Gemini 4 Carbon | Google LLM API Pricing & Performance

Name: Gemini 4 Carbon API
Brand: google
Price: 4 USD
Availability: InStock

google/gemini-4-carbon

Tanggal rilis: 2026-11-11

1,048,576 context · $4.00/M input tokens · $20.00/M output tokens

Gemini 4 Carbon is a frontier Gemini 4 model from Google for coding, agentic and complex reasoning work. It takes text, image, audio, video and file input, supports long decode...

Harga

Bayar sesuai pemakaian

Tanpa biaya di muka, bayar hanya sesuai penggunaan

Input$4.00 / M Tokens

Output$20.00 / M Tokens

Cache Read$0.20 / M Tokens

Cache Write$0.38 / M Tokens

Coba model

google/gemini-4-carbon

Online

Hai! Saya asisten AI yang siap membantu. Ada yang bisa saya bantu?

Siap memakai model ini di coding agent lokal?Setup agent

Penggunaan API

Gunakan contoh kode berikut untuk integrasi dengan API kami:

import OpenAI from 'openai';

if (!process.env.WAVESPEED_API_KEY) throw new Error('Set WAVESPEED_API_KEY');
const client = new OpenAI({
  apiKey: process.env.WAVESPEED_API_KEY,
  baseURL: 'https://llm.wavespeed.ai/v1',
  timeout: 120_000,
  maxRetries: 0,
});

try {
  const response = await client.chat.completions.create({
    model: 'google/gemini-4-carbon',
    messages: [{ role: 'user', content: 'Hello!' }],
  });
  const body = response as typeof response & { error?: { message?: string } };
  if (body.error) throw new Error(body.error.message || 'LLM generation failed');
  console.log(response.choices[0]?.message?.content ?? '');
} catch (error) {
  console.error('LLM request failed:', error);
  process.exitCode = 1;
}

import OpenAI from 'openai';

if (!process.env.WAVESPEED_API_KEY) throw new Error('Set WAVESPEED_API_KEY');
const client = new OpenAI({
  apiKey: process.env.WAVESPEED_API_KEY,
  baseURL: 'https://llm.wavespeed.ai/v1',
  timeout: 120_000,
  maxRetries: 0,
});

try {
  const response = await client.chat.completions.create({
    model: 'google/gemini-4-carbon',
    messages: [{ role: 'user', content: 'Hello!' }],
  });
  const body = response as typeof response & { error?: { message?: string } };
  if (body.error) throw new Error(body.error.message || 'LLM generation failed');
  console.log(response.choices[0]?.message?.content ?? '');
} catch (error) {
  console.error('LLM request failed:', error);
  process.exitCode = 1;
}

Pengenalan Model

Google: Gemini 4 Carbon

Gemini 4 Carbon is a frontier Gemini 4 model from Google for coding, agentic and complex reasoning work. It takes text, image, audio, video and file input, supports long decode...

This model is available through the WaveSpeed AI OpenAI-compatible API for chat completions and compatible application workflows.

Why It Looks Great

text+image+file+audio+video->text architecture for Audio, file, Image, Text, Video to Text workloads
1048576 context window for long prompts, document analysis, and multi-turn workflows
Vision input support for image understanding and multimodal tasks
Function calling and tool-use support for agentic application workflows
Structured output support for JSON responses and schema-constrained generation
Reasoning controls available through supported parameters

Key Features

Context Window: 1048576 tokens
Max Input: 1048576 tokens
Max Output: 943718 tokens
Vision: Supported
Function Calling: Supported
Structured Outputs: Supported
Image Generation: Not listed
Audio Input: Supported
Supported Parameters: include_reasoning, max_tokens, reasoning, reasoning_effort, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_p

Specifications

Specification	Value
Provider	google
Model Type	Chat Completions model
Architecture	text+image+file+audio+video->text
Context Window	1048576 tokens
Max Input	1048576 tokens
Max Output	943718 tokens
Input	Audio, file, Image, Text, Video
Output	Text
Vision	Supported
Function Calling	Supported
Structured Outputs	Supported
Release Date	November 11, 2026

How to Use

Write your prompt - describe the task, provide context, and specify the desired output format.
Submit - the model processes your request and returns the response.

API Integration

Base URL: https://llm.wavespeed.ai/v1 API Endpoint: chat/completions Model ID: google/gemini-4-carbon

API Usage

Python SDK

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://llm.wavespeed.ai/v1"
)

response = client.chat.completions.create(
    model="google/gemini-4-carbon",
    messages=[{"role": "user", "content": "Hello!"}]
)

print(response.choices[0].message.content)

cURL

curl https://llm.wavespeed.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "google/gemini-4-carbon",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Notes

Model: google/gemini-4-carbon
Provider: google

Info

Penyediagoogle

Tipellm

Fitur yang Didukung

Input

TeksGambarAudio

Output

Teks

Konteks1,048,576

Output Maks943,718

Vision✓ Didukung

Function Calling✓ Didukung

Panduan Akses API

Base URLhttps://llm.wavespeed.ai/v1

API Endpointchat/completions

Model IDgoogle/gemini-4-carbon

Pertanyaan Umum tentang Gemini 4 Carbon

Berapa biaya Gemini 4 Carbon melalui API?+

Harga di WaveSpeedAI: $4.00 per juta token input dan $20.00 per juta token output. Prompt caching dan batch processing ditagih terpisah dan mengurangi biaya efektif pada beban kerja yang panjang dan berulang.

Berapa context window Gemini 4 Carbon?+

Gemini 4 Carbon mendukung hingga 1049K token konteks dengan hingga 944K token output per permintaan.

Apakah Gemini 4 Carbon kompatibel dengan OpenAI?+

WaveSpeedAI menyediakan Gemini 4 Carbon di https://llm.wavespeed.ai/v1 melalui antarmuka Chat Completions yang kompatibel dengan OpenAI. Sebagian besar klien OpenAI SDK cukup mengganti base URL dan API key; field opsional bergantung pada model.

Bagaimana memulai dengan Gemini 4 Carbon?+

Masuk ke WaveSpeedAI, buat API key di Access Keys, lalu kirim permintaan ke https://llm.wavespeed.ai/v1/chat/completions dengan model id yang ditampilkan di atas. Lihat katalog model terbaru untuk ketersediaan, kemampuan, dan harga.