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GPT Chat Latest API: toman pricing and code

openai/gpt-chat-latest

Image inputTool callingJSON outputFile input
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Input · per 1M tokens
1,731,097 toman
Output · per 1M tokens
10,386,581 toman
One 1,000-word request ≈
15,753 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 173,110 toman per 1M tokens. Web search: 3,462.19 toman per request.Pricing and top-ups

GPT Chat Latest 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": "openai/gpt-chat-latest",
    "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="openai/gpt-chat-latest",
    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: "openai/gpt-chat-latest",
  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 GPT Chat Latest good for?

The id for GPT Chat Latest in our API is "openai/gpt-chat-latest", served from OpenAI. Context is 400,000 tokens; past that you have to summarise the history yourself. A single response can run to 128,000 tokens. On price it sits in the "expensive" band — cheaper than 13 and dearer than 382 of the other paid models in the catalogue.

What you get on top of text in, text out: 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. All of it works through the standard parameters of the official OpenAI SDK — no custom client, no wrapper.

Input runs at 1,731,097 toman per 1M tokens and output at 10,386,581 — output costs 6× input, so trimming the answer saves more than trimming the prompt. It supports cached input: repeated context is billed at 173,110 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 Fugu Ultra, and GPT Chat Latest costs about the same, and its context window is smaller. 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 6 thousand-word requests on GPT Chat Latest, and every 1,000 toman is about 63 words of round trip. A job with one million input tokens and one million output tokens comes to 12,117,677 toman in total. Filling this model's 400,000-token window costs 692,439 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "openai/gpt-4o-2024-05-13", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out about 70% more expensive (15,753 against 9,002 toman). The context windows differ too: 400,000 against 128,000 tokens. Maximum answer length differs as well: 128,000 against 4,096 tokens. On parameters, this one takes max_tokens while the other takes frequency_penalty, logit_bias, logprobs, max_completion_tokens.

OpenAI has 81 models in our catalogue; the cheapest is gpt-oss-20b (batch) at 61 toman per thousand words and the dearest GPT-5.5 Pro at 94,518. It does not support include_reasoning, reasoning, temperature, top_p, which most models here do, so test before switching if your code relies on them. By context size the nearest option from another provider is Llama 4 Scout at 327,680 tokens.

Where it makes sense: work where the quality of the answer matters more than its cost, 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

GPT Chat Latest points to OpenAI's stable API alias `chat-latest` that always resolves to the latest Instant chat model used in ChatGPT. As OpenAI rolls out new Instant model updates...

Three real jobs, priced on this model

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

JobTokensCost
One chat turn with a medium history1,500 in + 400 out6,751 toman
Summarising a ten-page document4,000 in + 600 out13,156 toman
Classifying a thousand short rows120,000 in + 20,000 out415,463 toman

Frequently asked

How do I call GPT Chat Latest 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 "openai/gpt-chat-latest". Nothing else in your code changes.
What does GPT Chat Latest cost in toman?
1,731,097 toman per 1M input tokens and 10,386,581 toman per 1M output tokens; a 1,000-word request is around 15,753 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does GPT Chat Latest take?
Up to 400,000 tokens per request, roughly 300k words. A single answer can reach 128,000 tokens.
Does GPT Chat Latest 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.