Gemini 4 Argon

4 Argon · Family: Gemini

Google's frontier multimodal model (Gemini 4 family) for reasoning, coding, agentic and scientific work; 1M token limit, available via the Gemini API.

✓ Active✓ Public accessLLMMultimodalReasoning modelTool-using model📁 Gemini

Context window

tokens

Release date

13 October 2026

🏢GoogleProducer 🔬Google DeepMindResearch lab

Access:APIHostedDeployment:☁ Cloud

Overview

Gemini 4 Argon is a frontier AI model developed by Google (Google DeepMind), introduced on 13 October 2026 as the first model of the Gemini 4 family. It combines advanced reasoning, deep visual perception, expanded domain knowledge and agentic capabilities, aimed at complex, multi-step tasks across coding, finance, legal, cybersecurity and science.

The model provides a unified 1M input-output token limit via the Gemini API. It is available globally for developers and enterprises through the API (AI Studio, Android Studio), in Google Antigravity, and in the Gemini app and AI Mode in Search for Google AI Ultra and Pro subscribers. Model weights are not publicly available (closed model).

In robotics, Argon scores 87% on the Embodied Reasoning (ERQA) benchmark and supports a "Video-to-Sim-to-Real" pipeline — in an internal test it generated control code from a raw video that was transferred onto Apptronik's Apollo humanoid. It also ranks #1 on the Vals Index, which measures model performance across finance, coding, legal and tax work.

Classification

LLMMultimodalReasoning modelTool-using model

Family: Gemini

Access & deployment

APIHosted

Cloud

Weights: Closed

Key parameters

📏 Context: 1M

✓ Tools

📥 Input: text, image, documents, video

Robotics

Spatial reasoningEmbodied task planning3D scene reconstructionSpatial prediction

Technical specification

Context window

tokens

Features:✓ Tool use

Modalities

⬇ Input

textimagedocumentsvideo

⬆ Output

textcode

Capabilities and applications

Native model capabilities

Reasoning

The model's ability to reason logically and solve complex problems.

Category: reasoning

Advanced reasoning

The ability to perform multi-step, structured reasoning: analysing problems, planning steps, and drawing conclusions from hypotheses. Reasoning-first models (e.g. GPT-5.1 Thinking) dedicate a portion of inference to chains of thought before responding.

Category: reasoning

Multi-step reasoning

Carrying out multi-step chains of reasoning across long, complex tasks.

Category: reasoning

Coding

Generating, analysing and modifying code in many programming languages. Covers writing functions, debugging, refactoring, code review, and creating tests. Measured by benchmarks such as HumanEval and SWE-bench.

Category: coding

Agentic coding

Multi-hour, multi-step programming tasks performed autonomously by the model: cloning a repository, running tests, iterating on fixes, integrating with CLI tools. Characteristic of Codex variants (GPT-5.1-Codex-Mini, Codex-Max).

Category: coding

Agentic capability

The model's ability to autonomously plan and execute multi-step tasks by sequentially using tools, maintaining context, and adapting to intermediate results.

Category: planning

Long context

Support for large context windows — tens to hundreds of thousands (or millions) of input tokens. Enables analysis of entire codebases, long documents, and many parallel conversations without losing earlier information. GPT-5.1 supports 400,000 tokens.

Category: language

Multimodal understanding

Category: multimodal

Image understanding

Analysing and interpreting the content of images.

Category: vision

Cybersecurity

The model ability to perform computer-security tasks: vulnerability analysis, proof-of-concept exploit generation, patching, and cybersecurity question answering.

Category: other

Vulnerability detection

The ability to identify and analyze security vulnerabilities in source code and software.

Category: coding

Tool use

The model's ability to call external functions, APIs and tools during a conversation: calculator, search engine, code editor, database. The model decides when and how to use a tool and interprets its result.

Category: planning

Mathematical reasoning

The model's ability to solve mathematical tasks requiring multi-step reasoning — equations, proofs, combinatorics, geometry, calculus and competition-level problems.

Category: reasoning

Financial modeling

Building financial models: DCF, company valuations, scenario analysis, budget forecasts, P&L sheets, cap table models. Requires precise numerical reasoning and knowledge of accounting conventions.

Category: reasoning

3D reconstruction

Recovering 3D scene geometry (point clouds, meshes, any-view geometry) from images.

Category: vision

Multi-step project execution

The ability to autonomously drive multi-hour, multi-step projects: decomposing the task, planning the sequence of actions, iteratively delivering results, and adjusting based on feedback. Key for enterprise and knowledge-work agents.

Category: planning

Robotics

Spatial reasoningEmbodied task planning3D scene reconstructionSpatial prediction