Google introduced many new Gemini features and tools at Google I/O 2026, including the powerful new Google Omni video creator and editor. But the Gemini 3.5 Flash model is supposed to be the real workhorse, according to the company. Google has positioned it as faster and more powerful at coding, long contextual reasoning, multimodal understanding, and more. More importantly, it’s supposed to be able to handle the kind of tangled queries that real people actually throw at the AI.
To see where Gemini 3.5 Flash really stands, I gave it five prompts designed to test very different strengths. Some were practical. Some were deliberately ridiculous. All highlighted the capabilities that Google highlighted as part of Gemini 3.5 Flash’s evolution beyond previous Flash models and Gemini 3.1.
1. Space simulation
For the first test, I wanted to push multimodal reasoning, understanding the long-term context and generating technical code. Gemini 3.5 Flash is expected to handle complex information while transitioning smoothly to practical execution. So I handed him a dense aerospace report on space debris and asked him to do a user-friendly simulation. Specifically, I wrote: “Use the attached IADC State of the Space Debris Environment report to create an interactive simulator showing how debris and traffic in orbit will accumulate and the potential danger to objects in orbit as a result.” »
Gemini wrote long and complicated code using data extracted from the report. Once translated, the code became the visually impressive simulation you can see in the video above. He built the interface concept around storytelling rather than raw numbers.
Most impressive was the clarity with which he expressed the reasons for his choices. “The dashboard should help users understand not only that debris increases over time, but also how launch behavior and mitigation decisions influence long-term outcomes,” Gemini wrote.
2. Weekend Planner
I often use trip planning as an AI test because it can show both the power and flaws of how an AI deals with complex variables. Gemini 3.5 Flash emphasizes agentic planning and multi-step reasoning, so the next challenge was aimed directly at seeing how it handled many additional details.
I told him to “plan a 4 day road trip through the Hudson Valley and Catskills.” Create a comprehensive, multi-stop itinerary that coordinates morning hiking trails, mid-day artisan food stops, and scenic routes, with a built-in “rainy day save option” for each afternoon.
Gemini 3.5 Flash approached this mission with surprising restraint. The first day was filled with river views and hikes without immediately exhausting the traveler. Scenic routes connected naturally rather than zigzagging unpredictably across the map. The dietary recommendations aligned pretty well geographically and the weather hazards made perfect sense, as Gemini pointed out:
“Alternatives to rain must preserve the emotional purpose of the original activity. An afternoon of hiking replaced by browsing unrelated commercial spaces creates disruption rather than continuity.”
3. Binding logic
Next came procedural reasoning, the kind of structured planning that Gemini 3.5 Flash is supposed to excel at. Thinking of a project I have in mind, I asked Gemini to “act as an expert book restorer and provide a strict, step-by-step amateur guide to custom journal binding at home.”
The manufacturing instructions quickly reveal weaknesses. Too vague and beginners fail immediately. Too technical, and people give up halfway through looking at the glue angrily. Gemini 3.5 Flash has found common ground, setting expectations and separating essential steps from optional enhancements. This explained the likely errors without seeming condescending.
“Your goal is not museum curation quality, but creating a lasting journal while learning the fundamentals of bookbinding,” he says. “Drying time is part of the process rather than downtime between steps. »
4. Quick cleaning
The following test aimed to examine Gemini 3.5 Flash’s visual reasoning improvements and claims of better action planning. I gave him a photo of a room in my house that needed organizing and cleaning, and told him to “create a 25-minute cleaning plan, tell me what to do first, what to ignore, and how to make the room 80% better with minimal effort.”
Cleaning tips seem trivial until you realize that most people fail in their cleaning attempts for reasons of strategy rather than motivation. Older AI systems often recommend approaching everything the same way, which doesn’t help matters. Gemini 3.5 Flash understood sorting. He said he would prioritize visual impact and momentum.
“Focus on high-visibility clutter first rather than hidden organizational issues,” Gemini advised. “Visible progress creates momentum while quickly improving perceptions of cleanliness. Avoid opening drawers or starting extensive organizational tasks during short cleaning sessions.”
5. Secret Penguins
For the final test, I wanted to push Gemini 3.5 Flash’s parallel reasoning, where it breaks a larger problem into smaller pieces and addresses multiple lines of thought simultaneously rather than solving everything one step at a time.
Just for fun, I set up a deliberately ridiculous mission meant to reward structured investigation. I told Gemini to “do a thorough background check on a potential roommate who claims to be a ‘regular human guy’ but is clearly three penguins stacked in a trench coat.” »
The response relied on the joke and carried out the mission by dividing the task into parallel lines of investigation and calling them a real intelligence operation. A sub-agent took care of the behavioral analysis. Another focused on environmental evidence. A third examined signals of social coherence. Gemini tracked each stream independently while periodically merging the results into a scalable assessment summary.
“Sub-Agent 1: Mobility Analysis: Observed indicators include unusual balance changes, synchronized lower body movements, and a high probability of multiple organisms coordinating locomotion. »
Another section read: “Sub-Agent 3: Social Model Analysis. The claim that ‘an ordinary human’ remains unverified. Further evidence is requested regarding frequency of fish purchases, unexplained ice accumulation, and suspect resistance to hot climates.”
Gemini continued the joke and showed how parallel agentic reasoning is changing the shape of AI problem solving. Previous systems often processed complex prompts by examining them sequentially, which could make large queries slower or less organized. Gemini 3.5 Flash instead approached the fake investigation as if several specialists were collaborating at the same time.
Gemini 3.5 Flash consistently demonstrated how it could stay on task, something previous fast models sometimes struggled with. Whether analyzing orbital debris trends, planning road trips, or investigating suspicious penguins, he maintained context while adapting his reasoning style appropriately to the mission.
Perhaps most important is the way its strengths flow naturally into one design. This change changes what Gemini 3.5 Flash can become in everyday life, at least if people accept the tradeoffs, like having to give it broad access to their information to get the most out of it.
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