Alibaba Cloud states its new AI model performs similarly to major competitors like GPT-5.2-Thinking, Claude-Opus-4.5, and Gemini 3 Pro.
Alibaba detailed in a blog post that the model’s development involved extensive resources and reinforcement learning, resulting in enhanced factual accuracy, reasoning abilities, instruction adherence, human preference alignment, and agent-like functionality.
“Across 19 recognized benchmarks, it demonstrates performance on par with prominent models like GPT-5.2-Thinking, Claude-Opus-4.5, and Gemini 3 Pro,” the company asserted.
Alibaba highlighted two significant enhancements to Qwen3-Max-Thinking: adaptive tool utilization, enabling the model to access information or execute code when necessary, and test-time scaling methods, which reportedly surpass Google’s Gemini 3 Pro in reasoning on certain benchmarks.
Analysts expressed a degree of caution regarding the announcement. While benchmarks assess performance in particular scenarios, “Lian Jye Su, Omdia’s chief analyst, noted that enterprise IT leaders might deploy foundational models for diverse applications within varying IT infrastructures.”
Su commented, “Therefore, although Qwen models appear to be valid substitutes for established Western models, their efficacy requires further evaluation in specialized tasks, including their flexibility and tailored capabilities. Furthermore, their scalability and operational efficiency on Alibaba Cloud infrastructure, which differs from Google Cloud Platform and Azure, must be critically examined.”
Increased Choices for Vendor Diversification
Qwen3-Max-Thinking’s introduction is poised to accelerate enterprises’ initiatives to diversify their AI model portfolios.
Su advised, “With Qwen models proving to be credible alternatives to Western counterparts, CIOs ought to factor them into considerations for pricing, licensing, and overall ownership costs for their AI endeavors. Operating on Alibaba Cloud is expected to offer more cost-effective ownership, particularly in the Asia Pacific region, presenting a valuable opportunity for international businesses targeting the Chinese or China-aligned markets.”
The strong reasoning benchmarks achieved by Qwen models broaden the selection of potential providers, increasing the appeal of diversification, as noted by Charlie Dai, a principal analyst at Forrester.
Dai remarked, “For CIOs concerned with digital sovereignty and cost-effectiveness, the emergence of robust alternatives alters the strategic landscape, and growing model equivalence enhances the feasibility of varied portfolios that can balance sovereignty, adherence to regulations, and innovation pace.”
Additionally, experts noted that the strong benchmark performance is shaping CIOs’ approaches to multi-model strategies.
Neil Shah, VP for research at Counterpoint Research, stated, “These benchmarks serve as valuable indicators not only for tracking performance but also for discerning which firms are genuinely and persistently investing in foundational model development and deployment. This trend is influencing how CIOs consider adopting multi-model strategies to mitigate risk, balancing factors like performance, cost-efficiency, and geopolitical challenges.”
Nevertheless, CIOs must evaluate the availability of these models beyond the APAC region, along with other considerations such as export restrictions and adherence to local laws.
Shah posited, “The crucial inquiry revolves around how CIOs will choose between US and non-US models depending on their specific AI applications. In scenarios demanding high reliability and strict compliance, particularly in Western markets, businesses are likely to prefer proprietary US models, whereas advanced Chinese models might be employed for less critical tasks.”
Additional Governance and Compliance Hurdles
Evaluating models like Qwen3-Max-Thinking is complicated by geopolitical tensions. Dai suggested that this necessitates a thorough examination of operational specifics, especially concerning system logs, model update procedures, and cross-border data flows.
He further advised that corporate assessments should extend beyond mere performance tests to encompass red-teaming, stringent segregation of sensitive data, and alignment with internal risk management and regulatory compliance protocols.
Su emphasized, “Companies considering Alibaba Cloud-hosted models must meticulously examine the practical implementation of AI safety measures, data segregation, and auditability, rather than solely relying on documented policies. Although many cloud providers offer in-region or on-premise solutions to satisfy sovereignty requirements, CIOs are still responsible for verifying if these controls adequately address their internal risk tolerances, especially concerning proprietary intellectual property or regulated data.”
