Gemini 3.5 Explained: What I Like and What I Don’t

Google just dropped Gemini 3.5 at Google I/O 2026 and honestly, it is a big deal. This is not just another gradual update. Google has positioned it as a model that bridges raw intelligence with real-world action and after going through everything Google has shared, I have a lot to say.

They are also hard at work on Gemini 3.5 Pro, which is already being used internally, with a broader rollout expected next month, a move that hints Google is not slowing down anytime soon.

In this article, I will break down what Gemini 3.5 actually is, what works really well in my case, what needs improvement, and whether it lives up to the hype. Let's get into it.

What Is Gemini 3.5?

Gemini 3.5 is Google DeepMind's latest family of AI models. Google released it on May 19, 2026, at Google I/O. The series starts with Gemini 3.5 Flash, and Google says Gemini 3.5 Pro is already in internal testing and will roll out next month. The core idea behind the new Gemini release is combining frontier intelligence with action. In simple terms, it is not just a model that answers questions. It is a model that can plan, execute, and complete complex multi-step tasks on your behalf.

Gemini 3.5 Flash is available right now to billions of users globally through:

• The Gemini app and AI Mode in Google Search

• Google AI Studio and Android Studio for developers

• Gemini Enterprise Agent Platform and Gemini Enterprise for businesses

Read Also: Gemini vs. ChatGPT

What Changed With Gemini 3.5 Compared to Earlier Models?

If you use Gemini 2.0 or Gemini 3.0, you will notice the difference quickly. Earlier Gemini models were strong at answering questions and handling text tasks. They were good at reasoning and could process images and documents. But they were still largely reactive. You asked, it answered.

Gemini 3.5 changes the interaction model entirely.

The biggest shift is the move from reactive AI to agentic AI. The new Gemini release does not just respond to prompts. It can plan a sequence of steps, execute them, check its own work, and adjust when something goes wrong. This is a fundamentally different way of using AI.

Here is what changed specifically:

• Agentic performance: Gemini 3.5 Flash outperforms Gemini 3.1 Pro on coding and long-horizon task benchmarks, even though Flash is the entry-level model in the new series

• Speed: The model processes output at 4x the speed of comparable frontier models. Earlier models did not come close to this

• Subagent coordination: Gemini can now run multiple agents in parallel through the Antigravity harness. Earlier versions handled tasks sequentially

• Multimodal reasoning: The model scores 84.2% on CharXiv Reasoning, showing a clear leap in understanding complex visual data like charts and graphs

• Cost: Google says the model completes tasks at less than half the cost of other frontier models. For high-volume use cases, this changes the economics entirely

In short, the older Gemini models were powerful AI assistants. Gemini’s latest version is designed to be an AI executor.

Gemini 3.5 Flash: The Headline Model

The first model in the Gemini family is Gemini 3.5 Flash. Google describes it as their strongest agentic and coding model yet.

Here is what stands out about the AI system on paper:

• It outperforms Gemini 3.1 Pro on challenging coding and agentic benchmarks

• It scores 76.2% on Terminal-Bench 2.1

• It scores 83.6% on the MCP Atlas

• It leads in multimodal understanding with 84.2% on CharXiv Reasoning

• It is 4 times faster than other frontier models in output tokens per second

That last point is worth repeating. Four times faster than other frontier models. That is a massive performance gap.

Google also places Gemini 3.5 in the top-right quadrant of the Artificial Intelligence Index. This means it sits at the intersection of frontier-level intelligence and exceptional speed. You do not have to trade quality for latency with this model.

SOURCE: Gemini 3.5: frontier intelligence with action

New Features in Gemini 3.5

Google packed a lot into this release. Here are the standout new features in Google’s latest AI that are worth knowing about.

1. Antigravity Subagent Support

Gemini works with Google's Antigravity platform to deploy multiple subagents that tackle parts of a task in parallel. Think of it as one intelligent manager coordinating a team of specialists. This is what makes long, complex workflows possible without manual oversight at every step.

2. Gemini Spark

Gemini Spark is a new personal AI agent built on Gemini. It runs continuously in the background, manages your digital tasks and takes action on your behalf while you stay in control. This is not a feature inside an app. It is a standalone AI agent for everyday life.

3. AI Mode in Google Search

Gemini now powers AI Mode in Google Search. This goes beyond traditional search results. The model can build interactive visuals, explain concepts dynamically and provide information agents that work for you around the clock.

4. Interactive UI and Graphics Generation

You can now describe what you want to build in plain text and Gemini will generate interactive web UIs, animations and branded visuals. It can explore multiple design directions at once and give you options to choose from.

5. Advanced Safety Interpretability Tools

Google has added new interpretability tools that check and understand the model's inner reasoning before it produces a response. This is a meaningful improvement in making AI behavior more predictable and safer. Earlier models did not have this layer of internal reasoning checks.

6. Improved OCR and Document Reasoning

Gemini handles complex document types much better than its predecessors. It can reason over 100+ page documents, extract relevant information and return reliable answers with low latency. This is already live in enterprise contexts like Macquarie Bank's customer onboarding workflow.

Read Also: ChatGPT Tutorial

How Gemini 3.5 Performs in Real World Use

The latest version of Gemini is becoming popular because it can handle practical, everyday tasks that feel useful outside normal AI chatting. From understanding visuals to handling large files and giving real-time assistance, it offers experiences that feel more interactive and natural.

I personally used it for analyzing long PDFs, understanding YouTube videos, testing coding workflows, and getting real-time help while sharing my screen, which made the experience feel much more practical than a standard chatbot.

1. Real-Time Tool Orchestration & Execution

Gemini’s latest version can autonomously connect to live APIs, execute code in real time, browse the web, manage files, and chain multiple tools together to complete complex tasks end-to-end — not just planning or suggesting, but actually executing with live feedback and error correction at every step, making it a true AI executor rather than just an AI assistant.

Prompt:

You are an AI execution engine with access to multiple tools.

When I give you a goal, do NOT just explain how to achieve it.
EXECUTE it step by step using your available tools in sequence.

Rules:
- Break the goal into sub-tasks automatically
- Pick the right tool for each sub-task (search, code, write, calculate, browse)
- Show what tool you're using and why, in one line
- Pass the output of each step as input to the next
- If a step fails, self-correct and try an alternative
- End with a final consolidated output — not a summary of what you did

Output format per step:
[Tool: X] → [Action taken] → [Result]

Final Output:
[Clean, usable result the user actually asked for]

My goal: Research the top 5 AI tools launched in 2025, 
compare them by use case, pricing, and user rating, 
then generate a ready-to-send investor briefing document 
highlighting the most disruptive one.]

2. Advanced AI Coding Assistant

It can create, troubleshoot and enhance code in multiple languages, and interpret complex logic and navigate large code repositories which will enable developers to create applications used in real-world scenarios faster and more efficiently than ever before.

Prompt: “Fix this Python error and explain the bug.”

CODE:

def calculate_average(numbers):
    total = sum(numbers)
    average = total / len(number)   # Error here
    return average

data = [10, 20, 30, 40]
print(calculate_average(data))

BEFORE:

You Can Also Test Your Python Code Here: Online Python Compiler

AFTER:

3. Multi-Step AI Agents (Agentic Workflows)

Gemini 3.5 can automate developer workflows by handling multiple tasks in one process. It can research frameworks, generate code, debug errors, organize documentation, and create project structures automatically, making development faster and more efficient.

Prompt: “Create a full-stack task manager app plan with tech stack, folder structure, API routes, and database schema.”

4. Large Codebase Understanding

Unlike most AI tools that mainly focus on small code snippets, Gemini can understand and work with large coding projects and complete codebases. Developers can even upload their own code snippets or project files to debug errors, optimize performance, and manage complex applications more efficiently.

Prompt: “Review this backend project structure and suggest performance improvements.”

CODE:

from flask import Flask, request
import sqlite3

app = Flask(__name__)

@app.route('/users')
def get_users():
    conn = sqlite3.connect('users.db')
    cursor = conn.cursor()

    # Fetch all users from database
    cursor.execute("SELECT * FROM users")
    users = cursor.fetchall()

    result = []

    for user in users:
        # Extra database query inside loop (slow)
        cursor.execute(
            f"SELECT * FROM orders WHERE user_id = {user[0]}"
        )
        orders = cursor.fetchall()

        result.append({
            "id": user[0],
            "name": user[1],
            "orders": orders
        })

    conn.close()
    return {"users": result}

if __name__ == '__main__':
    app.run(debug=True)

BEFORE:

alt="large codebase understanding gemini 3.5"

AFTER:

5. AI-Powered UI and Web Design Generation

Gemini’s latest version is extremely proficient at creating modern user interface concepts from basic text prompts such as websites, landing pages, dashboards and front-end layouts. Developers and designers will now be able to effortlessly generate professional design ideas for the purposes of developing and designing a website without having to start with nothing.

Prompt: “Design a modern AI startup landing page.”

6. Personal AI Agent (Gemini Spark)

Using machine learning, Gemini Spark is able to assist with scheduling, task management and productivity enhancement as a personalized assistant. It learns about you (contextual learning) over time so it can help you with your planning, learning goals and workflow.

Prompt: “Create my weekly AI learning schedule.”

Read Also: Top Artificial Intelligence Interview Questions

Advantages of Gemini 3.5

Before getting into my personal takes, here is a straight look at the core advantages Gemini’s latest version brings to the table.

1. Speed at Frontier Quality

Gemini’s latest version sits in the top-right quadrant of the Artificial Analysis Intelligence Index. This means it offers both high intelligence scores and high output speed. Most models force you to choose one. Gemini gives you both.

2. Cost Advantage

Running AI at scale costs money. Gemini cuts that cost significantly. Google says it handles tasks at less than half the price of other frontier models. For businesses that run thousands of AI calls per day, this is a real financial advantage.

3. True Agentic Capability

Gemini Flash does not just generate text. It plans, executes and iterates. It can run multiple subagents in parallel, sustain context over long workflows and complete tasks that would otherwise require a human in the loop.

4. Multimodal Strength

The model handles text, images, charts, code and documents together. This breadth makes it useful across a much wider range of real-world tasks than text-only models.

5. Ecosystem Integration

For Google Cloud users and developers already in the Google ecosystem, Gemini Flash integrates smoothly into existing tools including Google Search Android Studio and Google Workspace.

6. Safety Architecture

Google has applied its Frontier Safety Framework to Gemini. The new interpretability tools add an extra layer of internal reasoning checks before the model responds. This is a significant step toward more predictable and controlled AI behavior.

Read Also: Generative AI Models

What I Like About Gemini 3.5

Now for my personal take. I have gone through everything Google has published, the benchmarks, the partner use cases, the product announcements and the technical details. Here is what genuinely stands out to me.

1. The Speed Is Genuinely Impressive

Speed has always been a selling point for the Flash series, but Gemini Flash takes it to another level. Being 4x faster than other frontier models is not a minor improvement. For developers building real-time applications and enterprises running large workflows, this speed advantage makes Gemini 3.5 Flash a practical first choice.

When you combine that speed with near-flagship-level intelligence, you get something genuinely useful. Most users have always had to pick between a fast cheap model and a slow smart one. Gemini’s latest version does not force that tradeoff.

2. Agentic Capabilities That Actually Work

This is probably the most exciting thing about the new Gemini release. The model is built from scratch for agent tasks. It does not just answer a question. It plans, builds and iterates to solve real problems over time.

When paired with Google's Antigravity harness, the new Gemini release can:

• Deploy collaborative subagents to tackle large-scale problems

• Execute multi-step coding workflows reliably

• Rename and categorize unstructured assets automatically

• Transform legacy codebases into modern frameworks like Next.js

The real-world examples Google shared are compelling. Shopify is using subagents in parallel to analyze complex data for more accurate merchant growth forecasts. Macquarie Bank is piloting it to accelerate customer onboarding by reasoning over 100+ page documents. Salesforce has integrated it into Agentforce to automate complicated enterprise tasks.

These are not demo use cases. These are live business workflows. That signals real production readiness.

3. Cost Efficiency That Matters at Scale

Google says the new Gemini release helps complete tasks at less than half the cost of other frontier models. For enterprises that run AI at scale, cost is not a secondary concern. It is often the deciding factor. If Gemini 3.5 Flash delivers comparable quality at a fraction of the price, that is a significant competitive advantage.

4. Multimodal Understanding Is Strong

Flash builds on the multimodal foundation of Gemini 3. It scores 84.2% on CharXiv Reasoning, which tests its ability to understand complex charts and visuals. This puts it ahead of the competition in multimodal tasks.

Ramp is already using it for smarter OCR on complex invoices. Xero is deploying it to handle multi-week administrative workflows. These use cases require the model to reason about visual and textual data together.

5. Richer UI and Graphics Generation

Gemini 3.5 Flash can generate interactive web UIs, animations and graphics. It can turn a plain text description into interactive hardware mockups. It can run multiple design concepts in parallel and produce a full branding concept in one go. For developers and designers, this opens many practical possibilities.

6. Gemini Spark Is a Real Personal Agent

Google introduced Gemini Spark, a personal AI agent powered by Gemini 3.5 Flash. It runs 24/7 and acts on your behalf while staying under your direction. Google is rolling it out to trusted testers now, with a broader Beta for Google AI Ultra subscribers in the US next week.

A personal AI agent that operates continuously and actually executes tasks is a meaningful step forward. If Gemini Spark fulfills that promise, it becomes a legitimate daily productivity tool.

Read Also: What is Claude Sonnet

Where Gemini 3.5 Is Lacking?

No model is perfect. Here is an honest look at where Gemini 3.5 still has room to grow.

1. The Pro Tier Is Missing at Launch

Google announced Gemini 3.5 as a family of models, but only Flash is out. If the pattern from earlier Gemini releases holds, Pro will be noticeably more capable. Right now you cannot access it. This makes the Gemini 3.5 launch feel incomplete.

2. Antigravity Is Required for Full Agentic Use

The most powerful agentic features in Gemini 3.5 Flash are tied to Google's Antigravity platform. If you are not building on Google's infrastructure, you will not get the full picture. This is a real barrier for teams that work on AWS, Azure, or other non-Google stacks.

3. Gemini Spark Is Geo-Restricted

Gemini Spark is arguably the most interesting new product built on Gemini 3.5 Flash. But it is only rolling out to trusted testers and then to Google AI Ultra subscribers in the US. If you are outside the US or on a lower-tier plan, you cannot try it yet.

4. Independent Benchmarking Is Not Here Yet

Google's own numbers look strong. But internal benchmarks and independent third-party evaluations do not always agree. Benchmarks like Terminal-Bench 2.1 and MCP Atlas are relatively new. The developer community needs time to stress-test Gemini 3.5 Flash across a wide variety of real tasks before we can take the numbers at full face value.

5. Context Retention in Very Long Workflows

Agentic tasks that run over many hours or many tool calls still carry a risk of context drift. This is an industry-wide challenge, not unique to Gemini. But it is worth flagging because Gemini 3.5's main pitch is around long-horizon task completion. Any failure to retain context over a complex workflow would undercut that core value.

6. No Offline or On-Device Option at This Tier

Gemini 3.5 Flash is a cloud-based model. There is no on-device or offline option for this level of capability. For enterprises in regulated industries that cannot send data to external servers, this remains a blocker.

Gemini 3.5 vs Other AI Models: Where Does It Stand?

AI models are evolving fast, but Gemini 3.5, ChatGPT GPT-5.5 and Claude Sonnet 4.6 each focus on different strengths. Here is a quick comparison of their performance, features, speed and real-world usability.

Read Also: Perplexity vs. ChatGPT

Who Should Use Gemini 3.5 Flash?

Not every model is right for every user. Gemini 3.5 Flash has clear strengths and those strengths map to specific types of users. Here is how to know if it is right for you.

1. Developers building agentic applications, coding assistants, or complex workflow automation will find Gemini 3.5 Flash to be a strong foundation model right now.

2. Enterprises in finance, legal, data science, or e-commerce that need to automate multi-step workflows at scale will benefit from the combination of speed, reasoning and subagent support.

3. Everyday users who access AI through the Gemini app or Google Search already have Gemini 3.5 Flash running under the hood. You do not need to do anything to benefit.

Final Verdict: Is Gemini 3.5 Worth the Attention?

Gemini 3.5 Flash is one of the most capable and practical AI model releases in 2026. The speed advantage is real. The agentic capabilities are genuinely new and useful. Cost efficiency makes it competitive at scale. The multimodal performance is strong.

The main caveats are the incomplete launch (Gemini 3.5 Pro is still coming), the reliance on Google's ecosystem for full agentic power and the limited rollout of Gemini Spark right now.

But overall, Gemini 3.5 represents a significant step forward in what AI models can actually do in the real world. It is not just a smarter chatbot. It is a system that takes action.

If you are a developer, get into Google AI Studio and start testing it today. If you are an enterprise user, talk to your Google Cloud rep about the Gemini Enterprise Agent Platform. And if you are just a regular user, open the Gemini app. The upgrade is already there.

FAQs

1. Is Gemini 3.5 capable of providing real-time voice and camera interaction?

Gemini 3.5 is equipped with Gemini Live technology that provides the ability to engage in live voice conversations and provide insight through interpretation of what is seen in real-time on a user's camera.

2. Will Gemini 3.5 be able to summarize YouTube Videos and PDFs?

One of Gemini 3.5’s many outstanding features is the ability to manage large content including YouTube videos, research documents, lengthy PDFs, and other types of documents by quickly providing summaries, explanations, and extracting the most significant points of each from large content sources.

3. Does it cost anything to use Gemini 3.5?

While many features can be accessed for free, there are some advanced capabilities that require a paid Google AI subscription; other limiting factors include the user's device, country of origin, and whether they are accessing through the mobile or web version.

4. How does Gemini 3.5 compare with other AI models with regard to accuracy?

Gemini 3.5 performs very well across coding, understands multiple modalities, and follows typical workflows within Google. However, like other AI models, it may also occasionally produce incorrect information/facts or incorrect answers.

5. Can Gemini 3.5 provide value to students and researchers?

Absolutely! Students can use Gemini 3.5 to summarize note-taking, to solve problems, to develop study materials, to clarify difficult concepts, and to organize research materials due to its enhanced ability to manage long contexts.