Chat with LLMs and make embeddings

POST /chat/completions and POST /embeddings follow OpenAI's format, so any OpenAI SDK works: change the base URL to https://api.cut.pro/api/v1 and use your key. model is an id with endpoint: chat_completions or embeddings from GET /models.

import OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://api.cut.pro/api/v1", apiKey: process.env.CUTPRO_API_KEY });

const answer = await client.chat.completions.create({
	model: "anthropic/claude-sonnet-5-5",
	messages: [{ role: "user", content: "Write three titles for a video about coffee." }],
});

curl -X POST https://api.cut.pro/api/v1/chat/completions \
  -H "X-Api-Key: $CUTPRO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{ "model": "deepseek/deepseek-v4.1-flash", "messages": [{ "role": "user", "content": "Hi!" }], "stream": true }'

• Streaming: with stream: true, the answer comes as server-sent events, as with OpenAI.
• Reasoning effort: reasoning_effort (low, medium, high) or reasoning: { effort }, on the models that list it in supported_parameters.
• Tools, images and structured output: tools, image_url parts in the messages and response_format go as you send them, where the model supports them.
• Cost: by the tokens read and written, at the prices of GET /models. Fractions of a credit add up and leave the balance as whole credits. A non-streamed answer reports its cost in usage.credits.

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