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Llama 3.1 8B Instruct API: toman pricing and code

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

Tool callingJSON output
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Input · per 1M tokens
17,311 toman
Output · per 1M tokens
27,698 toman
One 1,000-word request ≈
59 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 8,655 toman per 1M tokens.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))
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-3.1-8b-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: {
      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-3.1-8b-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 3.1 8B Instruct good for?

Llama 3.1 8B Instruct is one of Meta Llama'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 388 and dearer than 9 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 17,311 toman per 1M input tokens and 27,698 per 1M output tokens. A 1,000-word round trip on Llama 3.1 8B Instruct lands near 59 toman. It supports cached input: repeated context is billed at 8,655 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 about 60% 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 1,695 thousand-word requests on Llama 3.1 8B Instruct, and every 1,000 toman is about 16,949 words of round trip. A job with one million input tokens and one million output tokens comes to 45,009 toman in total. Filling this model's 131,072-token window costs 2,269 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 about 5.5× cheaper (59 against 324 toman). Maximum answer length differs as well: 117,964 against 128,000 tokens.

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, 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 out37 toman
Summarising a ten-page document4,000 in + 600 out86 toman
Classifying a thousand short rows120,000 in + 20,000 out2,631 toman

Price history

DateInput (toman/1M)Output (toman/1M)
28 Oct 202617,31127,698
28 Oct 20266,92413,849
7 Sep 202617,31127,698

Every price change for this model is recorded. Toman figures use today's rate.

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?
17,311 toman per 1M input tokens and 27,698 toman per 1M output tokens; a 1,000-word request is around 59 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.