{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-openai-gpt-5-mini","slug":"openai-gpt-5-mini","name":"OpenAI: GPT-5 Mini","type":"model","url":"https://openrouter.ai/models/openai~gpt-5-mini","page_url":"https://unfragile.ai/openai-gpt-5-mini","categories":["llm-apis"],"tags":["openai","api-access","text","image"],"pricing":{"model":"paid","free":false,"starting_price":"$2.50e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-openai-gpt-5-mini__cap_0","uri":"capability://text.generation.language.lightweight.instruction.following.with.reduced.latency","name":"lightweight-instruction-following-with-reduced-latency","description":"GPT-5 Mini executes natural language instructions with the same transformer-based architecture and instruction-tuning as full GPT-5, but with a reduced parameter count and optimized inference pipeline. This enables faster token generation and lower computational overhead while maintaining semantic understanding and multi-step reasoning for lighter workloads. The model uses the same safety-tuning and RLHF alignment as GPT-5 but with a smaller effective context window and reduced intermediate layer depth.","intents":["I need to process user queries with instruction-following capability but want sub-second latency for real-time applications","I want to reduce API costs for high-volume text generation while maintaining quality","I need to handle moderate reasoning tasks without the computational overhead of full GPT-5"],"best_for":["developers building cost-sensitive chatbots and conversational agents","teams processing high-volume text generation with latency constraints","startups optimizing inference costs while maintaining instruction-following quality"],"limitations":["Reduced reasoning depth compared to full GPT-5 — struggles with complex multi-step logical chains requiring 10+ reasoning steps","Smaller effective context window — may not handle documents longer than 8K-16K tokens as effectively as GPT-5","Lower performance on specialized domains requiring extensive training data — may underperform on highly technical or domain-specific instructions","No fine-tuning capability exposed through standard OpenAI API — locked to base instruction-tuned weights"],"requires":["OpenAI API key with GPT-5 Mini access enabled","HTTP client capable of making REST requests to OpenAI endpoints","Understanding of token counting for cost estimation (approximately 50-70% cheaper per token than GPT-5)"],"input_types":["text (natural language instructions, prompts, queries)","structured text (JSON, YAML, markdown with embedded instructions)"],"output_types":["text (natural language responses, code snippets, structured text)","streaming text (token-by-token output for real-time applications)"],"categories":["text-generation-language","instruction-following"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5-mini__cap_1","uri":"capability://text.generation.language.multi.turn.conversation.state.management","name":"multi-turn-conversation-state-management","description":"GPT-5 Mini maintains conversation context through explicit message history passed in each API request, using a role-based message format (system, user, assistant) that the model processes sequentially to generate contextually-aware responses. The model tracks implicit conversation state through the message array without server-side session persistence, requiring the client to manage and replay the full conversation history for each turn. This stateless design enables horizontal scaling and cost-per-request transparency.","intents":["I need to build a multi-turn chatbot where the model remembers previous messages in the conversation","I want to implement context-aware responses that reference earlier user statements","I need to inject system-level instructions that persist across multiple conversation turns"],"best_for":["developers building conversational AI applications with explicit context management","teams implementing chatbots where conversation history is stored in external databases","applications requiring fine-grained control over what context is included in each request"],"limitations":["No server-side session management — all conversation history must be sent with each request, increasing payload size and latency for long conversations","Token consumption grows linearly with conversation length — a 50-turn conversation consumes 50x more tokens than a single-turn request","No built-in conversation summarization or compression — developers must implement their own context windowing strategies","Message history is not persisted by OpenAI — client is responsible for storing and replaying full conversation state"],"requires":["OpenAI API key with chat completion endpoint access","Client-side conversation history storage (in-memory, database, or file system)","Understanding of message role semantics (system, user, assistant) for proper context formatting"],"input_types":["structured JSON messages with role and content fields","message arrays representing conversation history"],"output_types":["text (assistant response)","structured completion object with usage metadata (prompt tokens, completion tokens)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5-mini__cap_2","uri":"capability://text.generation.language.system.prompt.injection.and.behavior.customization","name":"system-prompt-injection-and-behavior-customization","description":"GPT-5 Mini accepts a system-level prompt (passed as the first message with role='system') that establishes behavioral constraints, output formatting rules, and domain-specific instructions that influence all subsequent responses in a conversation. The system prompt is processed by the model's attention mechanisms as a high-priority context token sequence, effectively creating a persistent instruction layer that modulates the model's response generation without requiring fine-tuning. This approach leverages the model's instruction-tuning to respect system-level directives while maintaining safety guardrails.","intents":["I need to customize the model's behavior for a specific use case (e.g., 'act as a Python expert') without fine-tuning","I want to enforce output formatting rules (JSON, markdown, specific structure) across all responses","I need to inject domain-specific knowledge or constraints that guide the model's reasoning"],"best_for":["developers building specialized chatbots with consistent behavioral requirements","teams implementing role-based AI assistants (customer support, technical help, creative writing)","applications requiring structured output formatting without custom fine-tuning"],"limitations":["System prompt effectiveness degrades with conflicting user instructions — adversarial users can override system constraints through prompt injection","No guarantee of system prompt adherence — the model may ignore or partially follow system instructions if user prompts are sufficiently strong","System prompt tokens are counted in billing — lengthy system prompts increase per-request costs","No built-in versioning or A/B testing framework for system prompt optimization"],"requires":["OpenAI API key with chat completion endpoint access","Understanding of prompt engineering principles and instruction hierarchy","Awareness of prompt injection vulnerabilities when accepting user-controlled input"],"input_types":["text (system prompt string)","structured instructions (JSON, markdown, natural language)"],"output_types":["text (responses influenced by system prompt constraints)","structured output (JSON, markdown, code) if specified in system prompt"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5-mini__cap_3","uri":"capability://text.generation.language.streaming.token.generation.for.real.time.output","name":"streaming-token-generation-for-real-time-output","description":"GPT-5 Mini supports server-sent events (SSE) streaming where tokens are emitted incrementally as they are generated, rather than waiting for the complete response. The API returns a stream of JSON objects with delta content fields that clients consume in real-time, enabling progressive rendering of responses and perceived latency reduction. This streaming approach uses HTTP chunked transfer encoding and maintains the same token-counting semantics as non-streaming requests, with identical billing per token regardless of streaming mode.","intents":["I need to display model responses in real-time as they are generated, rather than waiting for the full response","I want to reduce perceived latency in user-facing applications by showing partial results immediately","I need to implement cancellation logic that stops token generation mid-response when users interrupt"],"best_for":["developers building interactive web applications and chat interfaces","teams implementing real-time AI assistants where perceived latency matters","applications with long-form generation (essays, code, documentation) where progressive output improves UX"],"limitations":["Streaming adds complexity to client-side implementation — requires event stream parsing and error handling","No built-in retry logic for interrupted streams — clients must implement their own recovery mechanisms","Token counting is less transparent in streaming mode — final token usage is only available at stream end","Streaming may increase server load compared to batch requests due to connection overhead per request"],"requires":["OpenAI API key with streaming support enabled","HTTP client with SSE (Server-Sent Events) support or streaming response handling","Client-side event parsing logic to handle delta content objects"],"input_types":["text (prompts, conversation history)","structured JSON messages with streaming=true flag"],"output_types":["streaming JSON objects with delta content fields","final completion object with usage metadata at stream end"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5-mini__cap_4","uri":"capability://text.generation.language.json.mode.structured.output.generation","name":"json-mode-structured-output-generation","description":"GPT-5 Mini can be constrained to generate only valid JSON output by setting response_format={'type': 'json_object'}, which modifies the token generation process to enforce JSON syntax validity. The model uses constrained decoding (filtering invalid tokens at each generation step) to guarantee syntactically valid JSON output without post-processing, while maintaining semantic understanding of the requested structure. This approach combines instruction-tuning (the model learns to generate JSON from training data) with hard constraints (invalid JSON tokens are blocked during generation).","intents":["I need to extract structured data from unstructured text and guarantee valid JSON output","I want to generate configuration files, API responses, or database records with guaranteed syntactic validity","I need to integrate model output directly into downstream systems without JSON parsing error handling"],"best_for":["developers building data extraction pipelines that require guaranteed valid output","teams implementing API endpoints that return model-generated JSON responses","applications where JSON parsing failures would cause system failures"],"limitations":["JSON mode requires explicit schema specification in the prompt — the model cannot infer complex nested structures without guidance","Constrained decoding adds ~5-15% latency overhead compared to unconstrained generation","JSON mode may produce incomplete or truncated output if the requested structure is too complex","No schema validation beyond JSON syntax — the model may generate valid JSON that doesn't match the intended schema"],"requires":["OpenAI API key with JSON mode support enabled","Clear schema specification in the system prompt or user message","Understanding that JSON mode guarantees syntax validity but not semantic correctness"],"input_types":["text (unstructured data to extract from)","structured prompts with JSON schema specifications"],"output_types":["valid JSON objects or arrays","guaranteed syntactically valid JSON (no parsing errors)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5-mini__cap_5","uri":"capability://tool.use.integration.function.calling.with.schema.based.tool.invocation","name":"function-calling-with-schema-based-tool-invocation","description":"GPT-5 Mini can be provided with a list of function schemas (name, description, parameters) and will generate structured function calls when appropriate, returning a special 'function_call' response type containing the function name and arguments as JSON. The model uses instruction-tuning to understand when to invoke functions based on user intent, and generates properly-formatted function call objects that clients can execute directly. This approach enables tool use without requiring the model to generate arbitrary code, with the model acting as a semantic router between user intent and available functions.","intents":["I need the model to decide when to call external APIs or functions based on user requests","I want to build an agent that can use tools like calculators, web search, or database queries","I need to extract structured function parameters from natural language user input"],"best_for":["developers building AI agents with access to external tools and APIs","teams implementing autonomous workflows where the model decides which functions to call","applications requiring semantic routing between user intent and available capabilities"],"limitations":["Function calling requires explicit schema definition — the model cannot infer function signatures from code","No built-in error handling for function execution — clients must implement retry logic and error recovery","Model may hallucinate function calls that don't exist or misunderstand parameter requirements","Function calling adds latency compared to direct text generation due to schema processing overhead"],"requires":["OpenAI API key with function calling support enabled","Function schema definitions in OpenAI format (name, description, parameters with JSON schema)","Client-side function execution and result handling logic"],"input_types":["text (user intent, natural language requests)","function schemas (JSON objects with name, description, parameters)"],"output_types":["function call objects with function name and arguments","text responses when function calling is not appropriate"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5-mini__cap_6","uri":"capability://text.generation.language.temperature.and.sampling.parameter.control","name":"temperature-and-sampling-parameter-control","description":"GPT-5 Mini exposes temperature (0.0-2.0) and top_p (0.0-1.0) parameters that control the randomness and diversity of token selection during generation. Temperature scales the logit distribution before sampling (lower = more deterministic, higher = more random), while top_p implements nucleus sampling (only sample from the top p% of probability mass). These parameters enable fine-grained control over output variability without model retraining, allowing developers to tune the model's behavior from deterministic (temperature=0) to highly creative (temperature=2.0).","intents":["I need deterministic, reproducible outputs for tasks like code generation or data extraction","I want to increase creativity and diversity for tasks like creative writing or brainstorming","I need to balance consistency and variety for different use cases within the same application"],"best_for":["developers building applications with varying consistency requirements","teams tuning model behavior for specific domains without fine-tuning","applications where output diversity is a feature (e.g., generating multiple variations)"],"limitations":["Temperature=0 does not guarantee identical outputs across requests — tie-breaking in softmax can produce different results","Higher temperatures increase latency due to more complex sampling operations","No principled way to select optimal temperature for a given task — requires empirical tuning","Temperature and top_p interact in complex ways — changing both simultaneously can produce unexpected results"],"requires":["OpenAI API key","Understanding of temperature and top_p semantics and their effects on output","Empirical testing to find optimal parameters for specific use cases"],"input_types":["numeric parameters (temperature: 0.0-2.0, top_p: 0.0-1.0)"],"output_types":["text with varying levels of randomness and diversity based on parameter settings"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5-mini__cap_7","uri":"capability://data.processing.analysis.token.counting.and.usage.tracking","name":"token-counting-and-usage-tracking","description":"GPT-5 Mini API responses include detailed usage metadata (prompt_tokens, completion_tokens, total_tokens) that enable precise cost calculation and quota management. The model uses the same tokenization scheme as GPT-4 (BPE-based with 100K token vocabulary), allowing developers to pre-count tokens before making requests using the tiktoken library. This enables transparent billing, budget enforcement, and cost optimization without hidden charges or surprise overages.","intents":["I need to calculate API costs before making requests to stay within budget","I want to implement quota management and rate limiting based on token consumption","I need to optimize prompts to reduce token usage and lower costs"],"best_for":["developers building cost-sensitive applications with strict budgets","teams implementing quota management and billing systems","applications where token efficiency directly impacts profitability"],"limitations":["Token counting is approximate for pre-request estimation — actual token count may differ by 1-2 tokens due to edge cases","Streaming responses only provide final token count at stream end — cannot pre-count streaming requests","Special tokens (function calls, JSON mode) may consume more tokens than expected","No built-in cost tracking or billing integration — developers must implement their own accounting"],"requires":["OpenAI API key","tiktoken library for Python or equivalent tokenizer for other languages","Understanding of tokenization and how different input types affect token count"],"input_types":["text (prompts, messages)","structured data (function schemas, JSON)"],"output_types":["numeric token counts (prompt_tokens, completion_tokens, total_tokens)","usage metadata in API responses"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-5-mini__cap_8","uri":"capability://safety.moderation.safety.alignment.and.content.filtering","name":"safety-alignment-and-content-filtering","description":"GPT-5 Mini uses RLHF (Reinforcement Learning from Human Feedback) alignment to refuse harmful requests, generate balanced perspectives on controversial topics, and avoid generating illegal content, hate speech, or explicit material. The model has built-in safety guardrails that are applied during training and inference, without requiring explicit content filters in the API. This approach embeds safety into the model's decision-making rather than post-processing outputs, making it harder to circumvent through prompt engineering.","intents":["I need a model that refuses to generate harmful, illegal, or unethical content","I want to deploy an AI assistant in production without extensive content moderation infrastructure","I need to ensure my application complies with content policies and legal requirements"],"best_for":["developers building public-facing AI applications with safety requirements","teams deploying AI in regulated industries (healthcare, finance, legal)","applications where content moderation is critical to user trust and legal compliance"],"limitations":["Safety alignment is not perfect — adversarial prompts can sometimes bypass guardrails","The model may refuse legitimate requests if they superficially resemble harmful content","Safety tuning may reduce model capability on some technical tasks (e.g., security research, penetration testing)","No fine-grained control over safety levels — all users get the same safety alignment"],"requires":["OpenAI API key","Understanding of what content the model will refuse","Awareness that safety refusals may impact some legitimate use cases"],"input_types":["text (any user input, including potentially harmful requests)"],"output_types":["text (refusal messages for harmful requests, normal responses for safe requests)"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["OpenAI API key with GPT-5 Mini access enabled","HTTP client capable of making REST requests to OpenAI endpoints","Understanding of token counting for cost estimation (approximately 50-70% cheaper per token than GPT-5)","OpenAI API key with chat completion endpoint access","Client-side conversation history storage (in-memory, database, or file system)","Understanding of message 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