{"uid":"cap_pl0VMmbmkfQHiS-MmP0MW","slug":"x402-slinkylayer-ai-8a3805f9","name":"Liquid LFM 2.5 1.2B Thinking Chat Completions","description":"Create a chat completion using Liquid LFM 2.5 1.2B Thinking model.","url":"https://x402.slinkylayer.ai/api/v1/proxy/0c6dff19-f642-4509-bbb6-2893cdeffcb4/chat-completions-lfm-2-5-thinking","method":"POST","headers":{},"bodySchema":null,"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.005000","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"settled","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":"$0.005000/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_TjKQk47Wm9dvgPE1FW5OE","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":"Generates chat completions using the Liquid LFM 2.5 1.2B Thinking language model via a pay-per-call x402 proxy endpoint.","exampleAgentPrompt":"Use the Liquid LFM 2.5 1.2B Thinking model to answer this step-by-step reasoning question: 'A farmer has 17 sheep, all but 9 die — how many are left?' and show your chain of thought.","exampleUseCases":[{"title":"Quick math tutoring explanations","prompt":"Use the Liquid LFM 2.5 1.2B Thinking model to walk through this algebra problem step by step: 'If 3x + 7 = 22, what is x?' — show your reasoning at each stage so a student can follow along."},{"title":"Budget logic checks for agents","prompt":"I need a fast, cheap reasoning model to evaluate this business rule for me: 'If a customer has made more than 3 purchases in 30 days and their cart total exceeds $100, should they qualify for a loyalty discount?' — use the Liquid LFM thinking model and explain your chain of thought."},{"title":"On-demand code debugging reasoning","prompt":"Without a subscription, call the Liquid LFM 2.5 1.2B Thinking model to reason through why this Python snippet might be throwing an off-by-one error and suggest a fix, thinking through it step by step: 'for i in range(1, len(my_list)): print(my_list[i])'"}],"resultDescription":"A chat completion response object containing the assistant's generated text and reasoning output from the Liquid LFM 2.5 1.2B Thinking model, structured in standard OpenAI-compatible chat completion format.","failureModes":["Payment not provided or insufficient USDC balance — returns 402 Payment Required","Invalid or malformed message array in request body — returns 400 Bad Request","Model temporarily unavailable or overloaded — returns 503 Service Unavailable","Request exceeds model's context window — returns 400 with token limit error","Malformed JSON body — returns 400 parsing error"],"whenToPreferThis":"Prefer this endpoint when you need a lightweight, cost-efficient thinking/reasoning LLM (1.2B parameters) accessible via x402 micropayments without subscription overhead. Ideal for agents needing on-demand inference with pay-per-call billing in USDC, especially when the Liquid LFM architecture's efficiency for reasoning tasks is desired over larger general-purpose models.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-15T00:44:59.467Z","isFirstParty":false}