Gemma 4 31B IT

Google · Text · Context 262.1K · max output 131.1K tokens

gemma-4-31b-it Try

Prompt caching
12% below list price

Pricing

Per 1 million tokens
	MarkasAI	Vendor list price
Input	Rp 2.181	Rp 2.505
Output	Rp 6.226	Rp 7.156
Cache	Rp 2.181
Cache write	Rp 2.181

MarkasAI sale prices in Rupiah, read from the same catalog as GET /v1/models. Prices can change and apply to later requests.

Specifications

Model ID: gemma-4-31b-it
Provider: Google
Type: Text
Endpoint: POST /v1/chat/completions
Context window: 262,144 tokens
Output limit per request: 131,072 tokens

About this model

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...

Quoted from the provider's public catalog, in English.

Call example

curl https://api.markas.ai.id/v1/chat/completions \
  -H "Authorization: Bearer $MARKASAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemma-4-31b-it",
    "messages": [{"role": "user", "content": "What is an API?"}],
    "max_tokens": 300
  }'

import os
from openai import OpenAI

client = OpenAI(base_url="https://api.markas.ai.id/v1", api_key=os.environ["MARKASAI_API_KEY"])

resp = client.chat.completions.create(
    model="gemma-4-31b-it",
    messages=[{"role": "user", "content": "What is an API?"}],
    max_tokens=300,
)
print(resp.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://api.markas.ai.id/v1", apiKey: process.env.MARKASAI_API_KEY });

const resp = await client.chat.completions.create({
  model: "gemma-4-31b-it",
  messages: [{ role: "user", content: "What is an API?" }],
  max_tokens: 300,
});
console.log(resp.choices[0].message.content);

Keep your API key in the MARKASAI_API_KEY environment variable. Read the endpoint docs

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