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Llama 3.1 70B Instruct API: toman pricing and code

meta-llama/llama-3.1-70b-instruct

toolsjson
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Input · per 1M tokens
116,050 toman
Output · per 1M tokens
116,050 toman
One 1,000-word request ≈
302 toman

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

Llama 3.1 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-3.1-70b-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-3.1-70b-instruct", messages=messages, tools=tools)
print(final.choices[0].message.content)

What is Llama 3.1 70B Instruct good for?

The id for Llama 3.1 70B Instruct in our API is "meta-llama/llama-3.1-70b-instruct", served from Meta. Context is 131,072 tokens; past that you have to summarise the history yourself. A single response can run to 16,384 tokens. On price it sits in the "very cheap" band — cheaper than 71 and dearer than 335 of the other paid models in the catalogue.

Capabilities available on this id: 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.

For a back-of-envelope figure: about 302 toman per 1,000-word exchange (116,050 in, 116,050 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 DeepSeek V3.2: Llama 3.1 70B Instruct works out roughly 1.2× 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 331 thousand-word requests on Llama 3.1 70B Instruct, and every 1,000 toman is about 3,311 words of round trip. A job with one million input tokens and one million output tokens comes to 232,100 toman in total. Filling this model's 131,072-token window costs 15,211 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-3.3-70b-instruct", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out 1.9× dearer (302 against 158 toman). On parameters the other takes logprobs, top_logprobs.

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, min_p, repetition_penalty, top_k — 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.

What to use it for: 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

Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 70B instruct-tuned version is optimized for high quality dialogue usecases. It has demonstrated strong...

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 out220 toman
Summarising a ten-page document4,000 in + 600 out534 toman
Classifying a thousand short rows120,000 in + 20,000 out16,247 toman

Frequently asked

How do I call Llama 3.1 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-3.1-70b-instruct". Nothing else in your code changes.
What does Llama 3.1 70B Instruct cost in toman?
116,050 toman per 1M input tokens and 116,050 toman per 1M output tokens; a 1,000-word request is around 302 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Llama 3.1 70B Instruct take?
Up to 131,072 tokens per request, roughly 98k words. A single answer can reach 16,384 tokens.
Does Llama 3.1 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.