Llama 3.2 3B Instruct API: toman pricing and code
meta-llama/llama-3.2-3b-instruct
You are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups
Llama 3.2 3B Instruct example: JSON output against a schema
The example is picked from this model's own capabilities. Drop in your key and it runs as is.
from openai import OpenAI
import json
client = OpenAI(base_url="https://api.uttapen.ir/v1", api_key="sk-up-…")
schema = {
"name": "ticket",
"schema": {
"type": "object",
"properties": {
"category": {"type": "string", "enum": ["fani", "mali", "forush"]},
"priority": {"type": "integer", "minimum": 1, "maximum": 5},
"summary": {"type": "string"},
},
"required": ["category", "priority", "summary"],
"additionalProperties": False,
},
"strict": True,
}
resp = client.chat.completions.create(
model="meta-llama/llama-3.2-3b-instruct",
messages=[{"role": "user", "content": "Ticket: "For two days I cannot download my invoice and I was charged twice.""}],
response_format={"type": "json_schema", "json_schema": schema},
)
print(json.loads(resp.choices[0].message.content))import OpenAI from "openai";
const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });
const resp = await client.chat.completions.create({
model: "meta-llama/llama-3.2-3b-instruct",
messages: [{ role: "user", content: "Ticket: "For two days I cannot download my invoice and I was charged twice."" }],
response_format: {
type: "json_schema",
json_schema: {
name: "ticket",
strict: true,
schema: {
type: "object",
properties: {
category: { type: "string", enum: ["fani", "mali", "forush"] },
priority: { type: "integer", minimum: 1, maximum: 5 },
summary: { type: "string" },
},
required: ["category", "priority", "summary"],
additionalProperties: false,
},
},
},
});
console.log(JSON.parse(resp.choices[0].message.content));curl https://api.uttapen.ir/v1/chat/completions \
-H "Authorization: Bearer sk-up-…" \
-H "Content-Type: application/json" \
-d '{
"model": "meta-llama/llama-3.2-3b-instruct",
"messages": [{"role": "user", "content": "Classify the ticket and give it a priority from 1 to 5."}],
"response_format": {"type": "json_object"}
}'What is Llama 3.2 3B Instruct good for?
Llama 3.2 3B Instruct is one of Meta Llama's models. Put "meta-llama/
Capabilities available on this id: 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.
For a back-of-envelope figure: about 174 toman per 1,000-word exchange (17,623 in, 115,983 out, per 1M tokens). 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 Hy-MT2-30B-A3B, and Llama 3.2 3B Instruct costs about the same, 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 575 thousand-word requests on Llama 3.2 3B Instruct, and every 1,000 toman is about 5,747 words of round trip. A job with one million input tokens and one million output tokens comes to 133,606 toman in total. Filling this model's 131,072-token window costs 2,310 toman on the input side alone, which is the real reason to keep conversation history short.
Meta Llama has 8 models in our catalogue; the cheapest is Llama 3.1 8B Instruct at 59 toman per thousand words and the dearest Llama 3.1 70B Instruct at 648. Among the less common parameters it accepts logit_bias, logprobs, min_p, presence_penalty, repetition_penalty, top_k — all through the standard request body. It does not support include_reasoning, reasoning, response_format, tool_choice, 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 Aion-2.0 at 131,072 tokens.
What to use it for: high-volume work such as classification, tagging and bulk summarising, extracting data against a fixed schema.
Llama 3.2 3B is a 3-billion-parameter multilingual large language model, optimized for advanced natural language processing tasks like dialogue generation, reasoning, and summarization. Designed with the latest transformer architecture, it...
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 | 73 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 140 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 4,434 toman |
Price history
| Date | Input (toman/1M) | Output (toman/1M) |
|---|---|---|
| 4 Nov 2026 | 17,623 | 115,983 |
| 25 Oct 2026 | 17,311 | 114,252 |
| 19 Oct 2026 | 17,623 | 115,983 |
| 15 Oct 2026 | 17,311 | 114,252 |
| 15 Oct 2026 | 17,623 | 115,983 |
| 7 Sep 2026 | 17,311 | 114,252 |
Every price change for this model is recorded. Toman figures use today's rate.
Frequently asked
- How do I call Llama 3.2 3B Instruct 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 "meta-llama/ llama-3.2-3b-instruct". Nothing else in your code changes. - What does Llama 3.2 3B Instruct cost in toman?
- 17,623 toman per 1M input tokens and 115,983 toman per 1M output tokens; a 1,000-word request is around 174 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Llama 3.2 3B Instruct take?
- Up to 131,072 tokens per request, roughly 98k words. A single answer can reach 72,000 tokens.
- Can I stream Llama 3.2 3B Instruct's output?
- Yes — with stream=true you get SSE events as the tokens are produced. This model has no tool calling and no image input, so pick a different one if you need either.