Gemini 4

Positioning Overview

This is a flagship reasoning model that outputs text, aimed at the most complex multi-step, long-horizon tasks, and sits at a higher tier within its series. It accepts text and image inputs and uniformly produces text, supports a context window of up to 1,048,576 tokens, with a maximum output of 65,536 tokens per request, and allows reasoning intensity to be adjusted via the three reasoning_effort levels: low, medium, and high. Its target users are developers and research teams who need to process large volumes of material and produce written results, making it suitable for building analysis assistants, document processing tools, and complex workflows.

Core Highlights

• Flagship-level reasoning for complex tasks — Occupies the strongest position in the Gemini 4 series, aimed at complex tasks requiring multi-step decomposition and long-running progression.
• Million-level context window — The context window reaches up to 1,048,576 tokens, accommodating large batches of text and image material processed together.
• Three adjustable reasoning intensity levels — Via the low, medium, and high levels of reasoning_effort, trade off between reasoning depth and response pacing.
• Unified text and image input — Text and images go through the same input channel, with output uniformly as text, reducing the cost of integrating different materials separately.

Capabilities

Long-context text processing: Input text can fill the context window of up to 1,048,576 tokens, suitable for understanding, summarizing, and question answering over entire long documents, with analysis results output as text.

Image content understanding: Beyond text instructions, image inputs can be attached for image-based question answering, information extraction from images, and cross-referencing analysis between text and images, with output uniformly as text descriptions or conclusions.

Long-form text generation: A maximum output of 65,536 tokens per request can be used to write long reports, long documents, and longer code, meeting written deliverables that require full-length output.

Reasoning intensity adjustment: Set the low, medium, and high levels via reasoning_effort to choose reasoning depth according to task complexity, adapting to requests ranging from quick responses to multi-step reasoning.

Multi-step complex task processing: Aimed at long-horizon tasks requiring continuous analysis and comprehensive judgment, it can combine long text and image material within a single request to deliver complete textual conclusions.

Use Cases

• Long document analysis and Q&A — Research and operations staff submit long reports or papers to obtain summaries, Q&A results, and extracted key points, used for internal knowledge bases and review processes.
• Integrated interpretation of text and image material — Analysts provide both textual explanations and image material to produce cross-referenced interpretation text, used for intelligence organization and content verification.
• Long report and long document writing — Consulting and content teams provide background material, and the model generates long analysis reports or explanatory documents, used as delivery drafts and internal review materials.
• Complex multi-step reasoning assistant — Developers integrate the model into Q&A or analysis products to handle knowledge Q&A requests requiring multi-step decomposition, outputting natural-language responses for end users to read.
• Long code and technical documentation generation — Engineering teams provide requirement descriptions and related materials to generate longer code explanations or technical documentation, used for project documentation libraries and handover materials.

Limitations and Selection Advice

The output modality is text only and cannot generate images, audio, or video. The context window is shared by input and output, so long inputs consume the token budget available for generating output, requiring a trade-off between material length and output length. The maximum output per request is 65,536 tokens, and anything beyond must be processed in batches. Reasoning intensity is set via the low, medium, and high levels of reasoning_effort, and the choice of level affects the reasoning depth for complex tasks.