Gemini 4 Argon by Google

Overview

Gemini 4 Argon is Google’s flagship Gemini 4 reasoning model from Google DeepMind. It’s natively multimodal, so one request can mix text with images, audio, video, or PDFs. It produces long text outputs for complex workflows, and it supports function calling and code execution. The unified context window reaches 1,048,576 tokens, with long decode up to 1,048,576 output tokens.

What you can build

• Whole-repo refactors, migration plans, and test generation for large codebases
• Contract review memos, clause extraction, and issue lists from PDF documents
• Financial analysis write-ups that turn spreadsheets and charts into narrative reports
• Cybersecurity triage notes and remediation plans from findings and screenshots
• Long-form research synthesis from many sources, with a structured JSON summary
• Video and audio understanding tasks like meeting notes and timeline reconstruction

Inputs

• A written prompt that states the task, constraints, and the output style you want
• An optional multi-turn conversation history when you need consistent follow-ups
• Optional system-level instructions to set role, tone, and hard rules for responses
• Optional PDF documents for reading, extraction, and question answering at scale
• Optional image files for chart reading, UI review, and document inspection
• Optional video files for scene-level questions and long-video understanding
• Optional audio clips for speech understanding and call or meeting analysis
• Optional public URLs that the model can fetch and use as additional context
• Optional grounding controls that let the model use Google Search and Google Maps data
• An optional response length cap, up to 65,536 output tokens in standard mode
• Optional user and session identifiers to keep chat history tied to a person and thread
• Optional tool results from a prior step when you’re continuing a tool-based run

Outputs

Gemini 4 Argon returns text. The text often includes structured sections, code blocks, and citations or reasoning steps when you ask for them. If you request structured outputs, it can return JSON that follows a schema you describe. When function calling is enabled, it can also return tool-call instructions that your app can execute.

Recommended settings

• Use Medium thinking for most product features that need strong answers with stable cost.
• Use High thinking for hard debugging, multi-step planning, and agent-style workflows.
• Use Low thinking for short Q&A, light editing, and classification tasks.
• Keep standard output caps for chat, and only enable long decode for report-length work.

Limitations

• The model outputs text only. It does not generate images.
• Audio generation is not supported.
• Live API mode is not supported.
• Explicit caching is not supported. Only implicit caching is available.
• Computer use is marked as Preview and may not be available in all environments.
• The 1,048,576-token window is unified across input and output, so large inputs reduce output room.
• The model can hallucinate, especially with ambiguous prompts or missing evidence.
• Its stated knowledge cutoff is March 2026, so newer events may be missing.
• Low-quality inputs can hurt results, including scanned PDFs, blurry images, noisy audio, and low-resolution video.

Safety & compliance

Gemini 4 Argon can be powerful in security and automation contexts, so apply strict access controls. Only run cybersecurity testing workflows on systems you own or have permission to test. Review outputs before taking actions that affect production systems, finances, or legal outcomes. Don’t submit sensitive personal data unless you have a clear policy and user consent.