How do I call Particle-1 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 "particle-ai/particle-1". Nothing else in your code changes.

What does Particle-1 cost in toman?

17,311 toman per 1M input tokens and 173,110 toman per 1M output tokens; a 1,000-word request is around 248 toman. You pay for the usage that request actually reported, and a failed request costs nothing.

How much input does Particle-1 take?

Up to 1,048,576 tokens per request, roughly 786k words. A single answer can reach 131,072 tokens.

Does Particle-1 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.

Particle-1 API: toman pricing and code

particle-ai/particle-1

Image inputTool callingReasoningJSON output

Create a key Try it

Input · per 1M tokens

17,311 toman

Output · per 1M tokens

173,110 toman

One 1,000-word request ≈

248 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 1,039 toman per 1M tokens.Pricing and top-ups

Particle-1 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": "particle-ai/particle-1",
    "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="particle-ai/particle-1",
    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: "particle-ai/particle-1",
  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 Particle-1 good for?

Particle-1 is one of Particle Ai's models. Put "particle-ai/particle-1" in the model field and the rest of your code stays as it is. Particle-1 keeps 1,048,576 tokens in view at once, and that number is what caps the conversation history in your product. A single response can run to 131,072 tokens. On price it sits in the "very cheap" band — cheaper than 299 and dearer than 86 of the other paid models in the catalogue.

What it can do beyond plain text: 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 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.

Pricing is 17,311 toman per 1M input tokens and 173,110 per 1M output tokens. A 1,000-word round trip on Particle-1 lands near 248 toman. It supports cached input: repeated context is billed at 1,039 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 Claude Haiku 5.5: Particle-1 works out about 9% cheaper, 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 403 thousand-word requests on Particle-1, and every 1,000 toman is about 4,032 words of round trip. A job with one million input tokens and one million output tokens comes to 190,421 toman in total. Filling this model's 1,048,576-token window costs 18,152 toman on the input side alone, which is the real reason to keep conversation history short.

Among the less common parameters it accepts logprobs, min_p, presence_penalty, reasoning_effort, repetition_penalty, top_k — all through the standard request body. By context size the nearest option from another provider is DeepSeek V4 Flash 0423 at 1,048,576 tokens.

Good fits: 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.

Provider's own description

Particle-1 is a token-efficient agentic coding model from Particle AI, post-trained on DeepSeek V4.1 Flash. It is suited for software engineering work such as terminal workflows, codebase changes, and data...

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	95 toman
Summarising a ten-page document	4,000 in + 600 out	173 toman
Classifying a thousand short rows	120,000 in + 20,000 out	5,540 toman

Job

Tokens

Cost

One chat turn with a medium history

1,500 in + 400 out

95 toman

Summarising a ten-page document

4,000 in + 600 out

173 toman

Classifying a thousand short rows

120,000 in + 20,000 out

5,540 toman

Price history

Date	Input (toman/1M)	Output (toman/1M)
31 Oct 2026	17,311	173,110
28 Oct 2026	86,555	346,219

Every price change for this model is recorded. Toman figures use today's rate.

Frequently asked

How do I call Particle-1 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 "particle-ai/particle-1". Nothing else in your code changes.
What does Particle-1 cost in toman?: 17,311 toman per 1M input tokens and 173,110 toman per 1M output tokens; a 1,000-word request is around 248 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Particle-1 take?: Up to 1,048,576 tokens per request, roughly 786k words. A single answer can reach 131,072 tokens.
Does Particle-1 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.