Grok 4.3 (batch) API: toman pricing and code
x-ai/grok-4.3:batch
visiontoolsreasoningjsonfilesYou are billed for the usage the request actually reported. Prices follow the market. Cached input: 46,420 toman / 1M. Web search: 1,450.63 toman / request.Pricing and top-ups
Grok 4.3 (batch) 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.
# 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:batch",
"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:batch",
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:batch",
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 (batch) good for?
The id for Grok 4.3 (batch) in our API is "x-ai/grok-4.3:batch", served from xAI. Context is 1,000,000 tokens; past that you have to summarise the history yourself. A single response can run to 900,000 tokens. On price it sits in the "cheap" band — cheaper than 200 and dearer than 206 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 1,131 toman per 1,000-word exchange (290,125 in, 580,250 out, per 1M tokens). It supports cached input: repeated context is billed at 46,420 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 Seed 1.6: Grok 4.3 (batch) works out roughly 1.3× more expensive, and its context window is larger. 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 88 thousand-word requests on Grok 4.3 (batch), and every 1,000 toman is about 884 words of round trip. A job with one million input tokens and one million output tokens comes to 870,375 toman in total. Filling this model's 1,000,000-token window costs 290,125 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", because the underlying model is the same. The difference is the suffix: "batch" against the standard variant. On a thousand-word request this variant works out 1.3× cheaper (1,131 against 1,414 toman).
xAI has 8 models in our catalogue; the cheapest is Grok 4.3 (batch) at 1,131 toman per thousand words and the dearest Grok Latest at 3,017. 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: high-volume work such as classification, tagging and bulk summarising, 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.
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 | 667 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 1,509 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 46,420 toman |
Other variants of this model
Same core model, different execution terms and different price. This page is the batch (cheaper, slower) variant.
| Variant | Model id | Output / 1M | Context |
|---|---|---|---|
| standard | x-ai/grok-4.3 | 725,313 | 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 (batch) 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:batch". Nothing else in your code changes.
- What does Grok 4.3 (batch) cost in toman?
- 290,125 toman per 1M input tokens and 580,250 toman per 1M output tokens; a 1,000-word request is around 1,131 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Grok 4.3 (batch) take?
- Up to 1,000,000 tokens per request, roughly 750k words. A single answer can reach 900,000 tokens.
- Does Grok 4.3 (batch) 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.