{"uid":"cap_Ur_wSlaOhBQVwaoWkNdr4","slug":"pennyrail-openai-compatible-chat-mini-2c45ad9c","name":"PennyRail OpenAI-Compatible Chat Mini","description":"Machine-readable settlement service","url":"https://pennyrail.vercel.app/api/p/premium/ai.chat-mini--openai-compatible-chat","method":"POST","headers":{},"bodySchema":{"type":"object","required":["input"],"properties":{"input":{"type":"object"}}},"responseSchema":{"type":"object","additionalProperties":true},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.02","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.02/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.02","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.02","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_ce_cdMCoa7iNFm6Lnts1S","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.02","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Provides an OpenAI-compatible chat completion API endpoint with micropayment settlement via x402, offering a mini/lightweight chat model interface","exampleAgentPrompt":"Send this message to the mini chat model and get a response: 'Explain the difference between supervised and unsupervised learning in two sentences.'","exampleUseCases":[{"title":"Pay-per-call AI chatbot backend","prompt":"I want to build a small chatbot that charges users per message without subscriptions — send this user's question to the mini chat model and return the answer: 'What are the best practices for writing clean Python code?'"},{"title":"Lightweight content generation for scripts","prompt":"Use the mini chat model to generate a short product description for a wireless ergonomic keyboard aimed at remote workers — keep it under 60 words."},{"title":"Automated Q&A in an agent pipeline","prompt":"As part of my data pipeline, I need to classify this customer feedback using the chat model: 'The delivery was fast but the packaging was damaged.' Tell me if it's positive, negative, or mixed."}],"resultDescription":"Returns a chat completion response in OpenAI-compatible format, containing the assistant's generated message text and any associated metadata such as token usage and finish reason.","failureModes":["Payment failure if insufficient USDC balance — returns 402 Payment Required","Malformed input object causes 400 Bad Request","Model unavailability or upstream OpenAI outage returns 503 or 500","Rate limiting may result in 429 Too Many Requests","Missing required 'input' field returns validation error"],"whenToPreferThis":"Choose this endpoint when you need an OpenAI-compatible chat completion interface with per-call USDC micropayment billing via x402, avoiding subscription commitments. Ideal for agent pipelines that need lightweight, cost-controlled text generation without managing API key subscriptions. Prefer this over standard OpenAI API when your system already handles x402 payment flows and you want granular per-request cost control.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-15T00:42:40.607Z","isFirstParty":false}