Google confirmed off a whole lot of new Gemini features and tools at Google I/O 2026, together with the highly effective new Google Omni video creator and editor. However the Gemini 3.5 Flash mannequin is meant to be the true workhorse, based on the corporate. Google has positioned it as sooner and stronger at coding, lengthy context reasoning, multimodal understanding, and extra. Extra importantly, it is supposedly able to managing the sort of tangled requests actual folks truly throw at AI.
To see the place Gemini 3.5 Flash actually stands, I gave it 5 prompts designed to check very totally different strengths. Some have been sensible. Some have been intentionally ridiculous. All of them highlighted capabilities Google has emphasised as a part of Gemini 3.5 Flash’s evolution past earlier Flash fashions and Gemini 3.1.
1. Simulating area
For the primary check, I wished to push multimodal reasoning, lengthy context understanding, and technical code technology unexpectedly. Gemini 3.5 Flash is meant to deal with advanced info whereas shifting fluidly into sensible execution, so I handed it a dense aerospace report about area particles and requested it to make a pleasant simulation. Particularly, I wrote, “Use the hooked up IADC Standing of the House Particles Surroundings Report back to make an interactive simulator exhibiting how particles and orbiting visitors will construct up and the potential hazard to things in orbit consequently.”
Gemini wrote an extended, sophisticated little bit of code utilizing information pulled from the report. When translated, the code turned the impressively visible simulation you possibly can see within the video above. It constructed the interface idea round storytelling quite than uncooked numbers.
Probably the most spectacular half was how clearly it articulated the why behind its decisions. “The dashboard ought to assist customers perceive not merely that particles will increase over time, however how launch conduct and mitigation choices affect long-term outcomes,” Gemini wrote.
2. Weekend planner
I typically use journey planning as an AI check as a result of it may well exhibit each the facility and flaws in how an AI processes advanced variables. Gemini 3.5 Flash emphasizes agentic planning and multi-step reasoning, so the following problem aimed squarely at seeing the way it handled a whole lot of additional particulars.
I advised it to “Plan a 4-day highway journey by means of the Hudson Valley and Catskills. Create a complete, multi-step itinerary that coordinates morning climbing trails, mid-day artisanal meals stops, and scenic driving routes, full with a built-in ‘wet day backup possibility’ for every afternoon.”
Gemini 3.5 Flash approached the task with stunning restraint. Day one eased into river views and climbing with out exhausting the traveler instantly. Scenic routes linked naturally quite than zigzagging unpredictably throughout the map. Meals suggestions aligned geographically fairly nicely, and the climate contingencies made a whole lot of sense, as Gemini identified:
“Rain options ought to protect the emotional purpose of the unique exercise. A climbing afternoon changed by looking unrelated retail areas creates disruption quite than continuity.”
3. Bookbinding logic
Subsequent got here procedural reasoning, the sort of structured planning that Gemini 3.5 Flash is meant to be nice at. Fascinated about a venture I bear in mind, I requested Gemini to “act as an knowledgeable ebook conservator and supply a strict, step-by-step newbie information for case-binding a customized journal at dwelling.”
Craft directions expose weaknesses rapidly. Too obscure, and newbies fail instantly. Too technical, and folks give up midway by means of whereas staring angrily on the glue. Gemini 3.5 Flash discovered a center floor, setting expectations and separating important steps from elective refinements. It accounted for probably errors with out sounding patronizing.
“Your purpose just isn’t museum conservation high quality however making a sturdy journal whereas studying foundational binding ideas,” it stated. “Drying time is a part of the method quite than lifeless time between steps.”
4. Fast clear
The subsequent check focused Gemini 3.5 Flash’s visible reasoning enhancements and claims of higher planning for actions. I gave it an image of a room in my home in want of organizing and cleansing, and advised it to “create a 25-minute cleanup plan, inform me what to do first, what to disregard, and the right way to make the room look 80% higher with minimal effort.”
Cleansing recommendation sounds trivial till you understand most individuals fail cleanup makes an attempt for strategic causes quite than motivational ones. Older AI techniques typically suggest tackling the whole lot equally, which does not assist issues. Gemini 3.5 Flash understood triage. It stated it might prioritize visible influence and momentum.
“Focus first on high-visibility litter quite than hidden group issues,” Gemini suggested. “Seen progress creates momentum whereas enhancing perceived cleanliness quickly. Keep away from opening drawers or starting deep group duties throughout quick cleanup classes.”
5. Secret penguins
For the ultimate check, I wished to push Gemini 3.5 Flash’s parallel reasoning, the place it breaks a bigger drawback into smaller items and tackles a number of strains of pondering concurrently quite than fixing the whole lot one step at a time.
Only for amusement’s sake, I arrange a intentionally ridiculous task designed to reward structured investigation. I advised Gemini to “run a deep, background-agent test on a potential roommate who claims to be a ‘common human man’ however is clearly three penguins stacked inside a trench coat.”
The response leaned into the joke and carried out the mission by splitting the duty into parallel investigative tracks and labeling them like an actual intelligence operation. One sub-agent dealt with behavioral evaluation. One other centered on environmental proof. A 3rd examined social consistency indicators. Gemini tracked every stream independently whereas periodically merging findings into an evolving evaluation abstract.
“Sub-Agent 1: Mobility Evaluation: Noticed indicators embrace uncommon stability shifts, synchronized decrease physique motion, and elevated likelihood of a number of organisms coordinating locomotion.”
One other part learn, “Sub-Agent 3: Social Sample Evaluation. Declare of ‘common human man’ stays unverified. Further proof requested concerning fish buying frequency, unexplained ice accumulation, and suspicious resistance towards heat climates.”
Gemini stored the joke going and confirmed how parallel agentic reasoning modifications the form of AI problem-solving. Earlier techniques typically dealt with sophisticated prompts by pondering by means of them sequentially, which might make massive requests really feel slower or much less organized. Gemini 3.5 Flash as an alternative approached the pretend investigation like a number of specialists collaborating directly.
Gemini 3.5 Flash persistently demonstrated the way it might keep oriented on its duties, one thing earlier quick fashions often struggled with. No matter whether or not it was analyzing orbital particles tendencies, planning highway journeys, or investigating suspicious penguins, it maintained context whereas adapting its reasoning type appropriately to the task.
The larger story could also be how naturally its strengths meld right into a single mannequin. That shift modifications what Gemini 3.5 Flash can change into in on a regular basis life, a minimum of if individuals are okay with the trade-offs like needing to offer it numerous entry to their info to get probably the most out of it.
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