How do I call Grok 4.3 from Iran?

Sign up with your mobile number, top the wallet up in toman, create an API key, then in the official OpenAI SDK point base_url at https://api.uttapen.ir/v1 and set model to "x-ai/grok-4.3". Nothing else in your code changes.

What does Grok 4.3 cost in toman?

432,774 toman per 1M input tokens and 865,548 toman per 1M output tokens; a 1,000-word request is around 1,688 toman. You pay for the usage that request actually reported, and a failed request costs nothing.

How much input does Grok 4.3 take?

Up to 1,000,000 tokens per request, roughly 750k words. A single answer can reach 900,000 tokens.

Does Grok 4.3 support streaming and tool calling?

Streaming (stream=true) works on every model here. This one supports tool calling in the standard OpenAI shape. Image input is accepted through image_url, as a data URI or a public URL. Structured output through response_format works too.

Grok 4.3 API: toman pricing and code

x-ai/grok-4.3

Image inputTool callingReasoningJSON outputFile input

Create a key Try it

Input · per 1M tokens

432,774 toman

Output · per 1M tokens

865,548 toman

One 1,000-word request ≈

1,688 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 69,244 toman per 1M tokens. Web search: 1,731.10 toman per request.Pricing and top-ups

Grok 4.3 example: reading an image into JSON

The example is picked from this model's own capabilities. Drop in your key and it runs as is.

curlPythonNode.js

# tip: a data URI works too — base64 the file and prefix it with data:image/jpeg;base64,
curl https://api.uttapen.ir/v1/chat/completions \
  -H "Authorization: Bearer sk-up-…" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "x-ai/grok-4.3",
    "messages": [{
      "role": "user",
      "content": [
        {"type": "text", "text": "Return only the invoice number and the total, as JSON."},
        {"type": "image_url", "image_url": {"url": "https://example.com/factor.jpg"}}
      ]
    }]
  }'

import base64, json
from openai import OpenAI

client = OpenAI(base_url="https://api.uttapen.ir/v1", api_key="sk-up-…")

img = base64.b64encode(open("factor.jpg", "rb").read()).decode()

resp = client.chat.completions.create(
    model="x-ai/grok-4.3",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "Return the invoice number, the date and the total as JSON."},
            {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{img}"}},
        ],
    }],
    response_format={"type": "json_object"},
)
print(json.loads(resp.choices[0].message.content))

import OpenAI from "openai";
import { readFileSync } from "node:fs";

const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });
const img = readFileSync("factor.jpg").toString("base64");

const resp = await client.chat.completions.create({
  model: "x-ai/grok-4.3",
  messages: [{
    role: "user",
    content: [
      { type: "text", text: "Return the invoice number, the date and the total as JSON." },
      { type: "image_url", image_url: { url: `data:image/jpeg;base64,${img}` } },
    ],
  }],
  response_format: { type: "json_object" },
});
console.log(JSON.parse(resp.choices[0].message.content));

What is Grok 4.3 good for?

Grok 4.3 comes from xAI; in uttapen you reach it with the model id "x-ai/grok-4.3". It accepts up to 1,000,000 tokens of input per call, so a long document or several source files fit in a single request. A single response can run to 900,000 tokens. On price it sits in the "mid-range" band — cheaper than 160 and dearer than 224 of the other paid models in the catalogue.

Capabilities available on this id: it reads images directly, which makes it a real option for invoices, forms and screenshots; it takes files such as PDFs as input; it supports tool calling, so it can invoke your own functions with valid arguments; it returns schema-valid JSON through response_format, ready to hand to your code; it has a reasoning mode that pays off on multi-step problems, maths and debugging. All of it works through the standard parameters of the official OpenAI SDK — no custom client, no wrapper. Keep in mind that reasoning tokens are output tokens and do appear on the bill.

For a back-of-envelope figure: about 1,688 toman per 1,000-word exchange (432,774 in, 865,548 out, per 1M tokens). It supports cached input: repeated context is billed at 69,244 toman per 1M, which matters a lot if your system prompt is long. You are always charged for the usage the request actually reported, never for the estimate, and a failed request costs nothing.

The closest alternative with the same capabilities from a different provider is Nova 2 Lite: Grok 4.3 works out about 30% more expensive, and its context window is the same size. Both run on the same key and the same code, so trying the other one is a single string change.

To make the figure concrete: 100,000 toman of credit buys roughly 59 thousand-word requests on Grok 4.3, and every 1,000 toman is about 592 words of round trip. A job with one million input tokens and one million output tokens comes to 1,298,323 toman in total. Filling this model's 1,000,000-token window costs 432,774 toman on the input side alone, which is the real reason to keep conversation history short.

This id gets confused with "x-ai/grok-4.3:batch", because the underlying model is the same. The difference is the suffix: no suffix (the standard variant) against "batch". On a thousand-word request this variant works out about 30% more expensive (1,688 against 1,350 toman).

xAI has 11 models in our catalogue; the cheapest is Grok 4.3 (batch) at 1,350 toman per thousand words and the dearest Grok Latest at 3,601. Among the less common parameters it accepts logprobs, reasoning_effort, top_logprobs — all through the standard request body. By context size the nearest option from another provider is Nova 2 Lite at 1,000,000 tokens.

What to use it for: product chatbots and internal assistants, where cost and quality have to balance, logic puzzles and code review, pulling text and fields out of images, agents that reach out to APIs and databases, extracting data against a fixed schema, analysing a long document or codebase in one request.

Provider's own description

Grok 4.3 is a reasoning model from SpaceXAI. It accepts text and image inputs with text output, and is suited for agentic workflows, instruction-following tasks, and applications requiring high factual...

Three real jobs, priced on this model

Each figure is derived from the prices above and moves when they do.

Job	Tokens	Cost
One chat turn with a medium history	1,500 in + 400 out	995 toman
Summarising a ten-page document	4,000 in + 600 out	2,250 toman
Classifying a thousand short rows	120,000 in + 20,000 out	69,244 toman

Job

Tokens

Cost

One chat turn with a medium history

1,500 in + 400 out

995 toman

Summarising a ten-page document

4,000 in + 600 out

2,250 toman

Classifying a thousand short rows

120,000 in + 20,000 out

69,244 toman

Other variants of this model

Same core model, different execution terms and different price. This page is the standard variant.

Variant	Model id	Output / 1M	Context
batch	x-ai/grok-4.3:batch	692,439	1,000,000

Variant

Model id

Output / 1M

Context

batch

x-ai/grok-4.3:batch

692,439

1,000,000

Put the variant's id verbatim in the model field; nothing else in your code changes.

Frequently asked

How do I call Grok 4.3 from Iran?: Sign up with your mobile number, top the wallet up in toman, create an API key, then in the official OpenAI SDK point base_url at https://api.uttapen.ir/v1 and set model to "x-ai/grok-4.3". Nothing else in your code changes.
What does Grok 4.3 cost in toman?: 432,774 toman per 1M input tokens and 865,548 toman per 1M output tokens; a 1,000-word request is around 1,688 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Grok 4.3 take?: Up to 1,000,000 tokens per request, roughly 750k words. A single answer can reach 900,000 tokens.
Does Grok 4.3 support streaming and tool calling?: Streaming (stream=true) works on every model here. This one supports tool calling in the standard OpenAI shape. Image input is accepted through image_url, as a data URI or a public URL. Structured output through response_format works too.