Gemini for Developers

I have been using AI APIs in development for a long time, and honestly, Google was not my first call for most of that time. OpenAI had the developer momentum. Anthropic had Claude. Google had the best models on paper, but the developer experience felt like an afterthought. The APIs were good. The tooling around them was not.

Google I/O 2026 changed that in a way I did not fully expect.

It was packed with developer-focused announcements, including Gemini 3.5 Flash, Jules, Antigravity 2.0, Managed Agents in the Gemini API, and native Android vibe coding in AI Studio. Together, these updates showed that Google is no longer focused only on building powerful AI models. It is also investing heavily in the tools and platforms developers need to build with them.

In this article, I am going to walk through every major developer tool Google announced or updated in 2026, how they work, what they cost, and how they fit together. This will help you decide which ones belong in your workflow.

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What is the Gemini API?

The Gemini API is Google's developer-facing interface for accessing Gemini models programmatically. It gives developers the ability to build AI-powered applications, automate workflows, process multimodal inputs, and run agentic tasks.

It is accessed through Google AI Studio for prototyping and direct API calls, or Vertex AI for production-scale enterprise deployment. Both surfaces use the same underlying models. The difference is in scale, SLA guarantees, and compliance features.

As of mid-2026, the Gemini API has been significantly expanded beyond a simple chat completion endpoint. It now supports Managed Agents, context caching, grounding with Google Search and Google Maps, function calling, code execution, URL context, and multimodal inputs covering text, image, audio, video, and PDF.

The developer story in 2026 is not just "call the API and get a response." It is "call the API and get an agent that reasons, executes code, browses the web, and returns a result."

Different Models of Gemini for Developers2

Google has different models to offer, each with different features and for different use cases. Before getting into the tooling, you need to know which model to use for what. If you pick the wrong one, it will either burn your budget or underperform on benchmarks. Here is the current Gemini model lineup:

The latest and most advanced version is Gemini 3.5 Flash.

It launched on May 19, 2026, and it outperforms Gemini 3.1 Pro on coding and agentic benchmarks while running four times faster and costing 25% less. On Terminal-bench 2.1 for agentic coding, it scores 76.2%. On SWE-Bench Pro, it scores 55.1%. These are the numbers that matter if you are building anything with autonomous code execution.

One important warning: if you are still running Gemini 2.0 Flash in production, you need to migrate right now. It was shut down on June 1, 2026. Calls to that model are no longer being served.

Context caching is also worth calling out. For pipelines where the same large context gets reused across multiple requests, caching can reduce costs by up to 90%. Gemini 3.5 Flash cached input is priced at $0.15 per million tokens versus $1.50 for standard input.

Also Read: A Brief History Of Generative AI

What is Jules? Google's Async Coding Agent

Jules is an asynchronous coding agent by Google. One interesting part about it is that it is not just another coding agent. The main difference is that it does not work alongside you. It works instead of you on a well-defined task.

Every other major coding assistant, like GitHub Copilot, Cursor's agent mode, and Claude Code, operates synchronously. You give it a task, it runs, you watch, you intervene, you approve each step. You are still the bottleneck. The AI is faster than you, but it still waits for you at every decision point.

Jules breaks that model completely. You describe a task, submit it, and walk away. Jules provisions a fresh cloud virtual machine, clones your repository, reads the relevant code, writes a plan using Gemini models, implements the changes, runs your test suite, and opens a pull request. By the time you come back, the PR is waiting for review.

The productivity unlock here is not that Jules is faster per task. It is that you can submit ten tasks simultaneously and return to ten pull requests. Your output is no longer bottlenecked by the number of things you can actively supervise at once.

How Jules Works: Explained Step-by-Step

Understanding the architecture helps you use Jules more effectively, so let me walk through exactly what happens when you submit a task.

Step 1: Task submission

You describe the task in plain language. You can do this through the Jules UI, by mentioning @jules in a GitHub issue, or directly from the Gemini CLI by typing /jules followed by your task description. All three paths put the task into the same queue.

Step 2: VM provisioning

Jules' scheduler picks up the task and provisions a fresh, isolated cloud virtual machine managed by Google. Each task gets its own VM — no shared state between tasks, no leftover context from a previous run.

Step 3: Repository checkout

The VM checks out your repository. Jules then reads the codebase, identifies the files most relevant to your task, and builds an understanding of the context it needs to work with.

Step 4: Planning

A Gemini Pro planner instance breaks the task into specific implementation steps. Jules shows you this plan before execution, one of its most important safety features. You can review the plan and steer it before any code is written.

Step 5: Implementation and testing

Jules implements the changes across multiple files if needed, then runs your existing test suite to verify nothing is broken. It handles the full implementation, not just the first file.

Step 6: Pull request

Jules opens a PR against your repository with a plain-language summary of what it changed and why. You review, request changes if needed, and merge. The entire handoff is through your existing GitHub workflow.

The Gemini CLI integration is worth highlighting specifically. Installing the Jules extension means you can delegate tasks without leaving your terminal:

Languages Jules supports: Node.js, Python, Go, Rust, Java, and TypeScript. This matters because most async coding agents are JavaScript/TypeScript-first. Jules is not.

Read Also: What Is Midjourney and How to Use It?

What is Google Antigravity 2.0?

The original Antigravity launched in November 2025 as a single AI-powered IDE for macOS, Windows, and Linux. It was pretty useful, but limited in scope. Antigravity 2.0, on the other hand, is launched as a completely different beast. It is a five-surface agent development platform built around the idea that the IDE is no longer the unit of work; the agent is.

Here are the five surfaces of Antigravity 2.0:

1. Desktop app (Antigravity 2.0)

The new desktop application runs dynamic subagents in parallel, schedules background tasks, and accepts voice commands. You can orchestrate multiple agents working on different parts of a project simultaneously from a single interface. Here, AI Studio, Android, and Firebase are all natively integrated.

2. The agy CLI

The command-line interface brings the full Antigravity agent harness to your terminal. It has the same capabilities as the desktop application, which is accessible from anywhere in your existing workflow without switching to a separate application.

3. The Antigravity SDK

The SDK lets you host agents on your own infrastructure. You will have programmatic control over agent creation, customization, and deployment. Agents created with the SDK run as managed services on the Gemini API. This means Google will manage sandboxing, computing, and scaling. You just have to define the agent behavior.

4. Managed Agents in the Gemini API

It is the biggest developer-facing addition. Instead of building your own agent scaffolding, you make a single API call and get a fully operational agent running inside a secure Linux sandbox on Google's infrastructure.

5. Enterprise Agent Platform

The Enterprise Agent Platform provides multi-tenant deployment, centralized management, and Gemini Enterprise connector support, including Microsoft SharePoint, OneDrive, and ServiceNow. It is best for organizations deploying agents at scale.

The mental model shift Antigravity 2.0 forces is real. If your previous workflow was "write code, use AI for completion and suggestions," Antigravity wants to replace that with "describe what you want built, orchestrate agents that build it, review and ship." Whether that workflow works for your team depends on how well-specified your tasks typically are.

Managed Agents in the Gemini API

This is the Gemini API addition that I think is most underappreciated from I/O 2026, so I want to give it proper attention.

Before Managed Agents, building an AI agent that could reason across multiple steps, execute code, browse the web, and manage files meant you had to build a lot of scaffolding yourself. You would call the model, get a response, parse tool calls, execute those tools, feed results back, and loop. And you had to manage your own sandboxed execution environment for any code the agent ran.

Managed Agents collapse all of that into a single API call.

When you create a Managed Agent, you get an autonomous entity running inside a persistent, secure Linux sandbox hosted and managed by Google. It can reason across multiple steps, write and execute code, manage files, browse the web, and use tools, all within that sandbox. This way you just have to interact with it through the API without managing the infrastructure.

Here is what you need to know about the current implementation:

• The base agent model at launch is antigravity-preview-05-2026
• You can create up to 1,000 managed agents per project
• Agent versioning and rollback are not yet available
• Subagent nesting is not yet supported (one level of orchestration only)
• MCP server connections are supported, so you can extend agents with your existing tooling
• There is a free tier for development and experimentation
• Production usage is billed on token consumption, sandbox compute time, and storage

A Practical Use Case

Any multi-step workflow you currently handle with complicated prompt chaining, custom tool scaffolding, or manual agent loops can now be replaced with a single Managed Agent call. Customer support resolution, code review pipelines, data extraction workflows, document processing chains. All of these map cleanly onto Managed Agents.

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Native Android Vibe Coding in AI Studio

"Vibe coding" seems like a fancy word that may connect to Gen-Z. It has been a developer buzzword through early 2026. Google AI Studio now supports it natively for Android apps. This is a practice of building software by describing what you want in natural language and letting an AI generate the full implementation.

Here is what the new workflow looks like:

1. You describe the Android app you want to build in plain language inside AI Studio.
2. AI Studio generates the full project, layout, logic, and dependencies.
3. You can move the project from AI Studio directly to local development in Antigravity with a single click.
4. From there, you can deploy directly to the Google Play test track.

The entire path from idea to a testable Android app now runs inside Google's tools without requiring you to touch Android Studio for the initial build. For prototyping, validation, and MVPs, this is a meaningful reduction in friction.

The integration with Stitch, Google's AI UI design tool that generates React and Flutter components from design inputs, also feeds into this workflow. Stitch output becomes the UI layer of an Antigravity-built application, so design and implementation are steps in the same pipeline rather than handoffs between separate tools.

Code Mender: Automated Vulnerability Patching

Code Mender is another new developer tool announced at Google I/O 2026 that is worth knowing about even if it is not the headline feature.

The job it does is specific: it scans your codebase for security vulnerabilities, generates patches to fix them, and submits those patches as pull requests for your review. It runs asynchronously, like Jules, so you do not need to supervise it.

For teams maintaining large codebases with accumulated technical debt around security, Code Mender automates the patching work that currently requires dedicated security engineering time. It is not replacing a security team, but it reduces the amount of manual patching work that the team has to do.

At the time of writing, Code Mender is in preview. Pricing and availability details beyond the US have not been fully announced.

Also Read: Julius AI: AI Data Analysis Tool for Beginners

Project Astra: Multimodal AI for Real-World Agents

Project Astra moved to developer preview at I/O 2026, which means you can now build on it for the first time.

Astra is Google's research project for real-world multimodal AI agent capabilities. These agents can see through a camera, hear through a microphone, understand the physical environment, and take action based on what they perceive. Think of it as the foundation for AI agents that work in the real world rather than just in digital environments.

The developer preview opens access to Astra's real-time multimodal perception APIs. If you are building anything that involves physical-world AI interaction, assistive technology, robotics interfaces, smart device applications, field work tools, this is the API to watch.

Astra is also the underlying technology for Google's glasses hardware announced at I/O, in partnership with Samsung, Warby Parker, and Gentle Monster. Developer access to Astra means you can start building applications for that hardware platform now, ahead of consumer availability.

Gemma 4: The Open Source Option

Not every developer wants to call a commercial API. some of theme may have their on-premises requirements, cost constraints, or fine-tuning needs. Google's open-source Gemma family is the right alternative for them. Gemma 4 launched at I/O 2026 with two models worth knowing:

1. Gemma 4 31B

Third place on the Arena AI leaderboard as of May 2026. Supports native function calling, multimodal inputs, and audio. Apache 2.0 license, which means you can use it commercially without restriction.

2. Gemma 4 26B MoE (Mixture of Experts)

Sixth place on the Arena leaderboard. The MoE architecture means it activates only a subset of parameters for each inference pass, making it significantly more efficient to run than a dense model of similar capability. Also Apache 2.0.

Gemma has crossed 400 million total downloads across the model family. It is available on Hugging Face, Kaggle, and through the Gemma page on Google AI for Developers. For developers building RAG pipelines, fine-tuned domain-specific models, or applications with strict data residency requirements, Gemma 4 is currently the most capable open-source option from Google.

Gemini API Pricing

Let me put the full pricing picture in one place, because it has changed significantly since the start of 2026.

API model pricing

Free tier

Google AI Studio is free to use with daily request limits and no credit card required. Pro models have been paid-only since April 1, 2026, but Flash and Flash-Lite models remain accessible on the free tier for development and prototyping.

Read Also: GPT-4: Features, Capabilities, Pricing and Examples

Consumer plans (for developers building Workspace integrations)

Gemini for Developers vs Competitors

If you are deciding where to build, you need to know how the Google developer stack compares to the competition.

I. Gemini API vs OpenAI API

II. Jules vs GitHub Copilot Workspace

Related Article: What is ChatGPT?

III. Jules vs Claude Code (Anthropic)

IV. Antigravity 2.0 vs Cursor

Related Article: Cursor AI: The Ultimate Guide to the AI-First Code Editor

How to Get Started with Gemini for Development?

Here is the fastest path from zero to a working Gemini API integration:

Step 1: Get your API key

Go to ai.google.dev, sign in with your Google account, and create a project. Your API key is issued immediately. No credit card is required to start on the free tier.

Step 2: Install the SDK

For Python:

For Node.js:

Step 3: Make your first call

Python:

import google.generativeai as genai

genai.configure(api_key="YOUR_API_KEY")
model = genai.GenerativeModel("gemini-3.5-flash")
response = model.generate_content("Explain Managed Agents in the Gemini API")
print(response.text)

Node.js:

const { GoogleGenerativeAI } = require("@google/generative-ai");

const genai = new GoogleGenerativeAI("YOUR_API_KEY");
const model = genai.getGenerativeModel({ model: "gemini-3.5-flash" });
const result = await model.generateContent("Explain Managed Agents in the Gemini API");
console.log(result.response.text());

Step 4: Try Jules

Go to jules.google.com, connect your GitHub account, and submit your first task. Start with something well-defined and low-risk, a bug fix with a clear description, or a small feature with existing tests. Review the plan Jules shows you before approving execution.

Step 5: Explore Antigravity 2.0

Download the Antigravity desktop app from antigravity.google.com. Connect it to your Google AI Studio account and your GitHub. Run a simple agentic workflow, even just having it generate and test a small module, to understand how the multi-agent orchestration model works before building on top of it.

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Is Gemini the Right API for Your Project?

Here is how I would think about it honestly.

Choose Gemini if:

• You are building Android apps, Google Workspace integrations, or anything that benefits from native Google ecosystem connectivity
• Multimodal inputs — especially video, audio, and long PDFs — are central to what you are building
• You want asynchronous coding agents and async is the right model for your team's workflow
• You have large, repeated contexts where caching can dramatically cut your API costs
• You want open-source flexibility alongside a commercial API (Gemma + Gemini API together)

Consider alternatives if:

• You need the absolute broadest third-party integrations and community plugins today. OpenAI still wins here
• You want synchronous, fine-grained agent supervision at every step. Claude Code or Cursor Agent mode is more comfortable
• You are working with very tight budgets and simple text tasks. DeepSeek V4 Flash undercuts all Gemini models significantly on pure text at $0.14/$0.28 per million tokens
• You need agent versioning and rollback in production. Managed Agents do not support this yet

The honest answer is that Google's developer ecosystem in mid-2026 is more competitive than it has been at any point in the last three years. The tooling has caught up to the model quality. If you wrote it off 12 months ago, it is worth a second look.

Wrapping Up

Google I/O 2026 showed that Google is focusing more than ever on developers. With tools like Jules, Antigravity 2.0, Managed Agents, and Gemini 3.5 Flash, it is building not just better AI models but a complete platform for creating AI-powered applications.

The tools are practical, the pricing is competitive, and the deep integration with Android, Firebase, and Google Workspace makes the ecosystem especially attractive for developers already using Google products. If you are building AI applications today, these new offerings are well worth exploring.

Also Read: Claude Fable 5: Anthropic's Most Powerful AI Model

Frequently Asked Questions (FAQs)

Q1. How is Jules different from GitHub Copilot?

GitHub Copilot is a synchronous coding assistant, where as Jules is asynchronous. Jules also runs on Google Cloud VMs, not locally, and handles multi-file implementation and test execution as part of a single task submission.

Q2. What is the best Gemini model for coding?

Gemini 3.5 Flash, launched at Google I/O 2026, is currently the strongest Gemini model for coding and agentic tasks. It outperforms Gemini 3.1 Pro on coding benchmarks while running four times faster and costing 25% less at $1.50 per million input tokens.

Q3. What is Android Vibe coding in AI Studio?

Native Android vibe coding in Google AI Studio lets you describe an Android app in plain language and have AI Studio generate the full project. You can then move the project to Antigravity with a single click and deploy directly to the Google Play test track for testing.