xAI

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Chat models

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Long-context pricing

• The trigger is prompt size only. A long response never moves you into the higher tier.
• It applies to the whole request, not just the tokens past the threshold. A 201K-token prompt bills every one of those tokens at the doubled rate.
• Verlon bills this correctly for you. The cost field on every response already reflects whichever tier applied, so you don’t need to compute it yourself.

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Quickstart

import OpenAI from 'openai';

const openai = new OpenAI({
  baseURL: 'https://api.verlon.ai/v1',
  apiKey: process.env.VERLON_API_KEY,
});

// @ts-ignore - gateId is a Verlon AI extension
const response = await openai.chat.completions.create({
  gateId: process.env.GATE_ID,
  model: 'grok-4.5', // optional — overrides the gate's configured model
  messages: [{ role: 'user', content: 'Hello!' }],
});

console.log(response.choices[0].message.content);

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Reasoning

const response = await openai.chat.completions.create({
  gateId: process.env.GATE_ID,
  messages: [{ role: 'user', content: 'Prove there are infinitely many primes.' }],
  reasoning_effort: 'high', // 'low' | 'medium' | 'high' — xAI defaults to 'high'
});

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SDK compatibility

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Streaming

const stream = await openai.chat.completions.create({
  gateId: process.env.GATE_ID,
  messages: [{ role: 'user', content: 'Tell me a joke.' }],
  stream: true,
});

for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content ?? '');
}

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What’s supported

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Not available

• Embeddings. xAI publishes no embeddings model. Use OpenAI, Google, or Mistral embeddings and route your chat traffic to Grok — that mix is exactly what gates are for.
• Text-to-speech. xAI’s voice models are realtime/websocket-only and aren’t reachable through the standard TTS surface.
• OCR. No xAI equivalent; Mistral’s OCR line is the recommended path.

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Other modalities

• image generation — 3 models
• video generation — 2 models

const image = await openai.images.generate({
  gateId: process.env.GATE_ID,
  model: 'grok-imagine-image',
  prompt: 'A single red maple leaf on white',
  n: 1,
});

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How it works

1. You send a request in your chosen SDK’s format
2. Verlon receives — validates the gate, applies routing rules
3. Verlon translates to xAI’s format and dispatches to xAI’s API
4. xAI responds with its raw response
5. Verlon normalizes back to your SDK’s expected shape
6. You receive a response in the format your code already handles, with an added cost field

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Errors

try {
  await openai.chat.completions.create({
    gateId: process.env.GATE_ID,
    messages: [{ role: 'user', content: 'Hello!' }],
  });
} catch (error) {
  // error.status and error.message carry the API's answer,
  // e.g. 404 'Gate with ID "…" not found'
  console.error(error.status, error.message);
}

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Full non-chat model list

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Image generation

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Video generation

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Related

• OpenAI · Anthropic · Google · Mistral
• Verlon SDK reference — agent tracing, observability, and the model registry
• Gates — set up routing rules and model configuration
• Bring your own key — use your own xAI account for provider costs
• xAI’s official docs ↗