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Llama 3.3 70B Instruct API: toman pricing and code

meta-llama/llama-3.3-70b-instruct

toolsjson
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Input · per 1M tokens
29,013 toman
Output · per 1M tokens
92,840 toman
One 1,000-word request ≈
158 toman

You are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups

Llama 3.3 70B Instruct example: tool calling

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-…")

tools = [{
    "type": "function",
    "function": {
        "name": "check_stock",
        "description": "Returns the stock level of a product",
        "parameters": {
            "type": "object",
            "properties": {"sku": {"type": "string"}},
            "required": ["sku"],
        },
    },
}]

messages = [{"role": "user", "content": "How many of the Nike NK-42 shoe are in stock?"}]
first = client.chat.completions.create(model="meta-llama/llama-3.3-70b-instruct", messages=messages, tools=tools)
call = first.choices[0].message.tool_calls[0]

# you run the function yourself — the model never touches the database
args = json.loads(call.function.arguments)
result = {"sku": args["sku"], "qty": 7}

messages += [first.choices[0].message, {"role": "tool", "tool_call_id": call.id, "content": json.dumps(result)}]
final = client.chat.completions.create(model="meta-llama/llama-3.3-70b-instruct", messages=messages, tools=tools)
print(final.choices[0].message.content)

What is Llama 3.3 70B Instruct good for?

Llama 3.3 70B Instruct comes from Meta; in uttapen you reach it with the model id "meta-llama/llama-3.3-70b-instruct". It accepts up to 131,072 tokens of input per call, so a long document or several source files fit in a single request. A single response can run to 16,384 tokens. On price it sits in the "very cheap" band — cheaper than 67 and dearer than 339 of the other paid models in the catalogue.

What it can do beyond plain text: 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.

Pricing is 29,013 toman per 1M input tokens and 92,840 per 1M output tokens. A 1,000-word round trip on Llama 3.3 70B Instruct lands near 158 toman. 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 Voxtral Small 24B 2507: Llama 3.3 70B 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 633 thousand-word requests on Llama 3.3 70B Instruct, and every 1,000 toman is about 6,329 words of round trip. A job with one million input tokens and one million output tokens comes to 121,853 toman in total. Filling this model's 131,072-token window costs 3,803 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "meta-llama/llama-3.1-8b-instruct", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out 3.2× dearer (158 against 49 toman). Maximum answer length differs as well: 16,384 against 117,964 tokens.

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, 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.

Good fits: high-volume work such as classification, tagging and bulk summarising, agents that reach out to APIs and databases, extracting data against a fixed schema.

Provider's own description

The Meta Llama 3.3 multilingual large language model (LLM) is a pretrained and instruction tuned generative model in 70B (text in/text out). The Llama 3.3 instruction tuned text only model...

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 out81 toman
Summarising a ten-page document4,000 in + 600 out172 toman
Classifying a thousand short rows120,000 in + 20,000 out5,338 toman

Frequently asked

How do I call Llama 3.3 70B 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-3.3-70b-instruct". Nothing else in your code changes.
What does Llama 3.3 70B Instruct cost in toman?
29,013 toman per 1M input tokens and 92,840 toman per 1M output tokens; a 1,000-word request is around 158 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Llama 3.3 70B Instruct take?
Up to 131,072 tokens per request, roughly 98k words. A single answer can reach 16,384 tokens.
Does Llama 3.3 70B Instruct support streaming and tool calling?
Streaming (stream=true) works on every model here. This one supports tool calling in the standard OpenAI shape. Structured output through response_format works too.