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Use messages with the OpenAI-compatible chat endpoint.
POST /v1/chat/completions with a chat-completions model, Bearer authentication, a messages array, and a reasonable max_completion_tokens limit.
curl "https://YOUR-REGION.example/v1/chat/completions" \
-H "Authorization: Bearer $CODEFLARE_API_KEY" \
-H 'Content-Type: application/json' \
-d '{"model":"MODEL_ID","messages":[{"role":"user","content":"Hello"}],"max_completion_tokens":256}'
from openai import OpenAI
import os
client = OpenAI(
api_key=os.environ["CODEFLARE_API_KEY"],
base_url="https://YOUR-REGION.example/v1", max_retries=0,
)
response = client.chat.completions.create(
model="MODEL_ID",
messages=[{"role": "user", "content": "Hello"}],
max_completion_tokens=256,
)
print(response.choices[0].message.content)
import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.CODEFLARE_API_KEY,
baseURL: "https://YOUR-REGION.example/v1", maxRetries: 0,
});
const response = await client.chat.completions.create({
model: "MODEL_ID", messages: [{ role: "user", content: "Hello" }],
max_completion_tokens: 256,
});
console.log(response.choices[0].message.content);
stream: true. In Python, iterate over stream and read nonempty chunk.choices[0].delta.content. In JavaScript, use for await and read choices[0]?.delta?.content. Native HTTP SSE ends with [DONE]; do not use a Responses response.output_text.delta event parser.