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Llama 3.1 8B Instruct API: toman pricing and code

meta-llama/llama-3.1-8b-instruct

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Input · per 1M tokens
14,506 toman
Output · per 1M tokens
23,210 toman
One 1,000-word request ≈
49 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 7,253 toman / 1M.Pricing and top-ups

Llama 3.1 8B 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-3.1-8b-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 3.1 8B Instruct good for?

Llama 3.1 8B Instruct is one of Meta's models. Put "meta-llama/llama-3.1-8b-instruct" in the model field and the rest of your code stays as it is. Llama 3.1 8B 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 6 and dearer than 400 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 14,506 toman per 1M input tokens and 23,210 per 1M output tokens. A 1,000-word round trip on Llama 3.1 8B Instruct lands near 49 toman. It supports cached input: repeated context is billed at 7,253 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 Ling 3.0 Flash: Llama 3.1 8B Instruct works out roughly 1.5× 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 2,041 thousand-word requests on Llama 3.1 8B Instruct, and every 1,000 toman is about 20,408 words of round trip. A job with one million input tokens and one million output tokens comes to 37,716 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.

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 3.2× cheaper (49 against 158 toman). Maximum answer length differs as well: 117,964 against 16,384 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

Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 8B instruct-tuned version is fast and efficient. It has demonstrated strong performance compared to...

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 out31 toman
Summarising a ten-page document4,000 in + 600 out72 toman
Classifying a thousand short rows120,000 in + 20,000 out2,205 toman

Frequently asked

How do I call Llama 3.1 8B 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-3.1-8b-instruct". Nothing else in your code changes.
What does Llama 3.1 8B Instruct cost in toman?
14,506 toman per 1M input tokens and 23,210 toman per 1M output tokens; a 1,000-word request is around 49 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Llama 3.1 8B Instruct take?
Up to 131,072 tokens per request, roughly 98k words. A single answer can reach 117,964 tokens.
Does Llama 3.1 8B 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.