Nex-N2-Pro API: toman pricing and code
nex-agi/nex-n2-pro
visiontoolsreasoningYou are billed for the usage the request actually reported. Prices follow the market. Cached input: 7,253 toman / 1M.Pricing and top-ups
Nex-N2-Pro 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": "nex-agi/nex-n2-pro",
"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="nex-agi/nex-n2-pro",
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: "nex-agi/nex-n2-pro",
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 Nex-N2-Pro good for?
Nex AGI publishes this model; we expose it under the id "nex-agi/nex-n2-pro". Its context window is 262,144 tokens, roughly 197k English words in one request. A single response can run to 235,929 tokens. On price it sits in the "cheap" band — cheaper than 135 and dearer than 271 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 supports tool calling, so it can invoke your own functions with valid arguments; 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 471 toman per 1,000-word exchange (72,531 in, 290,125 out, per 1M tokens). It supports cached input: repeated context is billed at 7,253 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 GPT-5 Mini (batch): Nex-N2-Pro works out roughly 1.1× more expensive, 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 212 thousand-word requests on Nex-N2-Pro, and every 1,000 toman is about 2,123 words of round trip. A job with one million input tokens and one million output tokens comes to 362,656 toman in total. Filling this model's 262,144-token window costs 19,014 toman on the input side alone, which is the real reason to keep conversation history short.
Nex AGI has 2 models in our catalogue; the cheapest is Nex-N2-Mini at 47 toman per thousand words and the dearest Nex-N2-Pro at 471. Among the less common parameters it accepts logprobs, top_k, top_logprobs — all through the standard request body. It does not support response_format, seed, structured_outputs, 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 Trinity Large Thinking at 262,144 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, analysing a long document or codebase in one request.
Nex-N2-Pro is an agentic mixture-of-experts model from Nex AGI, with 17B active parameters out of 397B total. Built on the Qwen3.5 architecture, it accepts text and image input and produces...
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 | 225 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 464 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 14,506 toman |
Frequently asked
- How do I call Nex-N2-Pro 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 "nex-agi/nex-n2-pro". Nothing else in your code changes.
- What does Nex-N2-Pro cost in toman?
- 72,531 toman per 1M input tokens and 290,125 toman per 1M output tokens; a 1,000-word request is around 471 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Nex-N2-Pro take?
- Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 235,929 tokens.
- Does Nex-N2-Pro 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.