Model APIs quickstart

Model APIs are open-weight models we host for you, called over an OpenAI-compatible HTTP API. Base URL https://api.runinfra.ai/v1, a workspace API key, POST /chat/completions. You pay per token from your workspace balance, and new accounts start with $1 of credit.

Get a workspace API key

Open the Model Library, pick a model, and create or reveal a workspace key on its page. The secret is shown once.

Put the key in your environment

export RUNINFRA_GATEWAY_KEY="rp_your_key_here"

Make the call

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.runinfra.ai/v1",
    api_key=os.environ["RUNINFRA_GATEWAY_KEY"],
)
response = client.chat.completions.create(
    model="deepseek-v4-flash",
    messages=[{"role": "user", "content": "Write a TypeScript retry helper."}],
    max_tokens=16384,
)
print(response.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.runinfra.ai/v1",
  apiKey: process.env.RUNINFRA_GATEWAY_KEY,
});
const response = await client.chat.completions.create({
  model: "deepseek-v4-flash",
  messages: [{ role: "user", content: "Write a TypeScript retry helper." }],
  max_tokens: 16384,
});
console.log(response.choices[0]?.message?.content);

curl https://api.runinfra.ai/v1/chat/completions \
  -H "Authorization: Bearer $RUNINFRA_GATEWAY_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"deepseek-v4-flash","messages":[{"role":"user","content":"Write a TypeScript retry helper."}],"max_tokens":16384}'

What comes back

A normal completion. Token counts and ids are illustrative; reasoning carries the model’s thinking, and content carries the answer.

{
  "id": "chatcmpl-example",
  "object": "chat.completion",
  "model": "deepseek-v4-flash",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "reasoning": "The user wants a retry helper. I should use exponential backoff...",
        "content": "export async function retry<T>(fn: () => Promise<T>, attempts = 3) { ... }"
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 14,
    "completion_tokens": 892,
    "total_tokens": 906
  }
}

Check finish_reason before you use content. stop means the model finished on its own. length means it hit the output budget, and on a reasoning model that can leave content empty with the budget spent on thinking, which is what the next section is about.

The live model ids

deepseek-v4-flash, deepseek-v4-pro, qwen3-8-27b, qwen3-8-2-4t-a95b, nemotron-3-5-lightning-30b, ornith-1-5-35b. Any of these goes in the model field. GET /v1/models is the authoritative live list, each model’s page in the Model Library carries its prices and limits, and what each model supports is one table.

How usage is counted

The serving engine’s own token count is the billing count. For chat completions, cached input appears at usage.prompt_tokens_details.cached_tokens when the engine reports it. Those tokens remain part of prompt_tokens and are billed at the model’s cached-input rate. A client-side tokenizer can disagree with the engine because it may use a different tokenizer revision or omit the exact chat template, special tokens, and media preprocessing applied by the serving stack. Use the response usage fields and the usage dashboard for reconciliation.

Why max_tokens is 16,384

These models reason before they answer, and reasoning tokens are billed output that count toward max_tokens.

You pay for what is generated, not for the ceiling you allow, so a generous ceiling costs nothing and a small one can cost you a whole request.

Next steps

Chat completions

Every field the gateway forwards, validates, or drops.

Streaming

Read deltas and ask for the usage frame.

Errors

What each refusal means and whether to retry.

Start here

Guides

API reference

Model APIs quickstart

The live model ids

How usage is counted

Why max_tokens is 16,384

Next steps

Chat completions

Streaming

Errors

​The live model ids

​How usage is counted

​Why max_tokens is 16,384

​Next steps

Chat completions

Streaming

Errors

The live model ids

How usage is counted

Why max_tokens is 16,384

Next steps