Meet SIMA 2: The AI Game-Playing Agent That Can Think Like a Human

Google announced Gemini 3.6 Flash on July 21, 2026, with sharp benchmark gains over 3.5 Flash: DeepSWE rose from 37% to 49%, MLE Bench from 49.7% to 63.9%, and OSWorld Verified reached 83%. Yet 4AIVN's hands-on experience tells a very different story. The model handles small jobs reasonably well, but a multi-step plan can make it forget the objective, skip steps, and drift halfway through the work. Stronger benchmarks do not reflect real-world use According to Google's official announcement, Gemini 3.6 Flash uses 17% fewer output tokens than 3.5 Flash on the Artificial Analysis Index, while tests such as DeepSWE show token reductions of up to 65%. Its input window reaches 1,048,576 tokens and its output limit is 65,536 tokens, impressive numbers on paper. The problem is that these figures come from designed tests with a fixed objective and a relatively contained run. That is not how a real plan operates. Production work changes continuously in response to feedback rather than ending after one self-contained attempt. Following a long plan is the critical weakness In hands-on use, Gemini 3.6 Flash performs poorly as soon as it moves beyond a single task. Give it a small job with explicit checks and it can work well with few unnecessary loops. Give it a multi-step plan and it may forget the original objective, skip previously agreed steps, or drift after several turns. When corrected, it sometimes apologizes and then repeats the same mistake instead of actually fixing it. A one million token window describes input capacity, not memory quality. The model may be able to “see” the full context and still miss details during execution; one overlooked constraint can push the entire plan off course. This is not a rare random failure but a repeated weakness that is difficult to ignore. Gemini 3.6 Flash is strong at completing one job quickly, but it is not yet dependable at completing a sequence of jobs correctly. That is the gap the benchmarks do not measure. A 17% price cut may not match the quality Gemini 3.6 Flash is priced at $1.50 per million input tokens and $7.50 per million output tokens, about 17% below the $9 output price of 3.5 Flash. On the surface, this is a sensible improvement: lower cost and higher benchmark scores. But if long tasks are executed poorly, the savings can quickly disappear through repeated reminders, corrections, and complete reruns of the plan. Gemini 3.5 Flash Lite is cheaper still at $0.30 per million input tokens and $2.50 per million output tokens, but it targets simple classification and data transformation workloads that do not require the model to preserve a long plan. What do you gain and lose with Gemini 3.6 Flash? Objectively, this is not a failed upgrade. Google has likely made careful tradeoffs among output quality, speed, and cost, even if real-world behavior does not fully meet the high expectations attached to its engineering team. The improvements are real rather than purely theoretical: responses are faster, output costs are lower, and the model is efficient on short, narrow tasks such as content classification, writing one code function, or answering a specific question. In those cases, it keeps unnecessary loops to a minimum. The cost becomes visible when work extends beyond a few steps. The more constraints and earlier decisions the model must preserve, the more likely it is to drift. For coding agents or long workflows already running reliably on Claude Fable 5 or GPT 5.6, there is not yet a convincing reason to switch to Gemini 3.6 Flash solely because of benchmarks or lower pricing. Gemini 3.5 Pro is still the model to wait for Google says Gemini 3.5 Pro is still being tested with partners and will be released broadly when it is ready. The central story of this launch is therefore the sizeable gap between benchmarks and real work. Anyone looking for a dependable agent for long-running workflows may still need to wait and see whether 3.5 Pro delivers a genuine step forward. If future releases remain underwhelming in practice, Google risks surrendering its advantage to competitors including Anthropic, OpenAI, and Meta.

Gemini has won big in the most literal sense, right as Messi scored his first hat-trick at the 2026 World Cup, leading Argentina to a crushing 3-0 victory over Algeria and equaling Miroslav Klose's record of 16 World Cup goals. That historic moment became the perfect launchpad for Gemini. Back in March 2026, Google and the Argentine Football Association (AFA) made a bold decision: rather than simply printing a logo on training kits, they signed a deal for the AI to actively support tactical preparation and professional decision-making. That bet has now proven to be the right call. From training kit to the tactical meeting room The agreement between AFA and Google was unveiled at Times Square, New York, a venue deliberately chosen to capture global media attention. The Gemini logo appears across all training apparel for Argentina's men's, women's and youth squads, sitting alongside Adidas and American Express in AFA's top sponsorship tier. But the interesting part isn't the jersey. According to Inside World Football, Argentina's coaching staff will use Gemini for three specific purposes: tactical analysis, injury prevention and decision support. In other words, Gemini now has a seat in meetings that previously belonged only to Scaloni and his assistants. Google has not publicly disclosed which specific Gemini tools have been integrated into AFA's workflow. What is clear is that they are using the World Cup to bring Gemini into the reality of professional football, and the results will be graded in public. What is Gemini actually doing in the dressing room? Argentina arrives at the 2026 World Cup as the reigning champion. Every decision Scaloni makes, from the squad list to the starting eleven, is scrutinized more closely than any other team, and that is precisely why Argentina has become the most ideal testing ground Google has ever had for Gemini in professional football, especially at a major tournament. Tactical analysis Gemini is used to process match data for both Argentina and their opponents, covering movement statistics, attacking patterns and defensive vulnerabilities. Instead of the coaching staff spending hours reviewing footage, AI synthesizes the data and generates tactical diagrams automatically, saving significant preparation time before each match. Injury prevention This is a problem every major team wants to solve, especially when Messi and several key players are at an age that requires careful management of training loads. Gemini analyzes biometric data and injury history to issue early warnings, helping the coaching staff adjust intensity before problems actually occur. That is part of the reason why, immediately after completing his hat-trick, Scaloni chose to substitute Messi off, prioritizing fitness and safety for the matches ahead. AI in injury prevention is nothing new. Premier League clubs have had Microsoft as a partner for similar purposes. What is different this time is that Gemini is integrated directly into the workflow of a national team competing at a major tournament, not just at club level. For fans: create Messi content, follow scores without unlocking your screen Alongside supporting the coaching staff, Gemini has also rolled out a range of features aimed at fans, and this is the side that hundreds of millions of people will actually experience. Gemini lets you create content about players directly Users can generate images, songs and digital content featuring Argentina players like Messi directly inside the Gemini app. The feature is designed to bring the World Cup experience closer to those who cannot attend matches in person. Real-time scores and automated daily briefings On Google Search, live match scores can be pinned to the lock screen and update in real time, with dedicated animations for goals and red cards, all without needing to unlock the phone. For paid Gemini users, the Scheduled Actions feature allows an automated daily football briefing to be set up, covering scores, news and fixtures, delivered at a chosen time without needing to prompt it each day. Match-day infrastructure Google has updated Street View at all 16 host stadiums and optimized routing on Waze for match days. Waze also surfaces live scores when the car is stopped at red lights, so drivers do not need to pick up their phones while on the move. The 2026 World Cup is the real test for AI in sport Google is not sponsoring Argentina alone. Gemini also appears on the kits of France, Morocco, Iraq, Turkey and the United States, while Pixel is the official phone of the French squad, which is also using Gemini for internal communications. This is clearly a comprehensive strategy from Google, not a one-off deal. What makes the 2026 World Cup particularly significant is that it will answer a question no lab environment can: what do users actually do with AI when a World Cup runs for six weeks across 104 matches? Features that run on initial novelty will fade after the group stage. Whatever users keep coming back to all the way through the final is the honest answer to where AI actually fits in everyday life, and Google knows it. Google's communications director for Latin America, Flor Sabatini, stated that the 2026 World Cup will mark a before and after in the history of football because of AI. It sounds like marketing, but the reality is that this is the first time a major AI model has been integrated into the preparation of the reigning world champions, right in the middle of the most-watched sporting event on the planet. The 2026 World Cup is Gemini's real test The most significant part of this entire story is not the Gemini logo on Messi's jersey. It is the fact that Argentina, still the most expected to win and the most scrutinized team, carrying the pressure of defending the title, has committed part of its preparation process to AI. If Argentina succeeds, Gemini will have a case study that no advertising budget can buy. If Argentina falls short and the coaching staff attributes any part of it to AI, the narrative will flip entirely. Either way, this is the first time AI has been held accountable on a stage that genuinely matters, not a benchmark, not a demo, but the World Cup. For AI users, what is worth watching is not just whether Argentina wins, but whether Gemini actually changes how a football team operates, or whether it turns out to be nothing more than a logo on a training kit that looks better than previous years.

Artificial intelligence (AI) is fundamentally changing how people build applications. You no longer need to be a professional developer. With a smart AI assistant, you can turn any idea into a real product. Google AI Studio is the clearest proof of that shift. The platform lets anyone, even without coding knowledge, build their own app. With the latest update, creating an AI app is as simple as having a natural conversation: describe your idea in plain language, and let AI handle the rest. Google AI Studio: Build AI apps without code and create Android apps with ease Google AI Studio is a browser-based development environment designed to simplify prototyping and building applications on top of Google's powerful AI models. Notably, the platform now supports direct creation of complete Android applications, opening the door for anyone who wants to ship a mobile product without writing a single line of code. If Gemini was once described as the "brain" of an application, Google AI Studio now gives it "hands and feet" through direct connections to APIs and SDKs within Google's ecosystem (via the "Supercharge your apps with AI" section). This makes expanding functionality incredibly easy, and you can make your app behave exactly as intended without manually configuring APIs or SDKs from scratch. Third-party APIs and SDKs still require manual input, but Google's vast ecosystem including Nano Bananas, Veo 3, Text-to-Speech, Google Search, and especially Google Maps covers nearly every common need out of the box. Through personal testing, Google Maps works reliably for mini apps in Vietnam, such as navigation tools or real-time traffic viewers. When pulling data from Google Search, the quality of results is impressive enough to eliminate the need for third-party scraping tools entirely. Another major advantage: Google AI Studio is currently completely free to use. The free credits Google provides are generous enough to comfortably explore Gemini 3, Nano Banana Pro, Veo 3.1, and many other tools for personal use without spending a thing. Step-by-step guide to creating a mini AI app Building an app in Google AI Studio is straightforward. Just follow these steps: Step 1: Access and set up Visit: Go to the Google AI Studio tool page. Sign in: Log in with your Google account. Start building: Open the "Build" tab. Under the Start tab, you can choose an AI model (default is Gemini 3.5 Flash) and select a programming language: React, Angular, or Android. If you skip this, AI defaults to React. Step 2: Come up with an app idea If you don't have a specific idea yet, browse the App Gallery to see sample apps built by Google and the community. It's the fastest way to find inspiration and understand what's possible. If you want something even more hands-off, just click the I'm feeling lucky button in the Start tab. Google AI Studio will instantly suggest interesting ideas, complete with example API and SDK integrations (under the Supercharge your apps with AI section) and the prompts AI uses to build them. It saves time and teaches you how AI thinks when creating apps. If you already have a clear idea, move straight on to the next step. Step 3: Write a specific prompt If you don't have a detailed prompt covering all the functionality, language, and interface requirements like the samples in the I'm feeling lucky button, that's completely fine. You can create an app with just a single sentence, for example: "Create a photo collage app for me." From there, AI will automatically make all the decisions and carry out the remaining steps for you. That said, the more detail you provide, the closer the result will be to your vision, which means less time editing afterward. If possible, include reference images or mockups from tools like Figma or Canva, since AI can understand and recreate interfaces almost exactly from those references. Don't forget to add extras in the Supercharge your apps with AI section to let AI automatically connect the APIs or SDKs you need, or even enable intelligent reasoning mode for your app. Here's an example of a detailed prompt you can reference: "Create an AI Web App that allows users to: Upload 2 images (1 & 2) so the app combines them into 1 composite image. Support multiple aspect ratios: 1:1, 16:9, 4:3, 3:2. Include image preview and a Download button. Save creation history (including result image, prompt, and timestamp)." Once your prompt is ready, just click Build and wait a few seconds to see the result. Step 4: AI automatically handles the build Build process: AI Studio runs through several stages, including: Defining the UI Scope. Developing the React App. Planning the app structure. Integrating Gemini API. Auto fix errors. Preview and edit via conversation: A live preview of your mini app appears directly in the browser, so you can see it in action right away. Developers can edit the code directly in the code panel. But if you're not technical, that's no problem at all. Just chat with AI to add, remove, or adjust features without touching a single line of code. For example, you could say: "Add images 3 and 4 so I can merge four photos into one" or "Switch the interface to dark mode." If you didn't add APIs or SDKs in the "Supercharge your apps with AI" section earlier, don't worry. With a simple prompt, AI will automatically integrate the necessary APIs or SDKs into your mini app quickly and with minimal effort. You can even request advanced features like: Generate video from images using Veo 3, and the app will automatically connect to the Veo API. Add a speech-to-text button to make the app more interactive. And the most exciting part: you can edit your app visually, just like working in Canva or Figma, using the Annotate app button where you can draw, add text, change colors, and more, all in the most intuitive way possible. Step 5: Test and deploy Action How to do it Test in browser Click the "Run" button or view the live preview. Share app via link Click "Share" and copy the link. Download source code Click "Download" (ZIP file containing React + TypeScript code). Deploy to cloud Click "Deploy" and select Google Cloud Run (requires a Google Cloud account). Can you build a complete app with Google AI Studio? For personal use or quick idea testing, Google AI Studio is an excellent choice: easy to use and nearly zero cost. However, if you want to build a full-stack application with a proper backend, UX, and UI without any coding knowledge, you'll want to consider more suitable platforms. Comparison with Google Antigravity IDE While Google Antigravity is an IDE focused on helping professional developers write code faster through asynchronous background agents, Google AI Studio targets non-technical users in the no-code/low-code space. With AI Studio, there's no software to install and no environment to configure. Everything happens through natural language descriptions right in the browser. Antigravity, on the other hand, offers deeper control over source code, multi-model support (Claude, GPT), and is better suited for complex projects that require refactoring an existing codebase. Goal Recommended tool Personal use, rapid prototyping, idea testing Google AI Studio Commercial app development, full-stack products, scalability needs Google Firebase, Lovable, Bolt, Replit, Antigravity Google AI Studio is not the optimal choice for large-scale products or applications requiring high security. Instead, you can download the source code from AI Studio and upload it, or sync it directly via GitHub, to continue building on platforms like Firebase Studio (within the Google ecosystem), Lovable, Replit, Bolt, or Antigravity. These platforms help you complete your app with powerful backend features while still leveraging the AI foundation built in Google AI Studio.

Google isn't just adding a new model to Flow. At Google I/O 2026, the company is turning Flow into an agentic AI creative studio — complete with custom tools, conversational video editing, and a mobile app. For video creators, the signal is clear: the race is no longer about generating a beautiful clip from a single prompt, but about the ability to edit, iterate, and refine ideas like a real production pipeline. Gemini Omni turns Flow into a conversational video editing studio According to Google's announcement on May 19, 2026, Flow has been upgraded with Gemini Omni, with Omni Flash being the first model introduced to the experience. Google describes Omni Flash as a model capable of generating content from multiple input types — starting with video — while combining Gemini's intelligence with Google's generative media models. The simplest way to understand it: think of Omni Flash as the video equivalent of what Nano Banana did for images. If Nano Banana made photo editing feel more natural and conversational, Omni Flash brings that same approach to video — where users can pull from real-world inspiration, existing footage, and iterative prompts to keep refining their work. Critically, Google says Omni Flash improves character consistency, meaning identity and voice can be preserved across multiple scenes. Flow Agent and Tools bring AI into the entire creative workflow The second major upgrade is Google Flow Agent. Rather than simply accepting a prompt and returning a result, this agent is designed as a creative collaborator capable of planning, reasoning through complex tasks, and supporting users at multiple stages of the process. Google gives examples like the agent suggesting dialogue for a specific scene or proposing story development directions. As a project deepens, Flow Agent can generate multiple variations simultaneously to give users more options, and supports batch editing so changes are applied across many assets at once. Once enough material is gathered, the agent can also organize assets into collections and rename them in more intuitive ways. This feature is now available to all Flow users globally. The more interesting part is Google Flow Tools, where users can build their own tools and workflows using natural language. If you want a custom image preset, a video resize tool, or a personalized shader, Flow Tools lets you describe what you need rather than writing code. In other words, the vibe coding concept is moving into the content creation environment — not just sitting inside a developer's IDE. All Flow users globally can access pre-built Tools Google AI users can create and remix their own Tools Custom tools can be shared for others to remix Flow Music also gets meaningful upgrades for music creators Google Flow Music received a set of new features as well, with the most significant being the ability to edit songs at the section level. Users can select a specific portion of a track to rewrite lyrics, translate them, change the beat drop, or sample a passage and develop it in a different direction — all without affecting the rest of the track. The covers feature lets users transform the style of an entire song while preserving its original melody and structure. For example, a track could be shifted into a lo-fi study aesthetic for a study playlist or background content. For creators who are newer to AI music tools, this approach is far more accessible than having to regenerate from scratch every time they want to change the sonic character of a piece. Gemini Omni also appears in Flow Music to support music video creation. Users can work conversationally with the agent, directing style, subjects, and shots to match the story and rhythm of the underlying track. This feature is available to Google AI users, and it signals Google's intent to connect three layers of creative work: audio, visuals, and narrative. A mobile app takes Flow beyond the desktop Google also announced mobile apps for both Flow and Flow Music. The web version remains the most capable environment, but the mobile app lets users capture ideas, run quick tests, or make fast edits when they're away from their computers. Conclusion The biggest takeaway from this round of upgrades isn't any single feature. Google is connecting Gemini Omni, Flow Agent, Tools, and Flow Music into a more complete end-to-end workflow — from ideation and asset creation, through batch editing and resource organization, to publishing both music and video content. If you work with video, music, or short-form content, the most practical starting point is to bring in a real asset of your own and see how well Omni Flash holds character consistency, voice, and editing continuity across multiple rounds. If it handles that reliably, Flow will no longer be just an AI video generation tool — it becomes a content production environment worth watching closely through the rest of 2026.