• From Gemini 2.5 to 3.5: a year of iteration
• Launch day at Google I/O: speed and agents
• Building an agentic ecosystem
• Cost, scale and strategic stakes
• Competing visions of the next AI wave

Google I/O 2026: Gemini 3.5 Flash Model Launched for Speed and Efficiency Google is using its 2026 I/O conference to argue that generative AI only becomes truly useful when it stops acting like a chatbot and starts behaving like a fleet of fast, cheap software agents.

§From Gemini 2.5 to 3.5: a year of iteration

At I/O 2025, Google was still focused on the Gemini 2.5 line. In the year since, it has cycled through the 3.0 and 3.1 families and is now rolling out Gemini 3.5 Flash across “a wide range of Google products,” positioned as a model that delivers “frontier-level intelligence” while being efficient enough to make complex, long-running AI tasks viable at scale.

§Launch day at Google I/O: speed and agents

On May 19, 2026, Google introduced Gemini 3.5 Flash as its “strongest yet for coding and autonomous AI agents,” saying it can independently execute coding pipelines, manage research projects and, in internal tests, even build an operating system from scratch. DeepMind chief technologist Koray Kavukcuoglu framed the core pitch: 3.5 Flash offers “an incredible combination of quality and low latency,” outperforming the 3.1 Pro frontier model on nearly all benchmarks and running 4x faster, with an optimized variant claimed to be 12x faster at the same quality.

Developer advocate Addy Osmani echoed that positioning, calling it “fast, great for building rich UIs + agents” and stronger on “long-horizon tasks and multi-step workflows.” CEO Sundar Pichai highlighted that Gemini 3.5 Flash is “available today for everyone in @antigravity and across our products and APIs,” and said it is better than 3.1 Pro “across almost all benchmarks with huge progress in coding.”

§Building an agentic ecosystem

Behind the model, Google is building an ecosystem. Gemini 3.5 Flash was co-developed with Antigravity, described as a “native environment where [agents] can live, work, and execute,” and demonstrated onstage as multiple agents collaborating to assemble a full operating system inside the agentic IDE. At I/O, Google released Antigravity 2.0 as a standalone, agent-first desktop application.

A parallel product push, Gemini Spark, is pitched as a 24/7 personal AI agent “taking action on your behalf, and under your direction,” running on Gemini 3.5 atop Antigravity so it can perform “long-running tasks easily in the background.”

§Cost, scale and strategic stakes

Google emphasizes that 3.5 Flash can output nearly 300 tokens per second while matching larger frontier models like 3.1 Pro that operate at roughly a quarter of that speed, a combination it says could let heavy AI users “save a billion dollars per year” by shifting workloads to the more efficient model. Internally and with partners, the company points to banks and fintechs using 3.5 Flash to automate multi-week workflows and data teams using it to mine complex datasets, with agents able to run autonomously for hours.

Demis Hassabis, head of Google DeepMind, amplified the performance framing, calling Gemini 3.5 Flash “amazing,” claiming it performs better than 3.1 Pro on coding and agentic tasks, is 4x faster than other frontier models and 12x faster in Antigravity, reaching “800 tokens/sec” and “often at less than half the cost.”

Notably, all of this sits alongside Gemini Omni, a more expansive multimodal system that “doesn’t just build scenes that look real, it reasons about what should happen next,” combining an “intuitive understanding of physics” with broader world knowledge, and beginning rollout via video outputs in Google AI Plus. Hassabis describes Omni as a “major leap in world understanding & multimodal editing,” able to take photos, video and audio and “build entirely new scenes,” with the ambition that it will “handle any input & any output” over time.

§Competing visions of the next AI wave

Technology reporters interpret the shift as Google “bet[ting] its next AI wave on agents, not chatbots,” repositioning Gemini from a question-answering interface to a system that can “plan, build, and iterate on real work with minimal human input.” Another analysis argues that Gemini 3.5 Flash “might be fast enough for gen AI to make sense,” by finally aligning frontier-level performance with the speed and cost profile needed for scalable, agentic use cases in business and development.

In Google’s telling, Omni points to a future of richer, multimodal understanding, while Flash and Antigravity are the practical engine room—high-speed, lower-cost agents designed to quietly execute the long, messy workflows that could decide whether this next wave of AI is economically sustainable.

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