Alibaba Qwen 3.5 is here, and it is making bold claims. The Chinese tech giant says its latest AI model cuts inference costs by 60 percent, runs workloads eight times faster than its predecessor, and can now take actions inside real apps on its own, without waiting for a human to tell it what to do next. That last part is what separates this release from every previous Qwen update.
This is not a chatbot improvement. It is Alibaba’s attempt to build an AI that works the way an employee works, not the way a search bar works.
What Alibaba Is Actually Saying About Qwen 3.5
Alibaba described Qwen 3.5 as being “built for the agentic AI era,” which in plain language means the model is designed to complete tasks, not just answer questions. The company says it can navigate mobile and desktop applications, click buttons, fill out forms, extract information, and finish multi-step jobs without a person guiding every single move.
That ability is what Alibaba calls visual agentic capability. The model sees what is on a screen, figures out what needs to happen next, and does it. This puts Qwen 3.5 in a very different category compared to models that sit inside a chat window and wait for input.
The cost story is equally important. A 60 percent drop in inference cost means the bill for running Qwen 3.5 at scale is less than half of what enterprises were paying to run comparable Qwen models before. For any company that has been testing AI but holding back on full deployment because the compute bill kept growing, that number changes the math.
The Five Things That Define Qwen 3.5
Cost That Finally Makes Sense at Scale
Running AI at scale has always been expensive. Every query, every automated task, every API call adds up fast. Qwen 3.5 cuts inference costs by 60 percent, which means an organization running a million AI interactions a day pays significantly less than it did a year ago. For startups, mid-size businesses, and enterprises in price-sensitive markets like India and Southeast Asia, this is the most important number in the whole announcement.
Eight Times the Workload Capacity
Speed and efficiency matter when AI has to handle real business volumes. Qwen 3.5 processes large workloads eight times faster than the previous version. In practical terms, that means quicker responses for end users, less queuing during peak demand, and the ability to deploy AI across more business processes at the same time without adding infrastructure.
An AI That Takes Action, Not Just Advice
Visual agentic capability is the feature that changes how people think about what an AI model is for. Qwen 3.5 can look at any screen, understand what application is running, and carry out actions the way a human operator would. It works across Android, iOS, Windows, and macOS environments. It does not need an API to connect to an app because it interacts through the visual interface directly.
Think about what that means for enterprise processes. A finance team could assign Qwen 3.5 to pull invoice data from three different systems, cross-reference it, flag discrepancies, and send a summary to the right person. Nobody writes custom code. Nobody sets up API connections. The model handles the whole chain.
Works Across Every Major Platform
Most AI tools work well inside one ecosystem. Qwen 3.5 is built to work across all of them. Whether a business runs its operations on Google Workspace, Microsoft 365, or a mix of legacy tools and modern SaaS, the model can navigate those environments through visual interaction. That matters enormously for large organizations that have never been able to standardize on a single platform.
More Output Per Rupee, Dollar, or Yuan
Alibaba’s core benchmark claim is that Qwen 3.5 delivers more capability for every unit of compute cost than any model it is being compared against. That includes GPT-5.2 from OpenAI, Claude Opus 4.5 from Anthropic, and Gemini 3 Pro from Google. If those numbers hold up under independent testing, enterprise buyers in 2026 have a real alternative to consider beyond the usual U.S.-origin AI stack.
Qwen 3.5 Against the Competition
Versus GPT-5.2
OpenAI’s GPT-5.2 is deeply embedded in Microsoft’s enterprise ecosystem and carries strong brand recognition globally. Alibaba’s benchmarks put Qwen 3.5 ahead on capability per inference cost. Whether that holds up across all use cases remains to be tested independently, but the cost difference alone is significant enough to force a second look from any procurement team.
Versus Claude Opus 4.5
Anthropic’s Claude Opus 4.5 has built a reputation for careful reasoning and reliable instruction-following, particularly in compliance-sensitive industries. Alibaba claims Qwen 3.5 outperforms it on its published benchmarks. That is a strong claim given Claude’s standing in sectors like legal, finance, and healthcare IT, and it will face close scrutiny from the analyst community.
Versus Gemini 3 Pro
Google’s Gemini 3 Pro benefits from tight integration across Google Cloud and Workspace. Qwen 3.5 claiming performance gains over Gemini 3 Pro is notable given Google’s depth in multimodal AI research. The real-world difference will depend heavily on which tasks are being measured and in which deployment environment.
Versus DeepSeek (The One Everyone Is Watching)
Alibaba never mentioned DeepSeek in its official announcement. That silence is telling. DeepSeek is the competitor that moved global markets when its last model launched. It triggered a tech stock selloff and forced a reassessment of assumptions about American AI cost advantages. DeepSeek’s next model is expected within days of this writing, and the industry is watching closely. Alibaba almost certainly timed the Qwen 3.5 launch to capture attention before that happens.
The Chinese AI Market Is Moving Fast
ByteDance and the Doubao Factor
ByteDance currently holds the largest user base in China’s AI market. Its Doubao platform serves close to 200 million users and is deeply integrated into the ByteDance content and social ecosystem. One day before Alibaba’s Qwen 3.5 announcement, ByteDance launched Doubao 2.0 and positioned it as an AI agent platform. The timing made the competitive dynamic impossible to miss.
Both companies are now racing toward the same goal: AI that does things rather than just saying things.
Alibaba’s Commerce Integration Play
Earlier in February 2026, Alibaba reported a sevenfold increase in active Qwen users after running a campaign that let people order food and drinks through the chatbot directly. There were technical glitches. The growth still happened. That tells you what Alibaba is actually building toward: an AI layer woven into its commerce infrastructure, not a standalone product competing on model specs alone.
What This Means by Region
India
India’s enterprise AI adoption has moved quickly in intent and slowly in deployment. The gap between the two has often been cost. A 60 percent inference cost reduction from a model with Alibaba Cloud’s regional infrastructure behind it changes what is realistic for mid-market BFSI, logistics, and healthcare IT companies in 2026. If your AI budget could not justify full deployment before, the numbers look different now.
Southeast Asia
Qwen models have historically offered stronger multilingual support for Asian languages than most Western AI alternatives. That matters in markets where Indonesian, Vietnamese, Thai, and Filipino are the working languages. Combined with the cost efficiency gains, Qwen 3.5 has a real case to make in the region’s fast-growing fintech and e-commerce sectors.
Global Enterprise
For multinationals that want AI infrastructure outside the U.S.-hosted ecosystem, whether for data sovereignty reasons or simply to diversify vendor dependency, Qwen 3.5 is the most credible Chinese AI option available to date. Benchmark claims still need independent validation, but the commercial and technical profile is serious enough to belong in any shortlist evaluation.
Five Questions People Are Asking About Qwen 3.5
What exactly is Alibaba Qwen 3.5? Qwen 3.5 is Alibaba’s latest large language model, released in February 2026. It is built for autonomous task execution, offers 60 percent lower inference costs than its predecessor, and can interact independently with mobile and desktop applications through visual recognition.
How does the cost reduction work in practice? Inference cost is what organizations pay each time the AI processes a request. Qwen 3.5 reduces that cost by 60 percent, which means businesses can run significantly more AI operations for the same budget compared to the previous Qwen model.
What does visual agentic capability mean? It means the model can see what is on a screen, understand the context of whatever application is open, and take actions on its own, such as navigating menus, filling forms, and completing workflows, without a human directing each step.
Has anyone independently verified Alibaba’s benchmark claims? Not yet. The benchmarks showing Qwen 3.5 outperforming GPT-5.2, Claude Opus 4.5, and Gemini 3 Pro come from Alibaba’s own testing. Independent third-party validation had not been published at the time of this report.
When will DeepSeek’s next model launch? DeepSeek is expected to release its next-generation model within days of this article being published. That launch is widely anticipated as the next major benchmark event in the China AI space.
The Bigger Picture
Alibaba Qwen 3.5 arrives at a moment when the definition of what an AI model should do is shifting fast. The value is no longer in which model scores highest on a standard benchmark. The value is in which model can complete actual work inside actual business environments at a cost that makes commercial sense.
Qwen 3.5 has a credible answer to that question. Whether it holds up once DeepSeek’s next release lands and independent researchers put the benchmarks through their paces is what the next few weeks will decide.
What is already clear is that 2026 is not a year for watching AI from a safe distance. The companies acting on it now are the ones building an operational advantage that will be hard to close later.
For users who want to follow more regional updates, Best Startup Asia continues to track technology developments across the continent.
