{"uid":"cap_cR0FELicOag0hyNBmaGCE","slug":"liquid-lfm-2-5-1-2b-thinking-chat-completions-x402-slinkylayer-ai-7b0783d3","name":"Liquid LFM 2.5 1.2B Thinking Chat Completions (x402.slinkylayer.ai)","description":"Create a chat completion using Liquid LFM 2.5 1.2B Thinking model.","url":"https://x402.slinkylayer.ai/api/v1/proxy/:id/chat-completions-lfm-2-5-thinking","method":"POST","headers":{},"bodySchema":null,"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.005","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"registry","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":"$0.005/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.005","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.005","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_-zlCl-oi7MBYygp3b5Ytf","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.005","costPer":"request","priority":0,"asset":null,"unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Creates chat completions using the Liquid LFM 2.5 1.2B Thinking language model, a small but reasoning-capable LLM, via a pay-per-call x402 proxy.","exampleAgentPrompt":"Use the Liquid LFM 2.5 1.2B Thinking model to answer this step-by-step reasoning question: 'If a train travels 60 miles in 45 minutes, what is its speed in miles per hour and how long will it take to travel 200 miles?'","exampleUseCases":[{"title":"Step-by-step math problem solver","prompt":"I need to walk through a logic puzzle — use the Liquid LFM 2.5 Thinking model to reason through it carefully: 'A farmer has 17 sheep. All but 9 run away. How many does he have left?' Show your thinking."},{"title":"Lightweight agentic reasoning node","prompt":"In my AI pipeline, I need a fast, cheap LLM call to classify whether this user query needs a web search or can be answered from memory: 'What is the capital of France?' Use the Liquid LFM 2.5 1.2B Thinking model and return just the classification with reasoning."},{"title":"Code explanation for junior developers","prompt":"Use the Liquid LFM 2.5 Thinking model to explain this Python snippet in plain English, step by step, as if talking to a beginner: 'def fib(n): return n if n <= 1 else fib(n-1) + fib(n-2)'"}],"resultDescription":"Returns a chat completion object in OpenAI-compatible format, including the assistant's generated message content produced by the Liquid LFM 2.5 1.2B Thinking model. The response includes the model's reasoning-enhanced text output and standard completion metadata such as token usage and finish reason.","failureModes":["Invalid or missing proxy ID in the URL path results in a 404 or authorization error","Malformed message array or missing required chat fields returns a 400 validation error","Insufficient USDC balance or failed x402 payment results in a 402 Payment Required response","Model overload or upstream Liquid AI service unavailability returns a 503 error","Excessively long input exceeding the model's context window may result in a truncation error or 400"],"whenToPreferThis":"Choose this endpoint when you need a low-cost, pay-per-call chat completion with reasoning/thinking capabilities from a compact 1.2B parameter model. It is ideal for agentic workflows that require many LLM calls on a budget, tasks that benefit from chain-of-thought reasoning without needing a large frontier model, or when you want to pay in USDC per call rather than via a subscription. Prefer larger models if the task requires broad world knowledge or complex multi-step generation.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-15T12:39:39.653Z","isFirstParty":false}