Gemma 2 27B API: toman pricing and code
google/gemma-2-27b-it
You are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups
Gemma 2 27B 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="google/gemma-2-27b-it",
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: "google/gemma-2-27b-it",
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": "google/gemma-2-27b-it",
"messages": [{"role": "user", "content": "Classify the ticket and give it a priority from 1 to 5."}],
"response_format": {"type": "json_object"}
}'What is Gemma 2 27B good for?
Gemma 2 27B is one of Google's models. Put "google/
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 225,043 toman per 1M input tokens and 225,043 per 1M output tokens. A 1,000-word round trip on Gemma 2 27B lands near 585 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 ReMM SLERP 13B: Gemma 2 27B works out about 30% more expensive, and its context window is larger. 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 171 thousand-word requests on Gemma 2 27B, and every 1,000 toman is about 1,709 words of round trip. A job with one million input tokens and one million output tokens comes to 450,085 toman in total. Filling this model's 8,192-token window costs 1,844 toman on the input side alone, which is the real reason to keep conversation history short.
Google has 43 models in our catalogue; the cheapest is Gemma 4 26B A4B at 67 toman per thousand words and the dearest Gemini 3.1 Pro Preview Custom Tools at 6,301. Among the less common parameters it accepts presence_penalty, repetition_penalty — 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 MythoMax 13B at 8,192 tokens.
Good fits: 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).
Gemma 2 27B by Google is an open model built from the same research and technology used to create the [Gemini models](/models?q=gemini). Gemma models are well-suited for a variety of...
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 | 428 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 1,035 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 31,506 toman |
Frequently asked
- How do I call Gemma 2 27B 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-2-27b-it". Nothing else in your code changes. - What does Gemma 2 27B cost in toman?
- 225,043 toman per 1M input tokens and 225,043 toman per 1M output tokens; a 1,000-word request is around 585 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Gemma 2 27B take?
- Up to 8,192 tokens per request, roughly 6k words. A single answer can reach 2,048 tokens.
- Can I stream Gemma 2 27B'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.