Gemma 4 31B (free) API: toman pricing and code
google/gemma-4-31b-it:free
Free · shared capacityvisiontoolsreasoningjsonYou are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups
Gemma 4 31B (free) 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:free",
"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:free",
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:free",
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 (free) good for?
Gemma 4 31B (free) comes from Google; in uttapen you reach it with the model id "google/gemma-4-31b-it:free". 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 32,768 tokens. Using it costs nothing. In exchange the capacity is shared, so during busy hours a request can come back empty-handed.
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 0 toman per 1M input tokens and 0 per 1M output tokens. A 1,000-word round trip on Gemma 4 31B (free) lands near 0 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 Dots3-Note Preview (free), 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.
This id gets confused with "google/gemma-4-31b-it", because the underlying model is the same. The difference is the suffix: "free" against the standard variant. Maximum answer length differs as well: 32,768 against 16,384 tokens. On parameters the other takes frequency_penalty, logit_bias, logprobs, min_p.
Google has 45 models in our catalogue; the cheapest is Gemma 3 4B at 57 toman per thousand words and the dearest Google Gemini Pro Latest at 5,280. It does not support structured_outputs, 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 Trinity Large Thinking at 262,144 tokens.
Good fits: proving an idea works before you spend anything, 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.
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.
| Job | Tokens | Cost |
|---|---|---|
| One chat turn with a medium history | 1,500 in + 400 out | 0 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 0 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 0 toman |
Other variants of this model
Same core model, different execution terms and different price. This page is the free variant.
| Variant | Model id | Output / 1M | Context |
|---|---|---|---|
| standard | google/gemma-4-31b-it | 98,643 | 262,144 |
| batch (cheaper, slower) | google/gemma-4-31b-it:batch | 281,421 | 262,144 |
Put the variant's id verbatim in the model field; nothing else in your code changes.
Frequently asked
- How do I call Gemma 4 31B (free) 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:free". Nothing else in your code changes.
- What does Gemma 4 31B (free) cost in toman?
- 0 toman per 1M input tokens and 0 toman per 1M output tokens; a 1,000-word request is around 0 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Gemma 4 31B (free) take?
- Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 32,768 tokens.
- Does Gemma 4 31B (free) 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.
- What are the limits on a free model?
- Free models have a daily per-user cap and their capacity is shared with everyone else. When the shared pool is exhausted the request is refused with a clear error and your wallet is untouched.