The company says their new model performs just as well as top-tier models like GPT-5.2-Thinking, Claude-Opus-4.5, and Gemini 3 Pro.
Alibaba Cloud is making a bold statement with its new AI model, Qwen3-Max-Thinking, claiming it’s one of the best reasoning engines out there. Its recent benchmark scores really put it in the running against top models from Google and OpenAI.In a recent blog post, Alibaba explained that they trained the model with a lot more data and powerful computing, even using something called reinforcement learning. This helped them boost its accuracy, reasoning skills, how well it follows instructions, its ability to match what people want, and its “agent” capabilities.
“Across 19 standard benchmark tests, it performs similarly to other leading models like GPT-5.2-Thinking, Claude-Opus-4.5, and Gemini 3 Pro,” the company stated.
Alibaba also mentioned two big improvements to Qwen3-Max-Thinking: it now has “adaptive tool use,” meaning it can grab information or run code when necessary. Plus, they’ve used “test-time scaling techniques” which, they claim, help it reason even better than Google’s Gemini 3 Pro in certain tests.
Analysts are taking a measured approach to this news. Benchmark results show how models perform in very specific scenarios, “but enterprise IT leaders often deploy these foundational models for all sorts of different uses and in varied IT setups,” explained Lian Jye Su, a chief analyst at Omdia.
Su added, “So, while Qwen models definitely look like solid alternatives to popular Western models, we still need to see how they perform in specialized tasks, and how adaptable and customizable they truly are.” He also stressed, “It’s super important to check their scalability and efficiency when they run on Alibaba Cloud’s own infrastructure, which works differently than Google Cloud Platform or Azure.”
More Choices for Spreading Out Your Vendors
The arrival of Qwen3-Max-Thinking will probably encourage more companies to diversify their AI model choices.
“Now that Qwen models have proven they’re legitimate alternatives to Western options, CIOs should absolutely consider them when looking at pricing, licensing, and the overall cost of their AI projects,” Su suggested. “If you run them on Alibaba Cloud, the cost of ownership could be more efficient, particularly in the Asia Pacific region. This is fantastic news for global businesses aiming to enter the Chinese market or other China-friendly areas.”
The impressive reasoning scores from Qwen models mean there are more good suppliers to choose from, making it more appealing for companies to spread out their AI investments, noted Charlie Dai, a principal analyst at Forrester.
“For CIOs focused on digital independence and keeping costs down, having strong alternatives really changes the game,” Dai stated. “As models become more equal in performance, it makes more sense to use a mix of different models, helping to balance independence, regulatory compliance, and how fast they can innovate.”
Others commented that the consistent positive benchmark results are also shaping how CIOs approach using multiple AI models.
“These benchmarks are useful not just for tracking performance, but also for seeing which companies are truly committed to investing consistently in foundational model development and adoption,” explained Neil Shah, VP for research at Counterpoint Research. “This is influencing CIOs to explore multi-model strategies, avoiding putting all their eggs in one basket, while also considering performance, cost, and global political factors.”
However, CIOs will still need to think about whether these models are available outside of the Asia-Pacific region, along with other crucial things like export restrictions and following local laws.
“The larger question is how CIOs decide between US and non-US models depending on their specific AI needs,” Shah noted. “For situations where reliability and strict compliance are key, businesses, especially in Western countries, will likely lean towards proprietary US models. Meanwhile, capable Chinese models might be a good fit for less critical tasks.”
New Hurdles for Governance and Compliance
Global political tensions are making things even more complicated for companies trying to decide on models like Qwen3-Max-Thinking. Dai believes this means they need to look *very* closely at operational specifics, especially things like system logs, how models get updated, and how data flows across different countries.
He also suggested that company evaluations shouldn’t just stop at performance tests. They should also include “red-team exercises” (where experts try to find vulnerabilities), ensuring sensitive data is kept completely separate, and making sure everything aligns with their own internal risk and compliance rules.
“Companies looking at models hosted on Alibaba Cloud really need to dig into how AI safety controls, data separation, and auditability actually work in practice, not just what’s written down,” Su advised. “Even though most cloud providers now offer options for deploying models within specific regions or on-premise to meet data sovereignty rules, CIOs still have to determine if those controls truly meet their company’s own risk limits, especially when dealing with sensitive intellectual property or regulated information.”
