Vietnam AI 2025: Nearly 80% User Adoption, ChatGPT Still Leads the Market

More than 300 million people ask ChatGPT health-related questions every week, from decoding lab results to preparing for a doctor's appointment. The catch is that over 70% of those conversations happened outside the dedicated Health space OpenAI built for exactly that purpose. That's why OpenAI just expanded Health in ChatGPT to all eligible users in the US, letting people connect Apple Health and medical records so the AI can draw on personal data in any conversation, not just inside a separate tab. ChatGPT Health isn't a brand-new feature OpenAI first introduced ChatGPT Health on January 7, 2026, as a limited, waitlist-based pilot for a small group of users. At that stage, health conversations had to happen inside a dedicated Health space, and the friction of switching tabs was enough that most users kept asking health questions in the regular chat window instead of opening Health. The rollout on July 23 is actually a full-scale expansion, not a first launch. OpenAI brought Health to all eligible US users across the Free, Go, Plus, and Pro plans, and dropped the separate-space requirement entirely: once permission is granted, ChatGPT can use connected health data anywhere in the app, even when a user is simply asking about a meal plan or a workout schedule. What can ChatGPT Health actually do? Users can connect Apple Health along with medical records from supported hospital systems, One Medical, or Function Health. With permission, ChatGPT can use that information to compare new lab results with previous ones, summarize what's changed since the last visit, or spot connections between sleep, activity, and daily habits. The goal is to cut down on how often users have to re-collect, re-upload, and re-explain the same information every time they talk to the AI. How does health data actually enter a conversation? Health remains the place where users connect and manage their data, view recent trends, browse synced records, and return to past health conversations. But unlike the original pilot, once data is synced, relevant information can now be used in regular conversations if the user allows it, instead of being confined to a separate space. Typing @Health into a message is also a way to explicitly pull health context into a response. What data can ChatGPT use? That data can include current medications, lab results, recent visits, sleep, activity levels, and workouts. If a wearable or nutrition app already feeds into Apple Health, ChatGPT can use whatever gets passed through once permission is granted, though OpenAI notes that some proprietary third-party metrics may not carry over. Users still decide when to grant access By default, ChatGPT asks for permission before using medical records or Apple Health to personalize a response. Users can allow access once, always allow it, or change that setting later, and can disconnect at any time under Health > Accounts. Privacy is the biggest selling point, but it isn't absolute According to OpenAI's official announcement, connected medical records, Apple Health data, and conversations that use them are not used to train foundation models or target ads, regardless of a user's general training settings. Connected data gets additional layers of encryption on top of standard encryption at rest and in transit. When a data source is disconnected, synced information from that source is deleted from OpenAI's systems within 30 days, though anything already in a conversation history stays until the user deletes that conversation. What gets less attention is that once health data leaves a hospital or clinic's system and enters ChatGPT, it's no longer covered by HIPAA, the US medical privacy law. Every privacy commitment and no-training promise now rests on OpenAI's voluntary terms of service, not the legal obligations that apply to health records inside a traditional hospital system. Health data can be missing or outdated, such as a medication still listed after a patient has stopped taking it. Verify anything important against the original source and a healthcare professional, and think carefully before connecting genuinely sensitive information. GPT-5.6 Sol handles the harder health questions OpenAI says GPT-5.5 Instant brings health-question capability to free users, while GPT-5.6 Sol is the company's strongest option for questions that require reasoning across multiple details, reserved for paid users. The scenarios OpenAI highlights include explaining visit notes in plain language, tracking how lab results change over time, and preparing questions for a follow-up appointment. OpenAI worked with more than 260 physicians across 60 countries to build scenarios and scoring criteria, with over 600,000 evaluations of model outputs across 30 health domains. The criteria include accuracy, safety, communication, context awareness, completeness, and knowing when to escalate to professional care. Even so, the company still warns that ChatGPT can produce inaccurate information, a weakness that remains common across AI models in fields that demand near-perfect precision. ChatGPT Health is useful, but it's not a replacement for a doctor Health's clearest benefit is pulling together data that's normally scattered across patient portals, apps, and wearables into context the AI can actually use. That can help users understand their own health history, prepare better for appointments, and have clearer conversations with their doctor. The stakes are also higher than an ordinary conversation, since the answers touch directly on sensitive data and health decisions. Users shouldn't change medications on their own, delay emergency care, or make treatment decisions based solely on an AI's response, and should still follow guidance from an actual doctor. What should users outside the US make of this? Both rollouts of ChatGPT Health, from January's pilot to July's expansion, remain limited to the US. Medical record integration has been US-only from the start, while the EU, UK, and Switzerland were excluded from both phases due to stricter data protection rules and the possibility that this kind of feature would be classified as high-risk under the EU AI Act. OpenAI hasn't announced any timeline for expanding beyond the US, including into Asian markets. For ChatGPT users in regions where Health isn't available yet, a few things are worth keeping in mind. First, not being able to connect medical records doesn't mean you can't ask ChatGPT about health at all, it just means the answer will rely on what you describe yourself rather than automatically synced data. Second, even without the feature, it's worth being cautious about pasting raw lab results or medical records into a regular chat, since the level of data protection differs from the additional encryption used inside the Health space. Finally, how valuable Health becomes in other markets will depend on how many local healthcare systems support the integration, since US hospital record formats don't map directly onto other countries' healthcare infrastructure. This is a notable step in personalizing ChatGPT, but whether it actually works out will come down to data quality, how much control users retain, and whether the AI knows when to step back and hand things off to a medical professional instead of drawing its own conclusions.

Sol, Terra, and Luna make GPT-5.6 look more like a product family than a single model. The naming also signals what OpenAI is trying to change: users no longer have to choose only between an expensive flagship and a much smaller model. Instead, they get three capability tiers designed for different workloads. The important caveat is that GPT-5.6 is currently in limited preview, and OpenAI says it is not available in ChatGPT during this preview period.On the other side, Anthropic positions Claude Fable 5 as a frontier model for reasoning, software engineering, scientific research, and long horizon agentic work. The useful question is therefore not simply which model is smarter. It is which product architecture helps a team complete real work with predictable quality, latency, and cost.What GPT-5.6 actually isAccording to OpenAI's preview announcement, GPT-5.6 consists of Sol, Terra, and Luna. Sol is the flagship and most capable option, Terra is a strong lower cost model, and Luna is the fastest and most cost efficient member of the family.The important change is how OpenAI divides demand into three tiers. A research team might use Sol for a difficult reasoning problem, a product team might run most daily work on Terra, and a high volume system might use Luna for thousands of short requests. This looks more like an infrastructure strategy than the launch of a single new chatbot.Availability matters: OpenAI says GPT-5.6 is not available in ChatGPT during the preview. An experience in an API, developer tool, or partner platform should not be treated as the final ChatGPT experience.Sol is designed for difficult, extended workSol is positioned as the strongest GPT-5.6 model for deep reasoning, complex coding, and long multi step tasks. A software team might ask it to understand a repository, identify the cause of a bug, propose a minimal patch, and write regression tests. Sol's value is not answering a short question quickly. It is maintaining the objective while working through a longer chain of decisions.OpenAI also highlights stronger cyber capability as reasoning increases. That can be useful for authorized security testing and vulnerability analysis, but it also makes access controls, logging, sandboxing, and human approval more important.Terra aims for the practical middleTerra targets the broadest category of work: document analysis, content production, application development, research synthesis, and operational support. If Sol is the specialist called for the hardest problem, Terra is the strong team member expected to work throughout the day without making every request unnecessarily expensive.A marketing team could use Terra to read market reports, extract insights, build an outline, and draft several content variants. A development team could use it for code review, test generation, and tickets with a clear scope. This tier could become the default if its real world quality remains consistent.Luna prioritizes speed and scaleLuna is designed for low latency and lower cost. Classification, conversation summaries, field extraction, drafting, and ticket routing do not always require the strongest model. In these cases, response time and total operating cost matter more than maximum reasoning capability.Fast does not mean suitable for everything. If a task requires source verification, a long plan, or a code change with a large blast radius, a team should move it to Terra or Sol instead of forcing Luna beyond its intended role.Claude Fable 5 takes a different routeAnthropic presents Claude Fable 5 as a frontier model for reasoning, software engineering, vision, scientific research, and long horizon agentic work. Instead of emphasizing three product tiers in one generation, Anthropic's message focuses on the capability of a powerful model working inside the Claude ecosystem.This difference changes deployment decisions. With GPT-5.6, an engineering team might build a router that sends each request to Sol, Terra, or Luna. With Fable 5, the focus may be on optimizing prompts, tools, context, and reasoning budgets around one primary model. Neither approach is universally better because the answer depends on workload and operational maturity.A fair comparison: Do not run one prompt and declare a winner. Build a test set covering short tasks, long reasoning, coding, extraction, and recovery from errors. Measure accuracy, latency, the number of human corrections, and the total cost of a completed task.Coding and agentic work depend on the surrounding toolsBoth GPT-5.6 Sol and Claude Fable 5 target complex software work, but the practical experience depends heavily on the system around the model. The ability to read a repository, execute commands, observe results, and correct mistakes can matter as much as a benchmark score. For OpenAI workflows, the Codex page is a useful starting point for understanding how a model participates in coding work.Fable 5 may be attractive to teams already invested in Claude and long running agentic workflows. Read our Claude Fable 5 coverage for more context on Anthropic's positioning and the types of work it targets.What early forum experience tells usEarly discussions on Reddit and developer communities focus on how different Sol, Terra, and Luna feel in real work. Some users describe Sol as the better fit for multi step tasks, Terra as the practical option for routine work, and Luna as the interesting choice for speed. These observations match OpenAI's positioning, but they do not establish a precise quality gap.Forum reports are useful because they reveal the questions real users care about. However, they are self selected evidence. People may use different prompts, access levels, integrations, and preview versions. A result from a developer platform does not guarantee the same result when a model eventually appears in ChatGPT.Early positivesThe three tiers make it easier to understand which model belongs to which workload.Luna creates a clear expectation of low latency for high volume systems.Terra could become a default if it delivers stable quality at a practical cost.Sol is expected to be stronger for coding, long reasoning, and tasks with several verification steps.Open questionsHow large the practical quality gap between Sol and Terra will be on common workloads.The total cost after retries, corrections, and human review are included.How Luna behaves with long prompts and many constraints.Whether performance remains stable as GPT-5.6 expands beyond preview access.Forum reports are not benchmarks: Community experience should help you choose test cases, not make a production purchasing decision by itself.Comparing GPT-5.6 and Fable 5 by workloadWriting and document analysisTerra appears positioned for most document work because it balances capability and cost. Fable 5 may be attractive when documents are long, questions are complex, and the model must maintain an argument across a large context. A useful evaluation should score citation accuracy, structural consistency, and how much editing is required before publication.Software development and debuggingSol and Fable 5 are both candidates for difficult coding tasks. A representative test should include reading existing code, identifying the root cause, producing a minimal fix, writing tests, and explaining risk. Asking a model to create an isolated function from scratch does not reflect how well it works in a real repository.High volume processingLuna has the clearest positioning advantage when speed and cost dominate. At thousands of extraction or classification requests per day, a small difference in price and latency can have a large effect. Fable 5 may be unnecessarily expensive for a workload that only needs short, structured outputs.Research and long reasoningSol and Fable 5 should be compared with tasks that have verifiable outcomes rather than open questions that merely sound impressive. Give both models the same research material and ask them to identify assumptions, detect contradictions, propose an experiment, and explain what evidence is missing. The better model is the one that helps users discover errors faster, not the one that writes the longest answer.Should you choose Sol, Terra, Luna, or Fable 5?If you want maximum capability inside the OpenAI ecosystem, Sol is the first model to test. If you need a strong model for regular use, Terra has the more practical position. If your workload contains many short and repetitive tasks, Luna could reduce operating cost. Fable 5 remains relevant for teams invested in Claude or focused on long reasoning and agentic work.Because GPT-5.6 is still in preview, replacing an entire production workload would be premature. Run the models in parallel on real but sanitized data, record failures, and use the same criteria for every candidate.A test plan you can use nowSelect 20 tasks that represent real work, including easy and difficult cases.Run each task on Sol, Terra, Luna, and Fable 5 when access allows.Score accuracy, response time, total cost, and required human correction.Track severe failures separately instead of relying only on averages.Choose a model for each workload category rather than forcing one model to do everything.Is GPT-5.6 worth switching to now?The most important change in GPT-5.6 may not be Sol's raw capability. It is OpenAI's decision to turn one model generation into three operational tiers. That could help organizations control cost, but only if they can classify workloads and route requests intelligently.The practical next step is to build a small benchmark from your own data. If Sol wins difficult tasks, Terra is good enough for routine work, and Luna handles high volume requests reliably, the three tier architecture has real value. If Fable 5 remains more consistent on long reasoning, a multi model strategy may still be better than committing to one provider.

Three million Codex users per week — up six times in just the first three months of 2026. That number tells you something: Codex is the rising star. OpenAI is turning it into an all-in-one tool, which means Codex is no longer just a playground for developers. What is Codex? A tool that's not just for developers Think about this scenario: you want to build a spreadsheet that automatically updates every week, or a small website to let customers book appointments, or simply a tool that summarizes your email reports each morning without opening dozens of tabs. Previously, these things required a developer. With Codex, you just type your request in plain English and wait for the result. Codex is OpenAI's AI agent, launched in May 2025 and deeply integrated into the ChatGPT ecosystem. Its core difference from regular ChatGPT is that Codex doesn't just answer — it actually does the work through a code execution environment. You assign a task, Codex plans it out, executes each step, checks the result, and returns a finished product ready to use. No need to understand what code is, no need to monitor every command. Codex can now run as a standalone desktop app available for both Windows and macOS, and has recently expanded to Android and iOS on mobile as well. You can sign in using your existing ChatGPT account. Codex is currently available on ChatGPT Plus, Pro, Business, and Enterprise plans, though Free and Go users also get limited trial access. What Codex can do for you Build apps or small websites from a description You don't need to know HTML or JavaScript. Just describe what you need: "Create a simple appointment booking page with fields for name, phone number, and date/time selection, and send an email notification whenever someone books." Codex will build the entire interface, handle the logic, and guide you through publishing it online. A startup team in the US once shared that they completed in one weekend what would have previously taken an entire quarter — and it wasn't a team full of developers. Automate repetitive tasks This is where non-developer users will find the most value. For example: every week you have to consolidate revenue data from three different Excel files, merge them, and send a report to your manager. Codex can build an automated workflow that does this for you on a schedule and delivers the result without you ever opening your laptop. With the Automations feature launched in the April 2026 update, Codex can take on long-horizon tasks, pause, resume, and complete them over multiple days without needing to be reminded. Generate images and prototypes directly in the app Codex integrates image generation powered by GPT Image 2.0 directly inside the app. You can ask Codex to create interface mockups, product banners, or illustration assets for a document — all within the same workflow, without switching to another tool. For content creators, marketers, and solo founders, this is a genuine advantage: the entire journey from idea to finished output can happen in a single window. Control your computer to work in the background Since April 2026, Codex can operate Mac applications using its own cursor, viewing the screen and clicking and typing to complete tasks while you continue using the machine normally. A simpler way to picture it: you're in an online meeting while Codex has Figma open, editing a design and saving the file according to instructions you set earlier. Two things happening in parallel, neither getting in the other's way. The computer use feature is currently only available on macOS and is not yet available in the EU or UK. You will need to grant Codex Accessibility and Screenshot permissions during initial setup. How to get started with Codex Codex requires installing the desktop app on Windows or macOS — it does not run directly in a web browser. The setup process is straightforward and takes just a few minutes. Step 1: Go to openai.com/codex and download the version for your operating system. On macOS, there are two separate builds for Apple Silicon (M1 and later) and Intel chips. On Windows, there is a single universal build. Step 2: Install the app and sign in with your existing ChatGPT account or OpenAI API key. Step 3: Choose a project folder you want Codex to work within — you can also link it to a github repository — or skip this step if you only want to assign standalone tasks like creating files, generating images, or automating a workflow. Step 4: Type your request in natural language, and be as specific as possible. Instead of "make me something about a report," try: "Create an Excel file summarizing monthly revenue from the data I provide, add a bar chart comparing each month, and highlight the month with the highest revenue." The more specific your request, the better the result. Codex works best when you clearly describe the input, the desired output, and any constraints you need — such as file format, display language, or calculation rules. Codex vs Claude Code, Antigravity, and Cursor from a non-technical user's perspective If you're not a developer, the real question isn't "which tool is technically more powerful" — it's "which tool can I use right now without learning anything new." From that angle, these four tools are clearly different from one another. Codex and Claude Code Claude Code from Anthropic is Codex's most direct and formidable competitor. In terms of raw technical output quality, Claude Code currently leads the pack — producing cleaner code, tighter logic, and handling large, complex codebases more effectively. However, Claude Code is explicitly designed for developers: it runs in the terminal, requires command-line installation, and notably has no image generation capability. If you're not comfortable in a terminal, Claude Code is a barrier from the very first step. Codex, by contrast, offers a more user-friendly desktop interface, integrates image generation within the same workflow, and is noticeably more accessible to non-technical users. Codex and Antigravity Both require a desktop app, but their underlying philosophies are completely different. Codex is built around a "hand off the task and wait for results" model: you describe what you need, the agent runs in an isolated cloud sandbox, and returns a finished product without affecting your machine at all. It suits people who want to automate workflows, create files, or build something without monitoring every step. Antigravity works in the opposite direction: the agent runs directly on your machine, watches your screen, opens applications, and collaborates with you in real time while you work. If you want an AI colleague working alongside you — observing and reacting to what's happening on your screen — Antigravity is the better fit. Codex and Cursor Cursor is built on VS Code and targets developers who want to keep their familiar working environment intact. For non-coders, Cursor is largely inaccessible because the entire experience revolves around editing code inside an editor. Cursor excels at understanding an entire codebase and offers flexibility in choosing AI models, but those advantages are for developers — not for general users who need to automate workflows or build something from scratch. In summary, from a non-technical user's perspective: Codex: Friendly desktop interface on Windows and macOS, capable of generating images, well-suited for users who want AI as an automated workflow tool. Claude Code: Best technical output quality, but developer-oriented and cannot generate images. Antigravity: Agent works directly on your machine in real time, suited for users who want to collaborate with AI while they work. Cursor: Best for developers keeping their VS Code workflow intact; not suited for general users. Who is Codex best for? If you're a content creator who wants to build a landing page for a campaign without hiring a developer, Codex fits. If you're a marketer who needs to automate weekly reports pulling from multiple data sources, Codex fits. If you're a solo founder who needs to ship a product fast without a technical team, Codex fits. If you're a teacher who wants to build a small quiz app for students without learning to code, Codex fits. On the other hand, if you're a developer who needs granular control over every line of code in a large, complex codebase, Claude Code will deliver better output quality. Codex is the right tool for people who want fast results without needing to understand what's happening under the hood. One practical limitation worth knowing: Codex currently has full support for Python, JavaScript, TypeScript, and Ruby. For tasks that don't involve code — like generating images, automating workflows, or creating documents — this language limitation has no impact on you. The line between "can code" and "can't code" is fading The question "do you know how to program?" is losing its weight as tools like Codex continue to evolve. What matters more now is whether you can describe clearly what you want — because that's exactly the thinking skill required to work effectively with Codex and similar AI agent tools. If you want to try it today, start with something small and specific: ask Codex to create an Excel file consolidating data you currently process manually every week. That's the fastest test to evaluate whether Codex genuinely saves you time or not.

In its Q4 2025 financial report, Alphabet (Google's parent company) announced a historic milestone: the Gemini artificial intelligence application officially surpassed 750 million monthly active users (MAU). This figure is not only a testament to Google's rapid development but also signals a comprehensive restructuring of the global AI market.Rapid Growth and Gemini Position in the AI MarketIn just a short period, Gemini has achieved astonishing breakthroughs. In October 2024, the application had only about 90 million users, but by March 2025, it reached 350 million, and currently stands at 750 million. Compared to Q3 2025 (which saw 650 million MAU), Gemini added 100 million users in just one quarter.Currently, Gemini is closely trailing its biggest competitor, ChatGPT (estimated to reach approximately 810 million users by the end of 2025), and has far surpassed Meta AI (currently recording nearly 500 million monthly users). Sources indicate that Gemini's web traffic market share has quadrupled in one year, from 5.7% to 21.5%, while ChatGPT's has decreased from 86% to approximately 64%.[CHART_1]What Is Driving Gemini GrowthGemini's success is not accidental; it is the result of a strategy of deep integration and continuous technological innovation:Power of Gemini 3: The launch of the Gemini 3 model is considered a significant milestone, bringing profound reasoning capabilities and superior multimodal understanding. CEO Sundar Pichai emphasized that Gemini 3 Pro has a daily token processing speed three times higher than its predecessor.Massive Google Ecosystem: Gemini's biggest advantage is its distribution capability. Gemini is directly integrated into over 3 billion Android devices, the Chrome browser (accounting for 65% of the web market share), Gmail, and Google Workspace. This allows users to access AI naturally in daily tasks without needing to download a separate application.Strategic Partnerships: Google has become Apple's preferred cloud provider for developing foundational models for Siri and integrating Gemini technology. Additionally, an agreement with Reliance Jio in India has provided 500 million customers with access to a free 18-month Gemini trial package.Cost Optimization: Alphabet reduced operational costs per Gemini unit by 78% in 2025 through model optimization and the use of specialized hardware such as the Ironwood TPU chip (7th generation).A Broader Commercial StrategyTo attract cost-sensitive users, Google has launched the Google AI Plus service package at a fee of just $7.99 per month. Concurrently, the enterprise segment has also seen remarkable success with over 8 million paid users for the Gemini Enterprise package, serving more than 2,800 large companies such as BNY and Virgin Voyages.A notable point is that Google is developing an "Import AI chats" feature, allowing users to transfer their entire chat history from ChatGPT or Claude to Gemini. This is considered a "boost" to entice users to migrate to Google's ecosystem without worrying about losing previously "trained" data.Google 2026 Vision: Major Investment in AI InfrastructureGiven the current growth momentum, Alphabet expects to spend between $175 billion and $185 billion on capital expenditures (CapEx) in 2026. This amount will primarily be allocated to technical infrastructure, including servers (accounting for 60%) and data centers along with network equipment (accounting for 40%).According to sources, Google's goal is to maintain continuous innovation amidst soaring demand for AI. However, CEO Sundar Pichai also warned about challenges related to computing capacity, energy supply, and land for building new data centers.ConclusionThe milestone of 750 million users for the Gemini application is not just a dry number, but an affirmation of Google's strong comeback in the AI race. By leveraging its existing ecosystem and continuously improving model performance, Gemini is gradually eroding ChatGPT's monopoly, creating a more competitive and diverse AI market for global consumers.