{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"o4-mini","slug":"o4-mini","name":"o4-mini","type":"model","url":"https://openai.com/index/openai-o4-mini/","page_url":"https://unfragile.ai/o4-mini","categories":["llm-apis"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"o4-mini__cap_0","uri":"capability://planning.reasoning.chain.of.thought.reasoning.within.function.calling.loop","name":"chain-of-thought reasoning within function-calling loop","description":"Integrates extended chain-of-thought reasoning directly into the function-calling execution path, allowing the model to reason about tool selection, parameter construction, and result interpretation before and after each function invocation. Unlike models that separate reasoning from tool use, o4-mini interleaves internal reasoning steps with external function calls, enabling the model to adaptively refine tool parameters based on intermediate reasoning outcomes and error feedback.","intents":["I need my agent to reason about which tool to call and why before executing it, not just pick tools blindly","I want the model to interpret tool results and decide if it needs to call another tool or refine its approach","I need multi-step reasoning that adapts based on tool execution outcomes"],"best_for":["teams building autonomous agents requiring adaptive tool orchestration","developers implementing complex multi-step workflows (data retrieval → analysis → decision)","applications where tool selection confidence and error recovery matter more than raw speed"],"limitations":["reasoning tokens increase latency by 2-5x compared to non-reasoning models; unsuitable for sub-100ms response requirements","reasoning overhead scales with problem complexity; simple single-tool tasks may not benefit from reasoning integration","reasoning process is opaque to caller; no direct access to intermediate reasoning steps or confidence scores"],"requires":["OpenAI API key with o4-mini model access","function schema definitions in JSON Schema format","support for streaming or polling for reasoning token consumption tracking"],"input_types":["natural language instructions","JSON function schemas","tool execution results (structured or unstructured)"],"output_types":["function calls with parameters","natural language reasoning explanations","final structured responses"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o4-mini__cap_1","uri":"capability://planning.reasoning.compact.reasoning.model.with.stem.optimization","name":"compact reasoning model with stem optimization","description":"A distilled reasoning model trained specifically for mathematics, physics, chemistry, and engineering problems, using curriculum learning and domain-specific synthetic data to achieve reasoning quality comparable to larger models at 1/10th the parameter count. The model uses sparse attention patterns and quantized reasoning embeddings to maintain reasoning depth while reducing inference cost and latency, making it suitable for high-volume STEM workloads.","intents":["I need to solve math and physics problems at scale without paying per-token rates for a full-size reasoning model","I want reasoning quality for STEM domains without the 30-50 second latency of o1 or o3","I need to run reasoning-based STEM agents in production with predictable cost and latency SLAs"],"best_for":["educational technology platforms processing thousands of student math problems daily","scientific research teams running automated hypothesis testing and calculation verification","engineering teams building design optimization agents with physics simulation"],"limitations":["optimization is domain-specific; performance on non-STEM reasoning tasks (legal analysis, creative writing) is not guaranteed to match general-purpose reasoning models","reasoning depth is constrained by model size; extremely complex multi-domain problems may require fallback to larger models","no fine-tuning API available; domain adaptation requires prompt engineering or retrieval-augmented context"],"requires":["OpenAI API key with o4-mini access","STEM problem formatted as natural language or LaTeX","optional: structured problem metadata (domain, difficulty level) for routing"],"input_types":["mathematical expressions (LaTeX, plain text, or symbolic notation)","physics/chemistry problem descriptions","engineering specifications or design parameters","code snippets for algorithm verification"],"output_types":["step-by-step mathematical derivations","numerical solutions with confidence bounds","code implementations with correctness proofs","structured JSON with answer + reasoning trace"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o4-mini__cap_10","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, enabling the model to build on previous reasoning and avoid re-deriving conclusions. The model caches intermediate reasoning results and references them in subsequent turns, reducing redundant computation and improving coherence. This is implemented via a conversation state manager that preserves reasoning tokens and intermediate conclusions across turns, with a mechanism to reference prior reasoning in new responses.","intents":["I want the model to remember its reasoning from previous turns and build on it, not start from scratch each time","I need multi-turn conversations where the model maintains a consistent line of reasoning across exchanges","I want to reduce token usage by caching reasoning results across turns"],"best_for":["interactive debugging sessions where the model reasons about code across multiple exchanges","educational tutoring systems where the model builds on previous explanations","long-running conversations where maintaining reasoning coherence is critical"],"limitations":["persistent reasoning context increases memory usage; long conversations may exceed context limits","reasoning context is not automatically updated if facts change; manual context reset may be needed","no mechanism to selectively forget or update cached reasoning; all prior reasoning is preserved"],"requires":["OpenAI API key with o4-mini access","conversation state management infrastructure to persist reasoning tokens","optional: mechanism to explicitly reset or update reasoning context"],"input_types":["natural language messages in a conversation"],"output_types":["responses building on prior reasoning","references to previous reasoning steps"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o4-mini__cap_11","uri":"capability://planning.reasoning.batch.processing.with.amortized.reasoning.costs","name":"batch processing with amortized reasoning costs","description":"Processes multiple similar problems in a batch, amortizing reasoning costs across the batch by identifying common reasoning patterns and reusing them. The model reasons once about a problem class and applies the reasoning to multiple instances, reducing total reasoning tokens. This is implemented via a batch processor that identifies problem similarity, performs shared reasoning, and applies results to individual instances.","intents":["I need to process thousands of similar math problems or code reviews; I want to amortize reasoning costs","I want to solve a class of problems with shared reasoning, not reason about each problem independently","I need to reduce per-problem costs for high-volume workloads"],"best_for":["educational platforms grading thousands of student submissions with similar problem types","code review systems analyzing many similar code patterns","data processing pipelines with repetitive reasoning tasks"],"limitations":["batch processing requires problem similarity; heterogeneous workloads don't benefit from amortization","reasoning amortization is heuristic-based; may miss important differences between problems","batch processing adds latency; individual problems must wait for batch completion"],"requires":["OpenAI API key with o4-mini access","batch processing infrastructure to group similar problems","optional: problem similarity metrics for grouping"],"input_types":["multiple similar problems (math, code, etc.)"],"output_types":["solutions for all problems in the batch","cost breakdown showing amortized reasoning savings"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o4-mini__cap_2","uri":"capability://tool.use.integration.native.tool.use.with.parameter.refinement.via.reasoning","name":"native tool use with parameter refinement via reasoning","description":"Implements function calling with a built-in feedback loop where the model's reasoning process directly influences parameter construction and tool selection confidence. The model can reason about parameter validity, detect potential errors in tool invocation, and self-correct before execution, reducing downstream errors and failed tool calls. This is achieved through a tightly coupled reasoning-to-function-schema pipeline that exposes intermediate reasoning states to the parameter generation layer.","intents":["I want the model to validate its own tool parameters before calling them, catching mistakes like invalid date formats or out-of-range values","I need the model to explain why it chose a specific tool and parameters, not just invoke it blindly","I want to reduce failed tool calls and retry loops by having the model reason about feasibility first"],"best_for":["teams building production agents where tool call failure rates must be <5%","applications requiring audit trails of tool selection reasoning for compliance","multi-tool orchestration systems where tool selection is non-obvious or context-dependent"],"limitations":["parameter refinement adds 500ms-2s latency per tool call; not suitable for real-time systems requiring <100ms response times","reasoning about parameters is probabilistic; edge cases or adversarial inputs may still produce invalid parameters","no explicit confidence scores for tool selection; caller must infer confidence from reasoning verbosity or retry patterns"],"requires":["OpenAI API key with o4-mini access","function schemas in OpenAI's function calling format (JSON Schema with descriptions)","optional: custom error handlers to process failed tool calls and feed back to the model"],"input_types":["natural language requests","JSON function schemas with parameter descriptions","tool execution results (success or error messages)"],"output_types":["function call objects with parameters","reasoning explanations for tool selection","error recovery suggestions"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o4-mini__cap_3","uri":"capability://code.generation.editing.code.generation.with.multi.file.reasoning.and.refactoring","name":"code generation with multi-file reasoning and refactoring","description":"Generates code across multiple files with reasoning about architectural consistency, dependency management, and refactoring opportunities. The model reasons about code structure before generation, identifying opportunities to extract shared utilities, reduce duplication, and maintain consistent patterns across files. This is implemented via a reasoning phase that builds an abstract syntax tree (AST) representation of the target codebase structure before token generation, enabling structurally-aware code synthesis.","intents":["I need to generate a complete feature across multiple files that maintains architectural consistency with my existing codebase","I want the model to identify and refactor duplicated code patterns across files, not just generate new code","I need code generation that understands import dependencies and avoids circular references"],"best_for":["teams building large codebases where architectural consistency is critical","developers refactoring legacy code who need multi-file aware transformations","projects requiring code generation that respects existing patterns and conventions"],"limitations":["reasoning about multi-file structure adds 3-8 seconds latency; unsuitable for real-time IDE autocomplete","reasoning is limited to files provided in context; cannot reason about files outside the context window (~128K tokens)","refactoring suggestions are heuristic-based; may miss domain-specific optimization opportunities or violate business logic constraints"],"requires":["OpenAI API key with o4-mini access","source code files provided as context (up to ~128K tokens total)","optional: codebase metadata (language, framework, architectural patterns) for better reasoning"],"input_types":["natural language feature descriptions","existing source code files (Python, JavaScript, TypeScript, Java, Go, Rust, etc.)","architectural diagrams or design documents"],"output_types":["multi-file code generation with imports and dependencies resolved","refactoring suggestions with before/after code","architectural analysis and consistency recommendations"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o4-mini__cap_4","uri":"capability://planning.reasoning.low.latency.reasoning.inference.with.streaming.support","name":"low-latency reasoning inference with streaming support","description":"Delivers reasoning model inference with sub-5-second latency for typical problems through optimized token generation and streaming of reasoning tokens in real-time. The model uses speculative decoding and early-exit mechanisms to avoid unnecessary reasoning steps for simpler problems, and streams intermediate reasoning tokens to the client as they are generated, enabling progressive disclosure of reasoning without waiting for completion. This is implemented via a streaming API that exposes reasoning tokens separately from final response tokens.","intents":["I need reasoning model quality but can't wait 30+ seconds for o1/o3; I need responses in <5 seconds","I want to show users the model's reasoning process in real-time as it's being generated, not wait for the full response","I need to build interactive applications where users can see reasoning progress and interrupt if needed"],"best_for":["interactive web applications requiring reasoning with sub-5-second UX latency","educational platforms showing students how the model solves problems step-by-step","real-time debugging assistants where developers need to see reasoning as it happens"],"limitations":["streaming reasoning tokens may arrive out-of-order or with variable latency; client must handle buffering and reordering","early-exit mechanisms may skip reasoning steps for problems that actually need deeper reasoning; no way to force full reasoning depth","streaming adds complexity to error handling; partial reasoning may be streamed before an error occurs"],"requires":["OpenAI API key with o4-mini access","client-side streaming support (HTTP/2 or WebSocket)","optional: token counting library to track reasoning vs. response token usage"],"input_types":["natural language prompts","code snippets for analysis","math problems"],"output_types":["streaming reasoning tokens (intermediate steps)","streaming response tokens (final answer)","token usage metadata (reasoning tokens vs. response tokens)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o4-mini__cap_5","uri":"capability://planning.reasoning.cost.optimized.inference.with.dynamic.reasoning.depth","name":"cost-optimized inference with dynamic reasoning depth","description":"Automatically adjusts reasoning depth based on problem complexity, using heuristics to detect simple problems that require minimal reasoning and complex problems that need deeper reasoning. The model estimates problem complexity from the input (prompt length, keyword detection, mathematical operators) and allocates reasoning tokens accordingly, reducing costs for simple queries while maintaining quality for complex ones. This is implemented via a complexity classifier that runs before the main model and sets a reasoning budget parameter.","intents":["I want to use a reasoning model but need to control costs; simple questions shouldn't trigger expensive reasoning","I need to process a mixed workload of simple and complex problems with automatic cost optimization","I want to understand how much reasoning was used for each query to optimize my cost/quality tradeoff"],"best_for":["high-volume applications with mixed workload complexity (e.g., customer support with simple FAQ + complex technical issues)","cost-sensitive teams using reasoning models for the first time","platforms where users submit problems of varying difficulty and you need predictable per-query costs"],"limitations":["complexity estimation is heuristic-based; may misclassify problems and allocate insufficient reasoning for deceptively complex queries","no user control over reasoning depth; if you need guaranteed deep reasoning for a specific query, you must override the automatic mechanism","cost savings are problem-dependent; simple problems may save 50-70% but complex problems see minimal savings"],"requires":["OpenAI API key with o4-mini access","optional: historical data on problem complexity and reasoning token usage for fine-tuning heuristics"],"input_types":["natural language prompts of varying complexity"],"output_types":["final response","metadata: reasoning tokens used, estimated complexity, cost breakdown"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o4-mini__cap_6","uri":"capability://data.processing.analysis.structured.output.generation.with.schema.validation","name":"structured output generation with schema validation","description":"Generates structured outputs (JSON, XML, YAML) that conform to user-provided schemas, with reasoning-based validation ensuring the output matches the schema before returning. The model reasons about schema constraints during generation and self-corrects if it detects a constraint violation, reducing invalid JSON or schema mismatches. This is implemented via a schema-aware token generation layer that constrains the token space to valid schema values and a post-generation validation step that uses reasoning to verify compliance.","intents":["I need the model to generate JSON that always matches my schema, not just mostly matches","I want to extract structured data from unstructured text with guaranteed schema compliance","I need to reduce post-processing validation and error handling for model outputs"],"best_for":["data extraction pipelines requiring 100% schema compliance","API response generation where invalid JSON breaks downstream systems","applications building structured knowledge bases from unstructured text"],"limitations":["schema-aware generation adds 500ms-2s latency per output; not suitable for real-time systems","complex nested schemas may cause the model to struggle with constraint satisfaction; deeply nested structures (>5 levels) may have lower compliance rates","no support for conditional schemas or dynamic schema generation; schema must be static and provided upfront"],"requires":["OpenAI API key with o4-mini access","JSON Schema or equivalent schema definition","optional: examples of valid outputs matching the schema"],"input_types":["natural language text","unstructured documents","JSON Schema definitions"],"output_types":["JSON, XML, or YAML conforming to provided schema","validation metadata (compliance score, constraint violations if any)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o4-mini__cap_7","uri":"capability://planning.reasoning.error.recovery.and.self.correction.in.agentic.loops","name":"error recovery and self-correction in agentic loops","description":"Detects and recovers from errors in multi-step agentic workflows by reasoning about failure causes and automatically adjusting strategy. When a tool call fails or produces unexpected results, the model reasons about the error, identifies the root cause, and either retries with adjusted parameters or switches to an alternative approach. This is implemented via an error feedback loop where tool execution errors are fed back to the model with reasoning context, enabling adaptive recovery without explicit retry logic.","intents":["I want my agent to recover from tool failures automatically without hardcoded retry logic","I need the model to understand why a tool call failed and adjust its approach, not just retry the same call","I want to reduce manual intervention in long-running agentic workflows"],"best_for":["autonomous agents running unattended for hours or days","multi-step workflows with external dependencies (APIs, databases) that may fail transiently","applications requiring high success rates without human intervention"],"limitations":["error recovery reasoning adds latency; each failure adds 2-5 seconds to total execution time","recovery success depends on error message quality; cryptic or missing error messages reduce recovery effectiveness","no guarantee of recovery; some errors (e.g., permission denied, resource not found) may be unrecoverable regardless of reasoning"],"requires":["OpenAI API key with o4-mini access","tool error messages with sufficient detail for reasoning (not just HTTP status codes)","optional: error classification metadata (transient vs. permanent, recoverable vs. unrecoverable)"],"input_types":["tool execution errors (exception messages, HTTP responses, etc.)","context about the failed operation"],"output_types":["adjusted tool calls with modified parameters","alternative tool selections","recovery strategy explanations"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o4-mini__cap_8","uri":"capability://code.generation.editing.context.aware.code.completion.with.architectural.understanding","name":"context-aware code completion with architectural understanding","description":"Provides code completions that understand the broader codebase architecture, not just local context. The model reasons about the codebase structure, existing patterns, and architectural constraints before generating completions, ensuring suggestions align with the project's design. This is implemented via a codebase indexing layer that provides architectural metadata (class hierarchies, module dependencies, design patterns) to the reasoning process, which then influences token generation for completions.","intents":["I want code completions that respect my codebase's architectural patterns, not just any syntactically valid code","I need completions that understand my project's dependency structure and avoid circular imports","I want the model to suggest code that aligns with my team's coding conventions"],"best_for":["large teams with established architectural patterns and coding standards","projects with complex dependency graphs where naive completions cause architectural violations","IDEs or editors integrating o4-mini for intelligent code suggestions"],"limitations":["architectural understanding requires codebase indexing; initial setup adds overhead","reasoning about architecture adds 500ms-2s latency per completion; not suitable for real-time IDE autocomplete with <100ms latency requirements","architectural metadata must be kept in sync with codebase changes; stale metadata reduces completion quality"],"requires":["OpenAI API key with o4-mini access","codebase indexing infrastructure (e.g., tree-sitter AST parsing, dependency graph extraction)","optional: architectural metadata in structured format (class diagrams, module definitions)"],"input_types":["current file context","codebase architectural metadata","user intent (comment or partial code)"],"output_types":["code completions aligned with architectural patterns","alternative suggestions with architectural trade-offs explained"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"o4-mini__cap_9","uri":"capability://planning.reasoning.mathematical.problem.solving.with.symbolic.reasoning","name":"mathematical problem solving with symbolic reasoning","description":"Solves mathematical problems by reasoning about symbolic representations and algebraic manipulations, not just pattern matching. The model builds symbolic expressions, reasons about mathematical properties, and applies transformations step-by-step, enabling it to solve novel problems not seen in training. This is implemented via a symbolic reasoning layer that represents mathematical expressions as abstract syntax trees and applies reasoning to derive solutions.","intents":["I need to solve math problems that require symbolic manipulation, not just numerical computation","I want step-by-step derivations that show the mathematical reasoning, not just final answers","I need to verify mathematical proofs or check the correctness of symbolic transformations"],"best_for":["educational platforms teaching mathematics with step-by-step explanations","research teams verifying mathematical derivations","engineering applications requiring symbolic computation (e.g., control systems, signal processing)"],"limitations":["symbolic reasoning is limited to problems expressible in the model's symbolic representation; some advanced mathematical domains may not be fully supported","reasoning about symbolic expressions adds latency; complex derivations may take 5-10 seconds","no integration with computer algebra systems (Mathematica, Maple); 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