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Magnum v4 72B API: toman pricing and code

anthracite-org/magnum-v4-72b

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Input · per 1M tokens
865,548 toman
Output · per 1M tokens
1,731,097 toman
One 1,000-word request ≈
3,376 toman

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

Magnum v4 72B 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="anthracite-org/magnum-v4-72b",
    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: "anthracite-org/magnum-v4-72b",
  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": "anthracite-org/magnum-v4-72b",
    "messages": [{"role": "user", "content": "Classify the ticket and give it a priority from 1 to 5."}],
    "response_format": {"type": "json_object"}
  }'

What is Magnum v4 72B good for?

The id for Magnum v4 72B in our API is "anthracite-org/magnum-v4-72b", served from Anthracite Org. Context is 32,768 tokens; past that you have to summarise the history yourself. A single response can run to 4,096 tokens. On price it sits in the "mid-range" band — cheaper than 108 and dearer than 279 of the other paid models in the catalogue.

What it can do beyond plain text: 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 865,548 toman per 1M input tokens and 1,731,097 per 1M output tokens. A 1,000-word round trip on Magnum v4 72B lands near 3,376 toman. 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 Palmyra X5: Magnum v4 72B works out about 10% 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 30 thousand-word requests on Magnum v4 72B, and every 1,000 toman is about 296 words of round trip. A job with one million input tokens and one million output tokens comes to 2,596,645 toman in total. Filling this model's 32,768-token window costs 28,362 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 Aion-RP 1.0 (8B) at 32,768 tokens.

Good fits: product chatbots and internal assistants, where cost and quality have to balance, extracting data against a fixed schema.

Provider's own description

This is a series of models designed to replicate the prose quality of the Claude 3 models, specifically Sonnet and Opus. The model is fine-tuned on top of Qwen2.5 72B.

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 out1,991 toman
Summarising a ten-page document4,000 in + 600 out4,501 toman
Classifying a thousand short rows120,000 in + 20,000 out138,488 toman

Frequently asked

How do I call Magnum v4 72B 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 "anthracite-org/magnum-v4-72b". Nothing else in your code changes.
What does Magnum v4 72B cost in toman?
865,548 toman per 1M input tokens and 1,731,097 toman per 1M output tokens; a 1,000-word request is around 3,376 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Magnum v4 72B take?
Up to 32,768 tokens per request, roughly 25k words. A single answer can reach 4,096 tokens.
Can I stream Magnum v4 72B'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.