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Granite 4.2 8B API: toman pricing and code

ibm-granite/granite-4.2-8b

Tool callingReasoningJSON output
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Input · per 1M tokens
20,773 toman
Output · per 1M tokens
86,555 toman
One 1,000-word request ≈
140 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 5,193 toman per 1M tokens.Pricing and top-ups

Granite 4.2 8B 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="ibm-granite/granite-4.2-8b",
    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: "ibm-granite/granite-4.2-8b",
  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": "ibm-granite/granite-4.2-8b",
    "messages": [{"role": "user", "content": "Classify the ticket and give it a priority from 1 to 5."}],
    "response_format": {"type": "json_object"}
  }'

What is Granite 4.2 8B good for?

IBM Granite publishes this model; we expose it under the id "ibm-granite/granite-4.2-8b". Its context window is 131,072 tokens, roughly 98k English words in one request. A single response can run to 117,964 tokens. On price it sits in the "very cheap" band — cheaper than 343 and dearer than 50 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; it has a reasoning mode that pays off on multi-step problems, maths and debugging. All of it works through the standard parameters of the official OpenAI SDK — no custom client, no wrapper. Keep in mind that reasoning tokens are output tokens and do appear on the bill.

Pricing is 20,773 toman per 1M input tokens and 86,555 per 1M output tokens. A 1,000-word round trip on Granite 4.2 8B lands near 140 toman. It supports cached input: repeated context is billed at 5,193 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 Nemotron 3 Nano 30B A3B, and Granite 4.2 8B costs about the same, 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 714 thousand-word requests on Granite 4.2 8B, and every 1,000 toman is about 7,143 words of round trip. A job with one million input tokens and one million output tokens comes to 107,328 toman in total. Filling this model's 131,072-token window costs 2,723 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, reasoning_effort, repetition_penalty — all through the standard request body. 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, logic puzzles and code review, agents that reach out to APIs and databases, extracting data against a fixed schema.

Provider's own description

Granite 4.2 8B is a dense reasoning model from IBM. It is suited for mathematics, code generation, multilingual dialogue, and agentic workflows that need multi-step reasoning. It supports full, low-effort,...

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 out66 toman
Summarising a ten-page document4,000 in + 600 out135 toman
Classifying a thousand short rows120,000 in + 20,000 out4,224 toman

Price history

DateInput (toman/1M)Output (toman/1M)
4 Nov 202620,77386,555
29 Oct 202620,77351,933
21 Sep 202620,77386,555
21 Sep 202634,62251,933
9 Sep 202620,77386,555
7 Sep 202634,62251,933

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

Frequently asked

How do I call Granite 4.2 8B 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 "ibm-granite/granite-4.2-8b". Nothing else in your code changes.
What does Granite 4.2 8B cost in toman?
20,773 toman per 1M input tokens and 86,555 toman per 1M output tokens; a 1,000-word request is around 140 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Granite 4.2 8B take?
Up to 131,072 tokens per request, roughly 98k words. A single answer can reach 117,964 tokens.
Does Granite 4.2 8B 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.