Grok 4.3

xAI's latest reasoning model — strong STEM, coding, and step-by-step problem solving at competitive pricing, with a 1M token context window.

POST/v1/ai/chat

$0.01/call

Overview

xAI's latest reasoning model — strong STEM, coding, and step-by-step problem solving at competitive pricing, with a 1M token context window.

Property	Value
Model ID	`x-ai/grok-4.3`
Context Window	1,000,000 tokens
Max Output	32,000 tokens
Input Price	$1.25 / 1M tokens
Output Price	$2.50 / 1M tokens

Usage

const res = await fetch('https://api.yepapi.com/v1/ai/chat', {
  method: 'POST',
  headers: {
    'x-api-key': 'YOUR_API_KEY',
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    model: 'x-ai/grok-4.3',
    messages: [{ role: 'user', content: 'Explain how transformer attention works, then sketch a minimal PyTorch implementation.' }],
  }),
});
const { data } = await res.json();
console.log(data.message.content);

curl -X POST https://api.yepapi.com/v1/ai/chat \
  -H "x-api-key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "x-ai/grok-4.3", "messages": [{"role": "user", "content": "Explain how transformer attention works, then sketch a minimal PyTorch implementation."}]}'

Request Body

Parameter	Type	Required	Description	Default
`model`	`string`	Yes	Model ID (e.g. `x-ai/grok-4.3`)	—
`messages`	`Message[]`	Yes	Array of `{ role, content }` objects	—
`maxTokens`	`number`	No	Maximum tokens in the response	Model default
`temperature`	`number`	No	Sampling temperature (0.0–2.0)	`1.0`
`topP`	`number`	No	Nucleus sampling threshold	`1.0`
`frequencyPenalty`	`number`	No	Penalize repeated tokens	`0`
`presencePenalty`	`number`	No	Penalize tokens already present	`0`
`stream`	`boolean`	No	Enable SSE streaming	`false`

Info

All AI models use the /v1/ai/chat endpoint. Specify the model with the model field.

Response

{
  "ok": true,
  "data": {
    "model": "x-ai/grok-4.3",
    "message": {
      "role": "assistant",
      "content": "Attention computes a weighted sum of value vectors, where weights come from the scaled dot product of queries and keys passed through a softmax: Attention(Q,K,V) = softmax(QKᵀ/√d)V. A minimal PyTorch head: scores = (Q @ K.transpose(-2,-1)) / d**0.5; weights = scores.softmax(-1); out = weights @ V."
    },
    "usage": {
      "promptTokens": 16,
      "completionTokens": 245,
      "totalTokens": 261
    }
  }
}

Streaming

Set "stream": true to receive Server-Sent Events. Each chunk contains a delta object:

data: {"delta":{"content":"Attention"},"model":"x-ai/grok-4.3","index":0}
data: [DONE]

Under the Hood

We handle auth, billing, and response normalization — you just send messages.