Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber

Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber are Google Gemini models for production AI agents, built for efficiency, low latency, computer use, and security workflows.

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Overview

Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber are new additions to Google’s Flash model family, introduced for developers and customers building production AI agents. The release frames them around three needs: higher token efficiency, lower latency, and more reliable performance at scale.

3.6 Flash is positioned as the main workhorse model for coding, knowledge work, multimodal tasks, and computer use. 3.5 Flash-Lite targets the fastest, lowest-cost end of the family for high-throughput workflows such as agentic search and document processing. 3.5 Flash Cyber is presented as a specialized cyber-focused model used with CodeMender for security applications.

Core capabilities

3.6 Flash for general production work

3.6 Flash is described as the workhorse model in the release, with stronger coding, knowledge work, multimodal performance, and improved computer use over 3.5 Flash.

Higher token efficiency in agent workflows

The release says 3.6 Flash uses fewer output tokens, fewer reasoning steps, and fewer tool calls than 3.5 Flash on multi-step workflows, which lowers cost and latency.

Low-latency, high-throughput Flash-Lite

3.5 Flash-Lite is positioned as the fastest 3.5-class model, with a throughput figure of 350 output tokens per second in the cited index.

Configurable thinking levels and built-in computer use

3.5 Flash-Lite includes built-in computer use support and can be tuned with minimal, low, or higher thinking levels depending on workload needs.

Specialized cyber model with CodeMender

3.5 Flash Cyber is paired with CodeMender for cybersecurity work, where careful orchestration between model and agent infrastructure is required.

Designed for agentic production tasks

The models are presented as tools for production AI agents, with emphasis on reliability, efficiency, and scale rather than single-turn chat.

Representative use cases

Knowledge work and multimodal analysis
Use 3.6 Flash when the task requires stronger coding, document understanding, or multimodal analysis, such as parsing charts, reviewing reports, or drafting work products.
Computer use automation
Use 3.6 Flash or 3.5 Flash-Lite when an agent must take actions across browser, mobile, or desktop interfaces, especially for long-horizon workflows and enterprise automation.
High-volume processing
Use 3.5 Flash-Lite for large volumes of search, classification, extraction, or document processing where latency and throughput matter more than heavyweight reasoning.
Multi-step agent workflows
Use 3.5 Flash-Lite with higher thinking levels when a smaller model still needs to coordinate subagent-style or multi-step tasks without giving up speed.
Cybersecurity remediation
Use 3.5 Flash Cyber with CodeMender for security-related work that depends on model plus agent orchestration, such as finding and fixing vulnerabilities efficiently.

Pros and Cons

Pros

3.6 Flash is said to use fewer output tokens than 3.5 Flash while improving coding, knowledge work, multimodal tasks, and computer use.
3.5 Flash-Lite is optimized for speed and throughput, with built-in computer use and configurable thinking levels for different workload shapes.
The release emphasizes lower latency and lower cost for production agent workloads, especially where scale matters.
Computer use is built into the models, reducing the need to assemble a separate computer-use model in the workflows described.
The source points to enterprise-oriented safety safeguards for 3.6 Flash, including stronger resistance to jailbreaks in CBRN and cyber-offense misuse scenarios.

Cons

The source does not provide a complete pricing or plan structure beyond launch-post token prices for two of the models.
3.5 Flash Cyber is described at a high level, but the source gives fewer standalone product details than it does for the Flash and Flash-Lite models.
Some workflow details are benchmark-based or illustrated through examples, so readers may still need the model cards and API docs for implementation specifics.

FAQ

How do the three models differ in purpose?

The source describes Gemini 3.6 Flash as a workhorse model for better coding, knowledge work, multimodal tasks, and computer use. Gemini 3.5 Flash-Lite is positioned for low-latency, high-throughput workflows such as agentic search and document processing. Gemini 3.5 Flash Cyber is presented as a specialized cyber-focused model paired with CodeMender for security work.

Is computer use built in?

Yes. The source says computer use is built into Gemini 3.6 Flash and 3.5 Flash-Lite, and it is also integrated natively into Gemini 3.5 Flash in the earlier computer-use announcement. Developers can use it for agents that act across browser, mobile, desktop, and client-side surfaces.

How can developers access these capabilities?

The source says developers and enterprises can access computer use in Gemini 3.5 Flash via the Gemini API and Gemini Enterprise Agent Platform. It also mentions computer use as a built-in client-side tool via the Gemini API and Gemini Enterprise for Gemini 3.6 Flash.

What does the source say about pricing?

No pricing page details were available in the provided evidence. The launch post itself does, however, give API pricing for 3.6 Flash at $1.50 per 1M input tokens and $7.50 per 1M output tokens, and for 3.5 Flash-Lite at $0.30 per 1M input tokens and $2.50 per 1M output tokens.

What kinds of workflows are these models aimed at?

The source highlights enterprise and production use cases, including continuous software testing, knowledge work, document parsing, chart and data analysis, report drafting, agentic search, and high-volume document processing.

Quick Facts

Category: AI model family
Brand: Gemini
Source domain: blog.google
Primary audience: Developers and enterprises building AI agents
Platforms and access: Gemini API; Gemini Enterprise Agent Platform
Launch date: Jul 21, 2026

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