[DigitalToday reporter Yoonseo Lee (이윤서)] Google’s new artificial intelligence model, Gemini 3.5 Flash, has emerged as a practical AI model supporting coding, long-form reasoning, multimodal understanding and parallel task handling.

On May 21, local time, IT outlet TechRadar reported that Google unveiled various Gemini features at Google I/O 2026, but put Gemini 3.5 Flash forward as its flagship model focused on real-world utility.

This evaluation focused on real usage scenarios rather than announced specifications. The test used five prompts: building a simulator based on a “space debris report,” designing a travel itinerary, instructions for a handicraft task, an indoor organizing plan, and a humorous investigation based on parallel reasoning. The common thread was handling multiple requests at once rather than simple Q&A.

The first area examined was multimodal reasoning and code generation. Gemini 3.5 Flash read a space debris environmental report and produced simulator code that lets users change conditions and check outcomes. Rather than simply charting figures, it was designed to help users understand how long-term results change depending on satellite launch volumes or mitigation measures.

In drafting a travel itinerary, its ability to coordinate routes, time allocation and locations in sequence stood out. For a four-day plan crossing the Hudson Valley and mountain ranges, the model bundled suggestions including morning hikes, midday food-focused stops, scenery-oriented routes and rainy-day alternatives. It also said that “even if it rains, the trip’s original purpose should be maintained,” and pointed out that inserting unrelated activities undermines continuity. It built the plan by considering both routing and schedule flow even under requests with multiple conditions.

In procedural reasoning, its ability to break down complex tasks into step-by-step instructions at an accessible level was notable. While explaining how to bind a notebook at home, Gemini 3.5 Flash distinguished essential steps from optional reinforcement steps. It flagged possible mistakes in advance but avoided overly technical explanations. It explained that the goal was not museum-grade conservation binding, but making a sturdy notebook while learning the basics. Including drying time as part of the binding process also showed procedural design capability.

Changes also appeared in image-based reasoning. When shown a photo of a messy room and asked for a 25-minute cleaning plan, Gemini 3.5 Flash set priorities by clearing what stands out first rather than treating all problems equally. The model replied, “Start by organizing the items that are most visible, and if cleaning time is short, do not start organizing drawers.” It set a strategy to maximize perceived impact in a short period.

The final test was parallel reasoning. Parallel reasoning capability was confirmed even in a playful request. Gemini 3.5 Flash handled a prompt to “investigate the secret of a roommate who claims to be an ordinary person but actually has three penguins inside their coat” by splitting it into behavior analysis, environmental clues and a review of social consistency. After checking clues such as how the person moves, how often they buy fish and whether they avoid warm climates, it combined the results. A key feature was that it handled multiple hypotheses at once rather than only solving a complex request sequentially.

Across the test, Gemini 3.5 Flash’s strengths were in maintaining context and switching work modes rather than speed itself. It was assessed as not losing sight of a request’s goal while moving among different types of tasks such as space debris analysis, itinerary design, manual-work guidance and cleaning strategy. It also confirmed a trend in which coding, long-context handling, visual understanding and agent-like planning functions emphasized by Google are combined within a single model rather than existing as separate features.

It also raised the point that broader use may require greater user information access permissions. As Gemini 3.5 Flash handles a wider range of everyday tasks, the amount of personal and contextual information it needs to receive could increase. As a result, Gemini 3.5 Flash’s competitiveness is expected to hinge not only on performance but also on what level of information access and control is possible in real service environments.