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MythoMax 13B API: toman pricing and code

gryphe/mythomax-l2-13b

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Input · per 1M tokens
27,698 toman
Output · per 1M tokens
38,084 toman
One 1,000-word request ≈
86 toman

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

MythoMax 13B 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="gryphe/mythomax-l2-13b",
    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: "gryphe/mythomax-l2-13b",
  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": "gryphe/mythomax-l2-13b",
    "messages": [{"role": "user", "content": "Classify the ticket and give it a priority from 1 to 5."}],
    "response_format": {"type": "json_object"}
  }'

What is MythoMax 13B good for?

The id for MythoMax 13B in our API is "gryphe/mythomax-l2-13b", served from Gryphe. Context is 8,192 tokens; past that you have to summarise the history yourself. A single response can run to 3,686 tokens. On price it sits in the "very cheap" band — cheaper than 382 and dearer than 16 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 86 toman per 1,000-word exchange (27,698 in, 38,084 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 Phi 4: MythoMax 13B works out about 9% cheaper, 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 1,163 thousand-word requests on MythoMax 13B, and every 1,000 toman is about 11,628 words of round trip. A job with one million input tokens and one million output tokens comes to 65,782 toman in total. Filling this model's 8,192-token window costs 227 toman on the input side alone, which is the real reason to keep conversation history short.

Among the less common parameters it accepts logit_bias, logprobs, min_p, presence_penalty, repetition_penalty, top_a — 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 Gemma 2 27B at 8,192 tokens.

What to use it for: high-volume work such as classification, tagging and bulk summarising, extracting data against a fixed schema, short single-turn requests (the context window is small).

Provider's own description

One of the highest performing and most popular fine-tunes of Llama 2 13B, with rich descriptions and roleplay. #merge

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 out57 toman
Summarising a ten-page document4,000 in + 600 out134 toman
Classifying a thousand short rows120,000 in + 20,000 out4,085 toman

Price history

DateInput (toman/1M)Output (toman/1M)
16 Sep 202627,69838,084
7 Sep 202620,77320,773

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

Frequently asked

How do I call MythoMax 13B 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 "gryphe/mythomax-l2-13b". Nothing else in your code changes.
What does MythoMax 13B cost in toman?
27,698 toman per 1M input tokens and 38,084 toman per 1M output tokens; a 1,000-word request is around 86 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does MythoMax 13B take?
Up to 8,192 tokens per request, roughly 6k words. A single answer can reach 3,686 tokens.
Can I stream MythoMax 13B'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.