tokens within this window, allowing developers to include substantial context while reserving compute budget for reasoning. The context window is implemented as a standard transformer attention mechanism but with optimizations for handling the extended token sequence length.","intents":["I need to analyze a large codebase (10K+ lines) and want the model to understand the full architecture","I'm working with a long document or research paper and want the model to reason about the entire content","I want to maintain a long conversation history while still having room for the model's extended thinking"],"best_for":["developers working with large codebases who need full-context code analysis","researchers analyzing long documents or papers","teams building multi-turn reasoning systems with extensive conversation history"],"limitations":["200K tokens is still finite — very large codebases (100K+ lines) may exceed the window","Extended thinking tokens consume part of the context window, reducing available space for input context","Latency increases with context length — processing 200K tokens takes significantly longer than 4K or 8K","Token pricing scales with context length, making large-context requests expensive"],"requires":["OpenAI API access with o1 model","Understanding of token counting and context window management","Budget for higher token consumption"],"input_types":["text","code","documents","conversation histories"],"output_types":["text","code","analysis","reasoning explanations"],"categories":["memory-knowledge","context-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o1__cap_4","uri":"capability://planning.reasoning.multi.step.mathematical.proof.generation.and.verification","name":"multi-step mathematical proof generation and verification","description":"Generates rigorous mathematical proofs by reasoning through logical steps, applying theorems, and verifying intermediate results. The model can work with formal mathematical notation, symbolic reasoning, and complex proof structures. The extended thinking capability enables the model to explore proof strategies, backtrack when approaches fail, and synthesize elegant proofs. This is implemented through training on mathematical reasoning patterns and the native chain-of-thought architecture.","intents":["I need to prove a mathematical theorem and want the model to generate a rigorous, step-by-step proof","I'm stuck on a proof and want the model to help me think through the logical structure","I want to verify that a mathematical proof is correct by having the model check each step"],"best_for":["mathematicians and graduate students working on proofs","educators building proof-checking systems","researchers in formal verification and theorem proving"],"limitations":["Cannot formally verify proofs against a proof assistant (e.g., Lean, Coq) — relies on reasoning correctness","May generate proofs that are logically sound but not elegant or optimal","Struggles with proofs requiring deep domain knowledge in specialized mathematical fields","Cannot handle interactive proof development or real-time feedback from proof assistants"],"requires":["OpenAI API access with o1 model","Mathematical problem statement in clear notation","Understanding of mathematical logic and proof structure"],"input_types":["text descriptions of theorems","mathematical notation","formal problem statements","partial proofs to complete"],"output_types":["text proofs","mathematical notation","step-by-step reasoning","formal proof structures"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o1__cap_5","uri":"capability://code.generation.editing.code.debugging.and.correctness.reasoning.with.multi.file.context","name":"code debugging and correctness reasoning with multi-file context","description":"Analyzes code to identify bugs, reason about correctness, and suggest fixes by understanding program semantics and execution flow. The model can work with multi-file codebases (within the 200K context window) and reason about how changes in one file affect others. Debugging is performed through logical reasoning about code behavior rather than execution, enabling the model to catch subtle bugs that require understanding of language semantics and algorithm correctness.","intents":["I have a bug in my code and want the model to reason through the execution flow to find the root cause","I'm working with a multi-file codebase and need to understand how a bug in one file affects others","I want the model to verify that my code is correct before I run it"],"best_for":["software engineers debugging complex codebases","teams building code review systems","developers working on performance-critical code where bugs are expensive"],"limitations":["Cannot execute code to verify fixes — relies on reasoning correctness","May miss bugs that only manifest under specific runtime conditions or edge cases","Performance analysis is approximate — cannot provide exact timing without execution","Struggles with bugs in unfamiliar languages or frameworks outside training data"],"requires":["OpenAI API access with o1 model","Code in a supported programming language","Clear description of the bug or unexpected behavior"],"input_types":["code","error messages","test cases","execution traces"],"output_types":["bug explanations","corrected code","reasoning about execution flow","suggested fixes"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o1__cap_6","uri":"capability://planning.reasoning.structured.problem.decomposition.and.solution.planning","name":"structured problem decomposition and solution planning","description":"Breaks down complex problems into sub-problems, plans solution strategies, and reasons about dependencies between steps. The model uses the extended thinking phase to explore different decomposition strategies and select the most effective approach. This capability is fundamental to the model's reasoning architecture — the thinking phase is essentially a planning and decomposition process that happens before the final response.","intents":["I have a complex problem and want the model to break it down into manageable sub-problems","I need help planning the approach to a multi-step project or research problem","I want the model to reason about dependencies between different parts of a solution"],"best_for":["project managers and engineers planning complex technical projects","researchers designing multi-phase research plans","teams building planning and reasoning systems"],"limitations":["Decomposition is based on reasoning, not execution — may miss practical constraints that only emerge during implementation","Cannot adapt plans based on real-time feedback or changing conditions","May over-decompose simple problems, adding unnecessary complexity","Reasoning about dependencies is approximate and may miss subtle interactions"],"requires":["OpenAI API access with o1 model","Clear problem statement","Acceptance of extended latency for planning"],"input_types":["text problem descriptions","project specifications","research questions","code architecture questions"],"output_types":["decomposed sub-problems","solution plans","dependency analysis","step-by-step approaches"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o1__cap_7","uri":"capability://planning.reasoning.variable.latency.inference.with.adaptive.compute.allocation","name":"variable latency inference with adaptive compute allocation","description":"Allocates compute dynamically based on problem complexity, spending more thinking tokens on harder problems and fewer on simpler ones. The model estimates problem difficulty and adjusts the reasoning phase duration accordingly, resulting in variable latency (5-30 seconds) depending on problem complexity. This adaptive allocation improves efficiency compared to fixed-latency approaches.","intents":["get faster responses for simple problems while maintaining quality for complex ones","understand that latency varies based on problem difficulty","build systems that can tolerate variable response times","optimize cost by using less compute for simpler problems"],"best_for":["asynchronous systems that can tolerate variable latency","batch processing systems where latency variation is acceptable","teams building research tools where latency is not critical"],"limitations":["variable latency makes it unsuitable for real-time applications","no way to request fixed latency or maximum thinking time","latency is not predictable, making SLA guarantees difficult","users cannot control or observe the compute allocation strategy"],"requires":["OpenAI API key with o1 model access","asynchronous infrastructure that can handle variable response times","understanding that latency ranges from 5-30 seconds"],"input_types":["text (any problem statement)","code (any code-related task)","structured data (any structured problem)"],"output_types":["text (solutions with variable latency)","code (generated code with variable latency)","structured data (analysis with variable latency)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o1__cap_8","uri":"capability://tool.use.integration.api.based.access.with.streaming.and.batch.processing","name":"api-based access with streaming and batch processing","description":"Provides access to the o1 model through OpenAI's REST API with support for both streaming and batch processing modes. Developers can integrate o1 into applications via standard HTTP requests, with SDKs available for Python, Node.js, and other languages. Batch processing enables cost-optimized processing of multiple problems asynchronously.","intents":["integrate o1 reasoning capabilities into existing applications","build batch processing pipelines for large-scale problem solving","stream responses for real-time display of solutions","automate problem solving workflows using standard API patterns"],"best_for":["developers building applications that need reasoning capabilities","teams processing large batches of problems","organizations integrating AI into existing workflows"],"limitations":["API latency adds 5-30 seconds per request on top of model latency","streaming is not supported for thinking tokens, only final responses","batch processing has delayed results (not real-time)","API rate limits may restrict throughput for high-volume applications"],"requires":["OpenAI API key with o1 model access","HTTP client or OpenAI SDK (Python 1.0+, Node.js 4.0+, etc.)","understanding of API authentication and error handling"],"input_types":["text (JSON-formatted API requests)","code (in API request format)","structured data (in JSON format)"],"output_types":["text (JSON-formatted API responses)","code (in API response format)","structured data (in JSON format)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o1__headline","uri":"capability://planning.reasoning.advanced.reasoning.model.for.complex.problem.solving","name":"advanced reasoning model for complex problem solving","description":"OpenAI's first reasoning model that excels in multi-step problem solving using chain-of-thought techniques, achieving high performance in STEM benchmarks and competitive programming.","intents":["best reasoning model","reasoning model for complex problems","AI model for STEM subjects","high-performance reasoning model","advanced LLM for multi-step reasoning"],"best_for":["STEM education","competitive programming","complex problem-solving"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":54,"verified":false,"data_access_risk":"high","permissions":["OpenAI API access with o1 model availability (currently preview/limited)","Acceptance of significantly longer response times (10-120 seconds typical)","Budget for higher token consumption per request","OpenAI API access with o1 model","Clear, well-formulated scientific problem statements","Understanding of the model's limitations in novel domains","Clear problem statement in text or code format","Patience for extended reasoning time","Understanding of token counting and context window management","Budget for higher token consumption"],"failure_modes":["Significantly higher latency than standard models — thinking phase adds 10-60+ seconds per request depending on problem complexity","Thinking tokens are not visible to users by default, limiting transparency into the reasoning process","Extended thinking capability only available in preview/limited access, not yet in production API","Higher token consumption and API costs due to internal reasoning token overhead","May over-allocate compute to simple problems, reducing efficiency on straightforward queries","Performance is benchmark-dependent — excels on structured problems like IMO but may struggle with novel or highly specialized subdomains","Requires clear problem statements; ambiguous or poorly-specified scientific questions may receive lower-quality reasoning","No real-time experimental validation — cannot verify predictions against live lab data","Knowledge cutoff limits ability to reason about very recent scientific discoveries or emerging fields","Performance varies by problem type — excels on algorithmic reasoning but may struggle with problems requiring deep domain knowledge","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.8500000000000001,"ecosystem":0.15000000000000002,"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":"2026-05-24T12:16:24.483Z","last_scraped_at":null,"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=o1","compare_url":"https://unfragile.ai/compare?artifact=o1"}},"signature":"jXK1wuAsWr2C+wqfTqtmHJR03zBm2J8F07FZ/ESFEtP/caDo1GcO018llkttZJfePGQR68YivKlFev5pwWzbDA==","signedAt":"2026-06-21T06:22:12.930Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/o1","artifact":"https://unfragile.ai/o1","verify":"https://unfragile.ai/api/v1/verify?slug=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"}}