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Llama 3.1 Euryale 70B v2.2 API: toman pricing and code

sao10k/l3.1-euryale-70b

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Input · per 1M tokens
294,286 toman
Output · per 1M tokens
294,286 toman
One 1,000-word request ≈
765 toman

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

Llama 3.1 Euryale 70B v2.2 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="sao10k/l3.1-euryale-70b",
    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: "sao10k/l3.1-euryale-70b",
  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": "sao10k/l3.1-euryale-70b",
    "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 Euryale 70B v2.2 good for?

Sao10k publishes this model; we expose it under the id "sao10k/l3.1-euryale-70b". Its context window is 131,072 tokens, roughly 98k English words in one request. A single response can run to 16,384 tokens. On price it sits in the "cheap" band — cheaper than 267 and dearer than 130 of the other paid models in the catalogue.

What you get on top of text in, text out: 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.

Input runs at 294,286 toman per 1M tokens and output at 294,286 — output costs 1× input, so trimming the answer saves more than trimming the prompt. 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 Uncensored: Llama 3.1 Euryale 70B v2.2 works out about 50% 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 131 thousand-word requests on Llama 3.1 Euryale 70B v2.2, and every 1,000 toman is about 1,307 words of round trip. A job with one million input tokens and one million output tokens comes to 588,573 toman in total. Filling this model's 131,072-token window costs 38,573 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "sao10k/l3-lunaris-8b", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out about 19× more expensive (765 against 41 toman). The context windows differ too: 131,072 against 8,192 tokens. Maximum answer length differs as well: 16,384 against 7,372 tokens. On parameters the other takes logprobs, response_format, top_logprobs.

Sao10k has 2 models in our catalogue; the cheapest is Llama 3 8B Lunaris at 41 toman per thousand words and the dearest Llama 3.1 Euryale 70B v2.2 at 765. Among the less common parameters it accepts logit_bias, min_p, presence_penalty, repetition_penalty, top_k — 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.

Where it makes sense: high-volume work such as classification, tagging and bulk summarising, extracting data against a fixed schema.

Provider's own description

Euryale L3.1 70B v2.2 is a model focused on creative roleplay from [Sao10k](https://ko-fi.com/sao10k). It is the successor of [Euryale L3 70B v2.1](/models/sao10k/l3-euryale-70b).

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 out559 toman
Summarising a ten-page document4,000 in + 600 out1,354 toman
Classifying a thousand short rows120,000 in + 20,000 out41,200 toman

Frequently asked

How do I call Llama 3.1 Euryale 70B v2.2 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 "sao10k/l3.1-euryale-70b". Nothing else in your code changes.
What does Llama 3.1 Euryale 70B v2.2 cost in toman?
294,286 toman per 1M input tokens and 294,286 toman per 1M output tokens; a 1,000-word request is around 765 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Llama 3.1 Euryale 70B v2.2 take?
Up to 131,072 tokens per request, roughly 98k words. A single answer can reach 16,384 tokens.
Can I stream Llama 3.1 Euryale 70B v2.2'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.