Completions

The basic gateway call: send messages, pick a model, get a response.

The simplest gateway call: a list of messages goes in, a single response comes out. Same shape every model supports.

If you haven’t set up your SDK yet, start with Drop-in SDKs.

A minimal call

Python

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.opper.ai/v3/compat",
    api_key=os.environ["OPPER_API_KEY"],
)

r = client.chat.completions.create(
    model="openai/gpt-5-mini",
    messages=[{"role": "user", "content": "What's a vector?"}],
)
print(r.choices[0].message.content)

Pick a model

The model field is always provider-prefixed: openai/gpt-5.5, anthropic/claude-sonnet-4-6, gemini/gemini-3.1-pro-preview. Browse the full set on the Models page.

You can call any model from any SDK. The Anthropic SDK can talk to a Google model, the OpenAI SDK can talk to a Claude model. The provider prefix decides where the call routes. The SDK only sets the request shape.

If you don’t pass model, the call falls through to your default-model rule, which sets a model per organization or project so you can keep model names out of your code.

Common parameters

Parameter	What it does
temperature	0 to 2. Lower is more deterministic.
top_p	0 to 1. Nucleus sampling. Don’t use with temperature at the same time.
max_tokens	Cap the response length.
stop	A string or array of strings. The model stops as soon as it sees one.
frequency_penalty / presence_penalty	-2 to 2. Penalize repeated tokens.
n	How many response choices to generate. Default 1.

Reasoning models (the GPT-5 family, Claude with extended thinking) also accept reasoning_effort: "low" | "medium" | "high" to control how much the model “thinks” before answering.

The response shape

Response

{
  "id": "chatcmpl_...",
  "object": "chat.completion",
  "model": "openai/gpt-5-mini",
  "created": 1716124800,
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "A vector is..."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 11,
    "completion_tokens": 42,
    "total_tokens": 53
  }
}

The two things you’ll read most:

• choices[0].message.content is the assistant’s reply
• choices[0].finish_reason is why the model stopped (stop, length, tool_calls, content_filter)

What’s next

Conversations

Multi-turn chat with message history.

Tool calling

Let the model call your code.

Streaming

Stream tokens as they’re generated.

Structured output

Get JSON back instead of free text.