Google

Gemini 4 Argon

About

Gemini 4 Argon is a closed-weight model from Google. Google's flagship reasoning model for coding, math, science and multi-step agents. Multimodal chat. It accepts text, images, documents, and video. Context window is 1M.

Context window
1M
Tokens of context on a request.
License
—
Terms for the API.

Pricing

Token rates are US dollars per 1M tokens. Image rates are per 1K images. Audio rates are per 1M audio seconds.

Rate	Price
Input / 1M	$2
Cached input / 1M	$0.10
Output / 1M	$10

Compare

Gemini 4 Argon is one of the catalog's chat models. The table is each model's published price, size, and context.

Model	What it does	Price	Parameters	Context
Gemini 4 Argon	Google's flagship reasoning model for coding, math, science and multi-step agents. Multimodal chat.	$2 / 1M input	—	1M
Qwen3.5 0.8B	Multimodal chat model with up to 64 images or one video per request.	$0.08 / 1M input	0.8B	80K
Gemini 3.5 Flash Lite	Fastest and cheapest Gemini tier. Multimodal chat; emits no reasoning tokens.	$0.30 / 1M input	—	1M
Gemini 3.7 Flash	Newest Gemini Flash tier with extended reasoning. Multimodal chat + native video.	$0.75 / 1M input	—	1M

Benchmarks

No published eval is on file for this model.

Reading	Example
Qualitative	Clear structure, grounded in the input
Quantitative	No published score on file.
Cost and performance	Lower listed rate, mid-pack latency

Methods

Methods this model serves. Payload shapes are in the docs.

Method	Returns
detection	Boxes around objects
chat	The model's reply

Estimate cost

Estimate. A page is 2,500 input tokens and 1,000 output tokens. An hour of video is the whole timeline: 258 tokens a second of picture and 32 a second of audio, plus 500 output tokens a minute.

PagesHours of video

$2,403.00

Quick start

Call this model on the OpenAI-compatible gateway. The model id is already filled in.

from openai import OpenAI

client = OpenAI(
    base_url="https://gateway.vlm.run/v1/openai",
    api_key="<VLMRUN_API_KEY>",
)

response = client.chat.completions.create(
    model="google/gemini-4-argon",
    messages=[{"role": "user", "content": "What is in this image?"}],
)

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