{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"o3-mini","slug":"o3-mini","name":"o3-mini","type":"model","url":"https://openai.com/index/openai-o3-mini/","page_url":"https://unfragile.ai/o3-mini","categories":["llm-apis"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"o3-mini__cap_0","uri":"capability://planning.reasoning.multi.level.reasoning.with.configurable.compute.budgets","name":"multi-level reasoning with configurable compute budgets","description":"Implements a three-tier reasoning architecture (low, medium, high effort) that dynamically allocates internal compute resources and chain-of-thought depth based on problem complexity. The model uses adaptive reasoning token generation where low effort constrains reasoning steps to ~1000 tokens, medium to ~5000 tokens, and high to ~10000+ tokens, allowing developers to trade latency and cost against solution quality without model switching. This is achieved through learned routing mechanisms that determine reasoning depth at inference time rather than requiring separate model checkpoints.","intents":["I need to solve complex math problems but want to control costs per API call","I want to use the same model for both quick reasoning tasks and deep analysis without switching models","I need to optimize latency for real-time applications while keeping reasoning quality for batch jobs"],"best_for":["cost-conscious teams building reasoning-heavy applications","developers building tiered service offerings with different SLA/cost tiers","applications requiring dynamic reasoning depth based on problem difficulty"],"limitations":["reasoning effort parameter is coarse-grained (3 levels only) — no fine-grained control over intermediate compute budgets","actual token consumption and latency variance between effort levels not publicly documented — requires empirical testing","low effort mode may fail on problems genuinely requiring deep reasoning, with no graceful degradation or fallback mechanism"],"requires":["OpenAI API key with o3-mini access","HTTP client supporting streaming or non-streaming responses","understanding of reasoning token vs output token pricing differences"],"input_types":["text prompts","code snippets","mathematical problem statements","scientific questions"],"output_types":["text reasoning chains","code solutions","mathematical derivations","structured explanations"],"categories":["planning-reasoning","cost-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o3-mini__cap_1","uri":"capability://memory.knowledge.extended.context.reasoning.with.200k.token.window","name":"extended context reasoning with 200k token window","description":"Supports a 200,000 token context window enabling the model to reason over large codebases, lengthy research papers, or multi-document problem sets in a single inference pass. The implementation uses efficient attention mechanisms (likely sparse or hierarchical attention patterns) to handle the extended context without quadratic memory scaling. This allows developers to include full project repositories or comprehensive reference materials without chunking or retrieval-based context management, enabling end-to-end reasoning over complex, interconnected information.","intents":["I want to analyze an entire codebase for architectural issues without splitting it across multiple API calls","I need to reason over a full research paper with citations and appendices in one pass","I want to provide comprehensive project context to get better code generation without managing context windows manually"],"best_for":["developers working with large monorepos or complex codebases","researchers analyzing multi-document datasets","teams building code review or architectural analysis tools"],"limitations":["200K token window is fixed — no option for larger contexts even with higher reasoning effort","latency scales with context size — full 200K context will incur significant inference time overhead","cost per token remains constant regardless of context utilization — padding or sparse contexts are not discounted","no built-in mechanisms for context prioritization or importance weighting — all tokens treated equally in reasoning"],"requires":["OpenAI API key with o3-mini access","tokenizer compatible with OpenAI's cl100k_base encoding","application-level context management to stay within 200K limit"],"input_types":["text documents","source code","markdown files","concatenated multi-file inputs"],"output_types":["text analysis","code suggestions","architectural recommendations","cross-file reasoning"],"categories":["memory-knowledge","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o3-mini__cap_2","uri":"capability://planning.reasoning.stem.specialized.reasoning.with.benchmark.parity.to.o3","name":"stem-specialized reasoning with benchmark parity to o3","description":"Implements domain-specific reasoning optimizations for mathematics, physics, chemistry, and computer science problems, achieving performance parity with the full o3 model on standardized STEM benchmarks (e.g., AIME, AMC, coding competitions) while using significantly fewer compute resources. The model likely uses specialized token vocabularies, problem decomposition patterns, and symbolic reasoning pathways trained on STEM-heavy datasets. This enables cost-effective deployment of reasoning capabilities for scientific and technical applications without sacrificing solution quality on domain-specific tasks.","intents":["I need to solve competition-level math problems reliably but can't afford o3 pricing","I want to build a tutoring system that explains physics and chemistry concepts with rigorous reasoning","I need to verify complex code logic and algorithmic correctness without paying for full o3 compute"],"best_for":["educational platforms and tutoring systems","competitive programming platforms","scientific research tools requiring symbolic reasoning","engineering teams needing code verification"],"limitations":["STEM specialization may degrade performance on non-technical reasoning tasks (writing, analysis, creative work)","benchmark parity claims are on specific standardized tests — real-world STEM problems may show different performance profiles","no explicit mechanism to detect when a problem falls outside STEM domain and may receive degraded reasoning","symbolic reasoning capabilities not documented — unclear which mathematical domains (e.g., abstract algebra vs calculus) receive optimization"],"requires":["OpenAI API key with o3-mini access","problems formulated in natural language or standard mathematical notation","understanding that performance varies by specific STEM subdomain"],"input_types":["mathematical problem statements","physics/chemistry questions","code snippets for verification","algorithmic challenges"],"output_types":["step-by-step solutions","code implementations","mathematical proofs","explanations with reasoning chains"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o3-mini__cap_3","uri":"capability://text.generation.language.streaming.reasoning.output.with.progressive.token.generation","name":"streaming reasoning output with progressive token generation","description":"Supports streaming of reasoning tokens and output tokens separately, allowing developers to display reasoning chains in real-time as the model computes them rather than waiting for full completion. The implementation likely buffers reasoning tokens internally during the thinking phase, then streams them to the client once the reasoning phase completes, followed by streaming of final output tokens. This enables interactive applications where users can observe the model's reasoning process, providing transparency and enabling early termination if reasoning direction appears incorrect.","intents":["I want to show users the model's reasoning process in real-time for educational transparency","I need to build interactive debugging tools where users can see how the model approaches code problems","I want to implement early stopping if the model's reasoning direction seems wrong before it completes"],"best_for":["educational and tutoring applications","interactive debugging and code review tools","applications requiring transparency into model reasoning","real-time collaborative problem-solving platforms"],"limitations":["reasoning tokens are not streamed during the thinking phase — they only become available after reasoning completes, limiting true real-time transparency","no mechanism to interrupt or redirect reasoning mid-computation — early stopping requires waiting for reasoning phase completion","streaming adds latency overhead compared to non-streaming calls due to token buffering and transmission overhead","client-side token counting required to track streaming tokens accurately — no server-side token accounting during stream"],"requires":["OpenAI API key with o3-mini access","HTTP client supporting Server-Sent Events (SSE) or chunked transfer encoding","application-level buffering to handle interleaved reasoning and output tokens","token counter compatible with cl100k_base encoding"],"input_types":["text prompts","code snippets","problem statements"],"output_types":["streaming text tokens","reasoning chain tokens","final output tokens"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o3-mini__cap_4","uri":"capability://automation.workflow.cost.optimized.inference.with.reasoning.token.pricing","name":"cost-optimized inference with reasoning token pricing","description":"Implements a dual-token pricing model where reasoning tokens (generated during the thinking phase) are priced lower than output tokens, incentivizing efficient reasoning depth allocation. The model exposes reasoning token counts in API responses, enabling developers to optimize prompts and reasoning effort levels based on actual token consumption patterns. This architecture allows fine-grained cost analysis and optimization — developers can measure the cost-benefit of increasing reasoning effort for specific problem classes and adjust tier selection accordingly.","intents":["I need to understand the actual cost breakdown of my reasoning API calls to optimize spending","I want to measure whether increasing reasoning effort is worth the cost for my specific use cases","I need to build cost-aware applications that dynamically select reasoning effort based on problem complexity and budget constraints"],"best_for":["cost-conscious teams building production reasoning applications","platforms offering tiered reasoning capabilities to end users","teams requiring detailed cost attribution and optimization","applications with variable reasoning requirements across problem types"],"limitations":["reasoning token pricing is fixed per effort level — no way to optimize reasoning efficiency beyond the three predefined tiers","no visibility into how reasoning tokens are allocated internally — developers can't optimize prompt structure to reduce reasoning token consumption","cost optimization requires empirical testing across problem classes — no predictive model for reasoning token consumption","pricing model may incentivize excessive reasoning depth if reasoning tokens are significantly cheaper than output tokens"],"requires":["OpenAI API key with o3-mini access","application-level cost tracking and logging","understanding of reasoning token vs output token pricing differences","analytics infrastructure to measure cost-performance tradeoffs"],"input_types":["any text input","code","problems"],"output_types":["API response metadata with token counts","cost attribution data","reasoning token consumption metrics"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o3-mini__cap_5","uri":"capability://code.generation.editing.code.generation.and.verification.with.reasoning.depth.control","name":"code generation and verification with reasoning depth control","description":"Generates production-quality code across multiple programming languages while leveraging configurable reasoning depth to balance code correctness against latency and cost. The model uses reasoning chains to verify algorithmic correctness, check for edge cases, and validate against common pitfalls before generating final code. Low effort mode generates straightforward implementations quickly; high effort mode performs deeper verification including complexity analysis, security checks, and alternative approaches. The implementation likely uses specialized code reasoning patterns trained on competitive programming and open-source repositories.","intents":["I need to generate correct algorithms for competitive programming without manual verification","I want to build a code generation tool that can verify correctness for safety-critical code","I need to generate code with different quality/latency tradeoffs depending on the use case"],"best_for":["competitive programming platforms","code generation tools and IDEs","educational coding platforms","teams building code review automation"],"limitations":["code generation quality varies significantly by language — best performance on Python and JavaScript, less reliable for niche languages","reasoning depth doesn't guarantee correctness — high effort mode may still generate incorrect code for novel or ambiguous problems","no built-in testing or execution — generated code requires external validation","security analysis in high effort mode is not formally verified — relies on learned patterns rather than static analysis"],"requires":["OpenAI API key with o3-mini access","target programming language specification in prompt","external testing framework for code validation"],"input_types":["natural language problem descriptions","pseudocode","algorithm specifications","code snippets to extend or refactor"],"output_types":["source code in target language","reasoning chains explaining implementation","complexity analysis","test cases"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o3-mini__cap_6","uri":"capability://planning.reasoning.mathematical.problem.solving.with.symbolic.reasoning","name":"mathematical problem solving with symbolic reasoning","description":"Solves mathematical problems ranging from algebra to calculus to discrete mathematics by performing step-by-step symbolic reasoning, deriving intermediate results, and validating solutions against constraints. The model generates explicit reasoning chains showing mathematical derivations, allowing verification of solution correctness. The implementation likely uses specialized mathematical token vocabularies and reasoning patterns trained on mathematical datasets (e.g., AIME, AMC, university-level problem sets). Reasoning effort levels control the depth of verification and alternative solution exploration.","intents":["I need to solve competition-level math problems with verified step-by-step solutions","I want to build a math tutoring system that explains solutions with rigorous reasoning","I need to verify mathematical correctness of complex derivations"],"best_for":["math tutoring and educational platforms","competitive math problem platforms","research tools requiring symbolic computation","homework help and verification systems"],"limitations":["symbolic reasoning is limited to mathematical notation — no integration with computer algebra systems (CAS) for formal verification","solution verification relies on reasoning chains, not formal proof — high-effort mode may still contain subtle errors","performance varies by mathematical domain — stronger on algebra/calculus, weaker on abstract algebra or number theory","no ability to handle custom mathematical definitions or domain-specific notation"],"requires":["OpenAI API key with o3-mini access","mathematical problems in natural language or standard notation","understanding that solutions are not formally verified"],"input_types":["mathematical problem statements","equations","word problems","proofs to verify"],"output_types":["step-by-step solutions","mathematical derivations","reasoning chains","alternative solution approaches"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o3-mini__cap_7","uri":"capability://tool.use.integration.api.based.inference.with.structured.response.formatting","name":"api-based inference with structured response formatting","description":"Provides REST API endpoints for inference with support for structured response formatting (JSON mode), enabling integration into applications requiring machine-readable outputs. The implementation uses JSON schema validation to ensure responses conform to specified structures, allowing developers to parse model outputs programmatically without post-processing. The API supports both streaming and non-streaming modes, with configurable reasoning effort levels passed as request parameters. Response metadata includes token counts (reasoning and output separately) for cost tracking.","intents":["I need to integrate reasoning capabilities into my application via REST API","I want to get structured JSON responses from the model for downstream processing","I need to track token consumption and costs for each API call"],"best_for":["teams building applications on top of reasoning models","platforms requiring programmatic integration of reasoning","applications needing structured data extraction from reasoning"],"limitations":["JSON mode may constrain reasoning depth — complex reasoning chains may not fit within JSON structure constraints","API rate limits and quotas not publicly documented — production deployments require empirical testing","no built-in caching or request deduplication — identical requests incur full inference cost","structured response formatting adds latency overhead compared to unstructured text output"],"requires":["OpenAI API key with o3-mini access","HTTP client library (Python requests, Node.js fetch, etc.)","JSON schema definition for response structure","understanding of API authentication and error handling"],"input_types":["JSON request payloads","text prompts","system messages"],"output_types":["JSON responses","streaming token chunks","metadata (token counts, finish reason)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o3-mini__cap_8","uri":"capability://planning.reasoning.multi.turn.conversation.with.reasoning.context.preservation","name":"multi-turn conversation with reasoning context preservation","description":"Maintains reasoning context and conversation history across multiple turns, enabling the model to build on previous reasoning steps and refine answers based on user feedback. The implementation preserves the full conversation history within the 200K context window, allowing the model to reference earlier reasoning and adjust its approach based on clarifications or corrections.","intents":["I want to have a multi-turn conversation where the model refines its reasoning based on my feedback","I need the model to remember earlier reasoning steps and build on them in subsequent queries","I'm debugging code and want to iterate with the model, having it adjust its analysis based on my corrections"],"best_for":["interactive debugging and problem-solving workflows","educational tutoring systems requiring iterative explanation refinement","collaborative development where reasoning is refined through multiple rounds of feedback"],"limitations":["context window is shared between conversation history and new input — long conversations leave less room for new context","reasoning effort is per-request, not per-conversation — each turn may use different effort levels, potentially inconsistent reasoning quality","no explicit conversation memory management — developers must manually manage context window usage"],"requires":["OpenAI API key with o3-mini access","ability to manage conversation history and pass it with each API call","awareness of context window limits when building long conversations"],"input_types":["text prompts","follow-up questions","corrections and clarifications","code snippets with context"],"output_types":["refined reasoning traces","updated solutions based on feedback","clarifications and explanations"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o3-mini__cap_9","uri":"capability://planning.reasoning.transparent.reasoning.trace.generation.for.interpretability","name":"transparent reasoning trace generation for interpretability","description":"Generates explicit reasoning traces showing the model's thought process, intermediate steps, and justifications for conclusions, enabling users to understand and verify the reasoning. The implementation exposes the chain-of-thought as part of the output, allowing inspection of reasoning quality and identification of errors or logical gaps.","intents":["I need to audit the model's reasoning to ensure it's correct before using the output in production","I want to understand why the model arrived at a particular conclusion for debugging or improvement","I'm building an educational system and need to show students the reasoning process, not just the answer"],"best_for":["teams requiring reasoning transparency for compliance or verification","educational platforms teaching problem-solving methodology","research applications studying model reasoning patterns"],"limitations":["reasoning traces are generated text, not formally verified — they may contain subtle errors or logical gaps","trace verbosity is not controllable — users get full traces regardless of preference, potentially adding unnecessary tokens","reasoning traces may be misleading if the model is confident but 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