Introduction: In the relentless arms race of artificial intelligence, every major tech player vies for dominance, often with bold claims that outpace verification. Baidu’s latest open-source multimodal offering, ERNIE-4.5-VL-28B-A3B-Thinking, enters this fray with assertions of unprecedented efficiency and human-like visual reasoning, challenging established titans like Google and OpenAI. But as a seasoned observer of this industry, I’ve learned to parse grand pronouncements from demonstrable progress, and this release demands a closer, more critical examination.

Key Points

• Baidu’s ERNIE-4.5-VL-28B-A3B-Thinking boasts a Mixture-of-Experts architecture that promises high performance with significantly reduced operational resource consumption, activating only 3 billion of its 28 billion parameters per task.
• The strategic decision to release the model under the permissive Apache 2.0 license aims to accelerate enterprise adoption and carve out market share, particularly in industrial applications, by lowering barriers to entry.
• Despite ambitious performance claims against unreleased or highly specific competitive models, independent third-party verification remains conspicuously absent, raising questions about the real-world generalizability and robustness of its “Thinking with Images” feature.

In-Depth Analysis

Baidu’s new ERNIE variant, ERNIE-4.5-VL-28B-A3B-Thinking, arrives adorned with a laundry list of impressive specifications and capabilities. At its core lies a Mixture-of-Experts (MoE) architecture, a design gaining traction for its efficiency. The premise is compelling: why activate a sprawling 28 billion parameter model for every query when a specialized subset of 3 billion parameters can handle the task? This on-demand activation, allowing the model to run on a single 80GB GPU, is indeed a practical advantage for enterprises wary of the prohibitively expensive hardware demands of monolithic large language models. It democratizes access to advanced multimodal AI, at least on paper, potentially broadening the market beyond hyperscale data centers.

Yet, the true test of any architectural innovation lies not just in its theoretical elegance but its real-world resilience and performance consistency. While Baidu touts “human-like” dynamic zooming – “Thinking with Images” – to analyze fine details, this capability, while an advancement over fixed-resolution processing, requires scrutiny. Is it a genuine emulation of human cognition, or a sophisticated algorithmic process of iterative cropping and re-embedding? The distinction is crucial. True human ‘thinking’ involves abstraction, context retention across scales, and adaptive problem-solving that goes beyond merely zooming. For applications like industrial quality control or complex technical diagram analysis, robust, unambiguous interpretation is paramount, and the nuances of such a mechanism are critical.

The competitive landscape further complicates Baidu’s bold assertions. Claims of outperforming Google’s Gemini 2.5 Pro and OpenAI’s GPT-5-High (a model not yet officially released, let alone widely benchmarked) are attention-grabbing but also highly suspect. Benchmarks are notorious for being susceptible to data leakage, specific training optimizations, and selective reporting. The absence of independent validation makes these claims akin to a chef announcing their dish is the best in the world before anyone else has tasted it. The Apache 2.0 license is undoubtedly a shrewd move, strategically positioned to woo enterprise clients with open access and reduced vendor lock-in, contrasting sharply with the more restrictive approaches of some competitors. However, broad adoption will ultimately hinge on demonstrated, independently verified superiority and reliability in diverse, real-world operational environments, not just curated test sets.

Contrasting Viewpoint

While Baidu’s claims paint a picture of revolutionary efficiency and superior performance, a healthy dose of skepticism is warranted. The most glaring issue is the lack of independent verification. Benchmarks cited by a company for its own product, especially against unreleased or specific configurations of competitors, are inherently biased. We’ve seen this play out countless times in tech history: a model excels on a particular dataset designed to highlight its strengths, only to falter when exposed to diverse, ‘in-the-wild’ scenarios. The “Thinking with Images” feature, while conceptually intriguing, could easily be an advanced attention mechanism rather than genuine human-like reasoning. The leap from dynamic resolution processing to “thinking” is a significant semantic one that warrants robust, empirical proof. Furthermore, while the MoE architecture promises inference efficiency, the training costs for such massive systems, including the “extensive mid-training phase” and “vast and highly diverse corpus,” are likely immense, suggesting that the initial investment hurdle for Baidu was still considerable. What’s efficient for inference isn’t always efficient for development. The real challenge for Baidu will be proving that its model’s performance scales reliably and consistently across the unpredictable complexities of real-world enterprise deployment, where ‘industrial-grade precision’ isn’t just a marketing slogan but a non-negotiable requirement.

Future Outlook

Looking ahead, Baidu’s ERNIE-4.5-VL-28B-A3B-Thinking represents a significant technical effort, pushing the boundaries of multimodal AI efficiency. The next 12-24 months will be critical. The permissive Apache 2.0 license could indeed accelerate its adoption, particularly if developers and businesses can validate its performance and integration capabilities. However, its trajectory will largely depend on three key factors: rigorous, independent third-party verification of its performance claims; its ability to demonstrate robust, scalable performance in diverse real-world industrial and enterprise applications; and its capacity to sustain innovation against the rapidly evolving landscape of Western AI giants. The biggest hurdles remain establishing trust through transparency and consistent results, especially concerning the “Thinking with Images” functionality, and overcoming the inherent skepticism that arises when bold claims precede widespread validation. If Baidu can deliver on its promises and demonstrate a clear, repeatable advantage, ERNIE could become a significant player in the global multimodal AI race. If not, it risks becoming another footnote in the annals of AI hype.

For a deeper dive into the architectural trends driving AI efficiency, explore our recent feature on [[The Rise of Mixture-of-Experts in Large Language Models]].

Further Reading

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Original Source: Baidu just dropped an open-source multimodal AI that it claims beats GPT-5 and Gemini (VentureBeat AI)

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