Quickstart

Celeris provides celeris-1, a diffusion language model for short, structured responses, through an OpenAI-compatible API. Existing OpenAI clients can connect by using the Celeris base URL and API key.

1. Create an account

Sign up at the Customer Console with Google or an email address and password (email signups get a verification code).

2. Activate your workspace

A workspace must be activated before it can serve requests. In the Console, open the account menu in the bottom-left, select Settings → Billing, and add a payment method. This requests activation, at no charge, and is usually immediate; if it is not, the Console tells you and we email you once the workspace is activated. Then add credit.

3. Reveal your API key

Activating the workspace gives you a private API key named Default. In the Console, open API keys and reveal it. Store the full ck_ value in a secret manager and keep it out of source control:

export CELERIS_API_KEY="ck_..."

You can create additional private keys, or reveal, rotate, and revoke your existing keys from the Console. See Authentication.

4. Make your first request

The API base URL includes the model's name (see Models):

https://inference.celeris.ai/celeris-1/v1

cURL

curl https://inference.celeris.ai/celeris-1/v1/chat/completions \
  -H "Authorization: Bearer $CELERIS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "celeris-1",
    "messages": [
      {"role": "user", "content": "What is the capital of France? Reply with one word."}
    ],
    "max_tokens": 256,
    "temperature": 0
  }'

Python

# pip install openai
import os
from openai import OpenAI

client = OpenAI(
    base_url="https://inference.celeris.ai/celeris-1/v1",
    api_key=os.environ["CELERIS_API_KEY"],
)

response = client.chat.completions.create(
    model="celeris-1",
    messages=[
        {
            "role": "user",
            "content": "What is the capital of France? Reply with one word.",
        }
    ],
    max_tokens=256,
    temperature=0,
)
print(response.choices[0].message.content)

TypeScript

// npm install openai
import OpenAI from 'openai';

const client = new OpenAI({
  baseURL: 'https://inference.celeris.ai/celeris-1/v1',
  apiKey: process.env.CELERIS_API_KEY,
});

const response = await client.chat.completions.create({
  model: 'celeris-1',
  messages: [
    {
      role: 'user',
      content: 'What is the capital of France? Reply with one word.',
    },
  ],
  max_tokens: 256,
  temperature: 0,
});
console.log(response.choices[0].message.content);

Synced language tabs

Selecting a language updates the examples on other pages.

celeris-1 accepts any positive max_tokens. Prompt tokens plus max_tokens must be at most 131,072, so the accepted input limit is 131,072 - max_tokens.

5. Read the response

Responses use the standard OpenAI chat-completion shape. The quickstart returns one complete JSON response; OpenAI-compatible token streaming is also supported (Making requests):

{
  "id": "chatcmpl-9f2e41c0",
  "object": "chat.completion",
  "created": 1751673600,
  "model": "celeris-1",
  "choices": [
    {
      "index": 0,
      "message": {"role": "assistant", "content": "Paris"},
      "finish_reason": "stop"
    }
  ],
  "usage": {"prompt_tokens": 27, "completion_tokens": 2, "total_tokens": 29}
}

Every successful response includes a Server-Timing header with server-side latency durations. Error responses may omit usage and Server-Timing; see Errors.

Next steps

• Authentication: key lifecycle, rotation, and handling.
• Models: what celeris-1 is good at, and its limits.
• Prompt engineering: how to prompt a diffusion LLM for reliable structured output.
• Agentic workloads: request patterns the model excels at, such as classify, extract, judge, and rewrite.
• Rate limits: what's limited and how to back off cleanly.
• Pricing: token rates and how to estimate costs.
• Going to production: the production-readiness checklist.
• Use the Playground in the Console to test prompts without writing client code.