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Gemma 4 31B API: toman pricing and code

google/gemma-4-31b-it

Image inputTool callingReasoningJSON output
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Input · per 1M tokens
69,244 toman
Output · per 1M tokens
138,488 toman
One 1,000-word request ≈
270 toman

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

Gemma 4 31B example: reading an image into JSON

The example is picked from this model's own capabilities. Drop in your key and it runs as is.

# tip: a data URI works too — base64 the file and prefix it with data:image/jpeg;base64,
curl https://api.uttapen.ir/v1/chat/completions \
  -H "Authorization: Bearer sk-up-…" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "google/gemma-4-31b-it",
    "messages": [{
      "role": "user",
      "content": [
        {"type": "text", "text": "Return only the invoice number and the total, as JSON."},
        {"type": "image_url", "image_url": {"url": "https://example.com/factor.jpg"}}
      ]
    }]
  }'
import base64, json
from openai import OpenAI

client = OpenAI(base_url="https://api.uttapen.ir/v1", api_key="sk-up-…")

img = base64.b64encode(open("factor.jpg", "rb").read()).decode()

resp = client.chat.completions.create(
    model="google/gemma-4-31b-it",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "Return the invoice number, the date and the total as JSON."},
            {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{img}"}},
        ],
    }],
    response_format={"type": "json_object"},
)
print(json.loads(resp.choices[0].message.content))
import OpenAI from "openai";
import { readFileSync } from "node:fs";

const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });
const img = readFileSync("factor.jpg").toString("base64");

const resp = await client.chat.completions.create({
  model: "google/gemma-4-31b-it",
  messages: [{
    role: "user",
    content: [
      { type: "text", text: "Return the invoice number, the date and the total as JSON." },
      { type: "image_url", image_url: { url: `data:image/jpeg;base64,${img}` } },
    ],
  }],
  response_format: { type: "json_object" },
});
console.log(JSON.parse(resp.choices[0].message.content));

What is Gemma 4 31B good for?

Gemma 4 31B comes from Google; in uttapen you reach it with the model id "google/gemma-4-31b-it". It accepts up to 262,144 tokens of input per call, so a long document or several source files fit in a single request. A single response can run to 16,384 tokens. On price it sits in the "very cheap" band — cheaper than 316 and dearer than 74 of the other paid models in the catalogue.

What it can do beyond plain text: it reads images directly, which makes it a real option for invoices, forms and screenshots; 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 69,244 toman per 1M input tokens and 138,488 per 1M output tokens. A 1,000-word round trip on Gemma 4 31B lands near 270 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 Seed-2.0-Mini: Gemma 4 31B works out about 20% more expensive, and its context window is the same size. 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 370 thousand-word requests on Gemma 4 31B, and every 1,000 toman is about 3,704 words of round trip. A job with one million input tokens and one million output tokens comes to 207,732 toman in total. Filling this model's 262,144-token window costs 18,152 toman on the input side alone, which is the real reason to keep conversation history short.

This id gets confused with "google/gemma-4-31b-it:free", because the underlying model is the same. The difference is the suffix: no suffix (the standard variant) against "free". Maximum answer length differs as well: 16,384 against 32,768 tokens. On parameters, this one takes frequency_penalty, logit_bias, logprobs, max_completion_tokens.

Google has 43 models in our catalogue; the cheapest is Gemma 3 4B at 68 toman per thousand words and the dearest Gemini 3.1 Pro Preview Custom Tools at 6,301. Among the less common parameters it accepts logit_bias, logprobs, max_completion_tokens, min_p, presence_penalty, repetition_penalty — all through the standard request body. By context size the nearest option from another provider is Aion 3.5 at 262,144 tokens.

Good fits: high-volume work such as classification, tagging and bulk summarising, logic puzzles and code review, pulling text and fields out of images, agents that reach out to APIs and databases, extracting data against a fixed schema, analysing a long document or codebase in one request.

Provider's own description

Gemma 4 31B Instruct is Google DeepMind's 30.7B dense multimodal model supporting text and image input with text output. Features a 256K token context window, configurable thinking/reasoning mode, native function...

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 out159 toman
Summarising a ten-page document4,000 in + 600 out360 toman
Classifying a thousand short rows120,000 in + 20,000 out11,079 toman

Other variants of this model

Same core model, different execution terms and different price. This page is the standard variant.

VariantModel idOutput / 1MContext
freegoogle/gemma-4-31b-it:free0262,144

Put the variant's id verbatim in the model field; nothing else in your code changes.

Price history

DateInput (toman/1M)Output (toman/1M)
26 Oct 202669,244138,488
26 Oct 202634,622117,715
15 Oct 202669,244138,488
7 Sep 202631,160117,715

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

Frequently asked

How do I call Gemma 4 31B 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 "google/gemma-4-31b-it". Nothing else in your code changes.
What does Gemma 4 31B cost in toman?
69,244 toman per 1M input tokens and 138,488 toman per 1M output tokens; a 1,000-word request is around 270 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Gemma 4 31B take?
Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 16,384 tokens.
Does Gemma 4 31B support streaming and tool calling?
Streaming (stream=true) works on every model here. This one supports tool calling in the standard OpenAI shape. Image input is accepted through image_url, as a data URI or a public URL. Structured output through response_format works too.