What is Gemini (Google)?
Gemini is Google’s model family for multimodal applications. This entry specifies Gemini 4 Argon: it combines text, image, audio, video and PDF understanding with configurable thinking and tool use, making it suitable for workflows that need more than text-only retrieval.
Key features
- Text, image, audio, video and PDF input
- Unified input/output context budget
- Configurable thinking levels
- Search and Maps grounding
- Code execution and file search
- Optional long-decode output
Best for
- Mixed-media document analysis
- Video and audio understanding
- Software engineering agents
- Math and science assistance
Who it suits
- Multimodal app developers
- Teams analysing recorded media
- Developers building research agents
Who may not need it
- Projects requiring free inference
- Real-time voice applications
Free plan
Gemini 4 Argon is not available on the free tier. Checked Oct 26, 2026
Integrations and compatibility
- Google Search grounding
- Google Maps grounding
- Code execution
- File search
Where it fits in a workflow
Use Argon to combine media understanding with grounded research or code execution. Reserve enough of its unified context for the requested output, especially when enabling long decode.
Specifications
| Model family | Gemini |
|---|---|
| Version these details describe | Gemini 4 Argon (gemini-4-argon) |
| Developer | |
| Modalities | text, image, audio, video, PDF |
| Context window | 1,048,576 tokens shared by input and output |
| Output limit | 65,536 default synchronous; up to 1,048,576 with long decode |
| Access | Gemini API, Google AI Studio |
Specifications apply to the version named above only and were checked on Oct 26, 2026. Official model documentation.
Sources
Strengths
- Handles several media types in one model
- Built-in grounding and execution tools
- Long-decode option for large outputs
Watch-outs
- No free tier for this model
- No native audio or image generation
- Explicit context caching and Live API are unsupported
- Input and output share the context budget
Alternatives
Related coverage
Introduction to Large Language and Multimodal Models
Learn how language and multimodal models process information, what tokens and context mean, and why plausible answers still need checking.
GuideHow to Choose Between LLM and Multimodal Models
Choose a model by testing the real task, input requirements, response time, and full operating cost—not a leaderboard alone.
GuideLLM and Multimodal Model Implementation Checklist
Use this launch checklist to move an AI prototype into a monitored service with defined tests, data controls, and fallback behavior.
Listings are editorial and independent. Details that change often are marked “to be verified” until an editor checks them against the official source.