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Liquid LFM 2.5 1.2B Thinking Chat Completions (x402.slinkylayer.ai)
x402
Liquid LFM 2.5 1.2B Thinking Chat Completions (x402.slinkylayer.ai) is a paid API for AI agents from x402.slinkylayer.ai, paid per call via x402, $0.005/call, status unknown (last checked 2026-09-15).
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.
Price / call$0.005
Success—
StatusUnknown
How this service works
Create a chat completion using Liquid LFM 2.5 1.2B Thinking model.
Output
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.
Example use cases
Step-by-step math problem solver
“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.”
Lightweight agentic reasoning node
“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.”
Code explanation for junior developers
“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)'”
Technical details
Endpoint
POSThttps://x402.slinkylayer.ai/api/v1/proxy/:id/chat-completions-lfm-2-5-thinkingWhen to prefer
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.
Side effects
- 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
Tags
x402
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