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Llama 3.2 3B Instruct API: toman pricing and code

meta-llama/llama-3.2-3b-instruct

json
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Input · per 1M tokens
14,506 toman
Output · per 1M tokens
95,741 toman
One 1,000-word request ≈
143 toman

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))

What is Llama 3.2 3B Instruct good for?

Llama 3.2 3B Instruct is one of Meta's models. Put "meta-llama/llama-3.2-3b-instruct" in the model field and the rest of your code stays as it is. Llama 3.2 3B Instruct keeps 131,072 tokens in view at once, and that number is what caps the conversation history in your product. A single response can run to 117,964 tokens. On price it sits in the "very cheap" band — cheaper than 68 and dearer than 338 of the other paid models in the catalogue.

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 143 toman per 1,000-word exchange (14,506 in, 95,741 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: Llama 3.2 3B Instruct works out roughly 1× 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 699 thousand-word requests on Llama 3.2 3B Instruct, and every 1,000 toman is about 6,993 words of round trip. A job with one million input tokens and one million output tokens comes to 110,248 toman in total. Filling this model's 131,072-token window costs 1,901 toman on the input side alone, which is the real reason to keep conversation history short.

Meta has 8 models in our catalogue; the cheapest is Llama 3.1 8B Instruct at 49 toman per thousand words and the dearest Llama 4 Maverick at 338. Among the less common parameters it accepts logit_bias, logprobs, min_p, repetition_penalty, top_k, top_logprobs — 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.

Provider's own description

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.

JobTokensCost
One chat turn with a medium history1,500 in + 400 out60 toman
Summarising a ten-page document4,000 in + 600 out115 toman
Classifying a thousand short rows120,000 in + 20,000 out3,656 toman

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?
14,506 toman per 1M input tokens and 95,741 toman per 1M output tokens; a 1,000-word request is around 143 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 117,964 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.