Gemini 4 Argon, the Google DeepMind model in numbers

What you need to know

Gemini 4 Argon is a language model from Google DeepMind, released on October 13, 2026. It is a very recent release. Its weights are not published, so it only runs remotely, through the Google DeepMind API or partner hosts (25 listed on models.dev). It has a reasoning mode: it can "think" before answering, which helps with multi-step problems but uses more tokens, and therefore more money.

We file it under frontier models (24 models in this category on the site).

What do independent benchmarks say?

According to results published by Epoch AI (checked on October 22, 2026), Gemini 4 Argon has a capability index of 95.3 out of 100, computed on 5 benchmarks from our basket.

That puts it at #3 in our weekly model ranking.

• GPQA Diamond (PhD-level science questions): 96.4% — best score among tracked models
• OTIS Mock AIME (competition mathematics problems): 100% — the best tracked model, GPT-5.5 Pro, reaches 100%
• FrontierMath (very hard research-level mathematics): 86% — the best tracked model, GPT-6 Astra, reaches 93.7%
• FrontierCode (advanced programming tasks): 47.6% — the best tracked model, Claude Opus 5.5, reaches 54.6%

What it costs

Gemini 4 Argon costs $2 per million input tokens and $10 per million output tokens (developer's price, checked on October 22, 2026).

In practice, an agent task that reads 20,000 tokens and writes 2,000 costs about $0.06, or $60 for 1,000 such tasks.

That is 1.5 times cheaper than the median output price of frontier models ($14.5).

Tokens read back from cache cost $0.10 per million, which helps an agent that keeps resending the same context.

To estimate your own budget, use the agent cost calculator.

Context and capabilities

Its context window reaches 1,048,576 tokens, roughly 1,573 pages of text at 500 words per page: enough to load a whole book or a mid-sized codebase. A single answer can run to 32,768 tokens.

It accepts as input: text, image, video, audio and PDF. Its knowledge stops at March 2026: beyond that, it needs web search or your documents.

It can call tools (functions, APIs) and return structured JSON output, which makes it a possible foundation for an AI agent.

When to choose it

Given its category, Gemini 4 Argon is mainly aimed at hard tasks where quality matters more than cost: deep reasoning, complex code, long analyses.

Its FrontierCode result (47.6%, above the median of tracked models) makes it a serious candidate for coding agents.

Its very large context window makes it worth considering for analysing large sets of documents in a single request.

Before adopting it, test it on your own examples: benchmarks measure standard tasks, not your job.

Comparable models

In the same category, the models closest to Gemini 4 Argon are:

• GPT-6 Astra (OpenAI, index 94.7) — see the comparison
• Claude Sonnet 5.5 (Anthropic, index 95.9) — see the comparison
• Claude Opus 5.5 (Anthropic, index 95.9) — see the comparison
• GPT-5.5 Pro (OpenAI, index 96.9) — see the comparison