Google didn’t just ship a faster model at I/O 2026. With Gemini 3.5, the company is making a pointed argument: raw intelligence isn’t enough anymore. What matters is whether an AI can actually do things — browse, execute, reason, and act — without a human holding its hand through every step. That’s the bet behind Gemini 3.5, and it’s a significant one.
What Led Here: Google’s Long Road to Agentic AI
Let’s be honest — Google has had a complicated few years in AI. The original Gemini 1.0 launch in late 2023 was overshadowed by a demo that turned out to be heavily edited. Gemini 1.5 recovered some ground with its million-token context window, which was genuinely impressive. Then Gemini 2.0 and 2.5 pushed further into multimodal territory and reasoning benchmarks.
But throughout all of this, OpenAI kept the narrative. GPT-4o, o3, and the operator ecosystem gave developers a clear story about what AI agents could look like in production. Anthropic quietly built a reputation for Claude being the most reliable coding assistant. Google, despite having some of the best underlying research in the world, kept feeling like it was one step behind in the product layer.
Gemini 3.5 feels like Google’s answer to that problem. Not just a smarter model, but a model designed from the start to take actions in the world — what the company calls “frontier intelligence with action.” The timing makes sense too. The industry has shifted. Benchmarks matter less than they did two years ago. What developers and enterprises actually want to know is: can this thing do my work?
What Gemini 3.5 Actually Does Differently
The headline is the combination of strong reasoning capabilities with built-in agentic behavior. Google announced Gemini 3.5 at Google I/O on May 19, 2026, positioning it as their most capable series to date. Here’s what the release covers:
- Frontier reasoning: Gemini 3.5 pushes further on complex multi-step problem solving, with particular improvements in math, science, and code generation — areas where prior Gemini versions sometimes stumbled against o3 and Claude 3.7.
- Agentic action layer: The model is built to connect directly with tools, APIs, and real-world systems. This isn’t a plugin afterthought — action capability is baked into the model architecture itself.
- Multimodal inputs: Text, images, audio, and video are all fair game, continuing the trajectory Google set with earlier Gemini versions.
- Extended context: Building on the 1.5 and 2.5 context innovations, Gemini 3.5 handles extremely long documents and conversation histories with meaningful comprehension rather than just token stuffing.
- Developer API access: Available through Google AI Studio and Vertex AI, with tiered pricing that scales from experimentation to enterprise deployment.
- Integration with Google Workspace and Search: The model connects to Google’s existing product surface area, which is genuinely one of Google’s strongest competitive advantages here.
The “action” framing is doing a lot of work in this announcement. What Google means is that Gemini 3.5 is designed for agentic workflows — chains of tasks where the model plans, uses tools, checks its own outputs, and executes across multiple steps without constant human intervention. Think less chatbot, more autonomous collaborator.
How It Compares to the Competition
This is where things get interesting. OpenAI’s o3 and GPT-4o are the obvious benchmarks. On pure reasoning tasks, the gap between frontier models has narrowed considerably — at this level, differences are often marginal in practice, even when benchmark numbers look dramatic on slides. What differentiates Gemini 3.5 is the native integration with Google’s infrastructure and the explicit agentic design philosophy.
Anthropic’s Claude 3.7 has been widely praised for coding reliability and following complex instructions carefully. Google needs Gemini 3.5 to compete directly in that space, and the code generation improvements suggest they’re taking it seriously.
Meta’s Llama series continues to pressure everyone on the open-weight front. Google’s advantage there is less about raw capability and more about the tooling, safety infrastructure, and enterprise relationships that come with a managed API.
The Workspace Angle Nobody’s Talking About Enough
Here’s the thing: Google has something OpenAI and Anthropic genuinely don’t — billions of people already using Gmail, Docs, Sheets, and Drive every single day. If Gemini 3.5 integrates deeply with Workspace in ways that previous versions didn’t quite deliver on, that’s a distribution story that no amount of API pricing can compete with.
The earlier Gemini-in-Workspace experience was fine. Not transformative. If 3.5 actually makes those integrations feel like having a capable assistant rather than an autocomplete with ambitions, that’s when Google’s position in enterprise AI becomes a lot harder to dislodge.
What This Means for Developers and Businesses
For developers building on AI APIs, Gemini 3.5 gives them another serious option — and competition here is genuinely good for everyone. Google AI Studio remains one of the easier on-ramps for experimentation, and Vertex AI gives enterprises the compliance and scalability controls they actually need in production.
The agentic framing matters practically. Teams building autonomous workflows — customer support bots that actually resolve tickets, coding assistants that can run tests and iterate, research tools that pull and synthesize from live sources — need a model that handles tool use reliably. If Gemini 3.5 delivers on that promise consistently, it enters real consideration for those use cases.
For businesses already deep in the Google Cloud ecosystem, this is almost a no-brainer to evaluate. The integration story with existing Google infrastructure, combined with Vertex AI’s enterprise features, makes the switching cost low enough that running a pilot is easy to justify. We’ve already seen over 100 companies actively building on Gemini — that number is likely to grow substantially as 3.5 matures.
For teams that have been evaluating coding-focused AI tools, the comparison to what OpenAI has been building with Codex is worth thinking through carefully. The approaches differ — Codex leans into the software development workflow specifically, while Gemini 3.5 is going broader. Depending on your use case, that distinction matters a lot. If you want to understand how AI coding tools are landing in real engineering organizations, the work being done around how NVIDIA engineers actually use AI coding tools day-to-day gives useful context.
Who Should Pay Attention Most Closely
A few groups should be watching Gemini 3.5 more carefully than the general announcement might suggest:
- Enterprise CIOs already on Google Cloud: This is the clearest path to ROI. The integration story is real, and the enterprise controls on Vertex AI are mature.
- Developers building multi-step agents: The agentic design is purpose-built for this. Worth prototyping against your current stack.
- Teams evaluating AI for document-heavy workflows: Long context handling has been a Gemini strength, and 3.5 builds on that.
- Anyone watching the OpenAI-Google competition: This announcement signals Google is done playing catch-up and wants to set terms.
Key Takeaways
- Gemini 3.5 launched at Google I/O 2026, combining frontier reasoning with native agentic action capabilities.
- The model is available through Google AI Studio and Vertex AI for developers and enterprises.
- Agentic behavior — planning, tool use, multi-step execution — is a core design principle, not a bolt-on feature.
- Google’s Workspace integration gives Gemini 3.5 a distribution advantage that pure API players can’t match.
- On reasoning and coding benchmarks, Gemini 3.5 positions directly against OpenAI’s o3 and Anthropic’s Claude 3.7.
- For teams already in the Google Cloud ecosystem, evaluation friction is low — pilots are easy to spin up.
Frequently Asked Questions
What is Gemini 3.5 and how is it different from Gemini 2.5?
Gemini 3.5 is Google’s latest series of AI models, announced at Google I/O in May 2026. The key shift from 2.5 is the explicit focus on agentic action — the model is built to use tools, plan across multiple steps, and execute tasks in the real world, not just generate text responses.
Who is Gemini 3.5 designed for?
Google is targeting both developers building AI-powered applications and enterprises looking to deploy capable AI within their existing Google Cloud or Workspace infrastructure. The model scales from individual experimentation in AI Studio to production deployments on Vertex AI with enterprise compliance controls.
How does Gemini 3.5 compare to OpenAI’s o3 and Claude 3.7?
All three are frontier-tier models with strong reasoning and coding capabilities. Gemini 3.5’s differentiator is its native integration with Google’s product surface — Workspace, Search, and Cloud — which matters more in enterprise contexts than benchmark differences. Claude 3.7 still has an edge in developer trust for coding tasks, while o3 leads on some math and science benchmarks, but those gaps are narrowing.
When is Gemini 3.5 available and what does it cost?
Google made Gemini 3.5 available at or shortly after the Google I/O 2026 announcement on May 19, 2026, through Google AI Studio and Vertex AI. Pricing follows Google’s tiered model structure — exact per-token rates for Gemini 3.5 are available directly through the Vertex AI and AI Studio pricing pages, with rates varying by model variant and context length.
The real test for Gemini 3.5 isn’t the launch announcement — it’s what happens over the next six months as developers build on it and enterprises run real workloads. Google has the research depth, the distribution, and now a model explicitly designed for the agentic era. I wouldn’t be surprised if this marks the moment the Google-OpenAI competition genuinely tightens. The next benchmark that matters won’t be on a leaderboard — it’ll be in production.
