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Llama 3.3 Euryale 70B API: toman pricing and code

sao10k/l3.3-euryale-70b

json
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Input · per 1M tokens
188,581 toman
Output · per 1M tokens
217,594 toman
One 1,000-word request ≈
528 toman

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

Llama 3.3 Euryale 70B 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/l3.3-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))

What is Llama 3.3 Euryale 70B good for?

Llama 3.3 Euryale 70B is one of Sao10k's models. Put "sao10k/l3.3-euryale-70b" in the model field and the rest of your code stays as it is. Llama 3.3 Euryale 70B 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 16,384 tokens. On price it sits in the "cheap" band — cheaper than 113 and dearer than 293 of the other paid models in the catalogue.

Capabilities available on this id: 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 528 toman per 1,000-word exchange (188,581 in, 217,594 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 Weaver (alpha): Llama 3.3 Euryale 70B works out roughly 1.2× 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 189 thousand-word requests on Llama 3.3 Euryale 70B, and every 1,000 toman is about 1,894 words of round trip. A job with one million input tokens and one million output tokens comes to 406,175 toman in total. Filling this model's 131,072-token window costs 24,718 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 15.5× dearer (528 against 34 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 logit_bias, min_p, top_k.

Sao10k has 3 models in our catalogue; the cheapest is Llama 3 8B Lunaris at 34 toman per thousand words and the dearest Llama 3.1 Euryale 70B v2.2 at 641. Among the less common parameters it accepts logprobs, repetition_penalty, top_logprobs — all through the standard request body. It does not support include_reasoning, reasoning, tool_choice, tools, 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, extracting data against a fixed schema.

Provider's own description

Euryale L3.3 70B is a model focused on creative roleplay from [Sao10k](https://ko-fi.com/sao10k). It is the successor of [Euryale L3 70B v2.2](/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 out370 toman
Summarising a ten-page document4,000 in + 600 out885 toman
Classifying a thousand short rows120,000 in + 20,000 out26,982 toman

Frequently asked

How do I call Llama 3.3 Euryale 70B 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/l3.3-euryale-70b". Nothing else in your code changes.
What does Llama 3.3 Euryale 70B cost in toman?
188,581 toman per 1M input tokens and 217,594 toman per 1M output tokens; a 1,000-word request is around 528 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Llama 3.3 Euryale 70B take?
Up to 131,072 tokens per request, roughly 98k words. A single answer can reach 16,384 tokens.
Can I stream Llama 3.3 Euryale 70B'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.