Gemini 4 Carbon

gemini-4-carbon

Gemini 4 Carbon is Google's frontier multimodal reasoning model, designed for advanced coding, agentic workflows, and complex multi-step problem solving. It supports text, image, audio, video, and file inputs, enabling integrated reasoning and analysis across diverse modalities. Optimized for long-horizon execution and demanding professional workloads, Gemini 4 Carbon supports up to 1M output tokens through continuation and configurable reasoning effort levels (low, medium, high) to balance reasoning depth, latency, and cost. It is well suited for autonomous agents, software engineering, multimodal analysis, and large-scale workflow automation.

Context: 1M tokens
Endpoint

Pricing

Input$2.00 / 1M

Output$10.00 / 1M

Cache Write (5m)$2.00 / 1M

Cache Write (1h)$2.00 / 1M

Cache Read$2.00 / 1M

Web Search$0 / 1M

Quick Start

Select an endpoint and copy a working example for this model.

Endpoint

python

from openai import OpenAI client = OpenAI(    api_key="YOUR_API_KEY",    base_url="https://api.apertis.ai/v1") response = client.chat.completions.create(    model="gemini-4-carbon",    messages=[        {"role": "user", "content": "Hello!"}    ],    max_tokens=1024,    temperature=0.7) print(response.choices[0].message.content) # Optional: Enable context compression to reduce token usage# response = client.chat.completions.create(#     model="gemini-4-carbon",#     messages=[{"role": "user", "content": "Hello!"}],#     extra_body={"compression": {"enabled": True, "model": "gpt-4.1-mini"}}# )

Supported Parameters

API docs

Common7 params

modelmessagesmax_tokenstemperaturetop_pstreamtools

Extended4 params

reasoning_effortstream_optionsthinkingextra_body

Cursor IDE Model IDs

Use these namespaced identifiers in Cursor IDE to avoid conflicts with built-in models.

gemini-4-carbon

Questions about Gemini 4 Carbon

How much does Gemini 4 Carbon cost on Apertis?

Gemini 4 Carbon costs $2.00 per 1M input tokens and $10.00 per 1M output tokens on Apertis.

What is Gemini 4 Carbon's context window?

Gemini 4 Carbon has a context window of 1M tokens.

Which company develops Gemini 4 Carbon?

Gemini 4 Carbon is developed by Google.

Which model ID do I use to call Gemini 4 Carbon?

Use the model ID gemini-4-carbon to call Gemini 4 Carbon.

Compare with Other Models

See how this model compares to others from the same provider.

Gemini 4 Hydrogen

Gemini 4 Hydrogen is Google's high-efficiency multimodal model in the Gemini 4 family, optimized for high-throughput agentic workflows, coding, and multi-model routing. It delivers strong performance at lower cost and latency than Gemini 4 Carbon, supporting text, image, audio, video, and file inputs for versatile multimodal applications. Designed for scalable production workloads and long-horizon execution, Gemini 4 Hydrogen supports up to 1M output tokens through continuation and configurable reasoning effort levels (low, medium, high, and xhigh) to balance reasoning depth, speed, and cost. It is well suited for coding assistants, autonomous agents, complex workflow automation, and large-scale AI systems.

Context: 1M
Input: $1.50/M
Output: $7.50/M

Gemini Nano Banana 2.1 (Google AI Studio)

Gemini Nano Banana 2.1 served from Google AI Studio. Billed per image at US$0.0336 (up to 1K).

Context: N/A
Input: — (Not priced per input token)
Output: — (Not priced per output token)

Gemini 4 Argon

Gemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across long, multi-step tasks and to turn long documents mixing text, tables, charts and images into a single grounded output. It leads the Vals Index across finance, legal, coding and tax work, and supports long decode of up to 1M output tokens in a single response.

Context: 1M
Input: $2.00/M
Output: $10.00/M

Gemini 3.1 Flash Lite Image (Google Vertex AI)

Gemini 3.1 Flash Lite Image served from Google Vertex AI. Billed per image at US$0.0336. Call it through /v1/chat/completions with stream set to false; the image is returned in the message content as a base64 data URI. Maximum output resolution is 1K. A request for a larger size returns a 1K image with the same aspect ratio at the same price. When a size is reduced, the response carries an X-Image-Resolution-Capped header with the requested and delivered size.

Context: N/A
Input: — (Not priced per input token)
Output: — (Not priced per output token)