Wu Dao 3.0 in 2025: China's Version of GPT

In July 2023, the Beijing Academy of Artificial Intelligence (BAAI) unveiled Wu Dao 3.0, the successor to their previous AI system. This new iteration takes a different approach, focusing on helping startups and smaller companies build their own AI applications without sacrificing performance.

The shift toward smaller Wu Dao models

China’s AI landscape has faced several challenges lately. Legal restrictions, high development costs, and international chip sanctions have made building massive AI models increasingly difficult. In response, researchers reimagined Wu Dao as a collection of smaller, more efficient models called Wu Dao Aquila.

This practical shift makes advanced AI more accessible to Chinese businesses. These compact models need fewer chips to run, reducing dependence on scarce hardware—a critical advantage given China’s current tech constraints.

The Chinese government has pivoted its AI strategy toward practical applications and open-source collaboration. Rather than pursuing isolated mega-projects, they’re now encouraging companies to share models, datasets, and computing resources to speed up innovation across the board.

Alibaba’s Qwen and DeepSeek models represent the most successful examples of this collaborative approach. BAAI, a nonprofit research organization, has embraced this philosophy by making Wu Dao Aquila open-source. Their goal resembles creating an AI ecosystem similar to Linux—providing a foundation that ensures long-term growth and accessibility for everyone.

Differences between Wu Dao 2.0 and 3.0

Wu Dao 2.0 has been trained on vast datasets, including 4.9 TB of high-quality text and images in English and Chinese.¹ It utilized a Mixture of Experts (MoE) system, FastMoE, which distributes tasks across specialized models to improve efficiency.^{2 3} By surpassing the state-of-the-art (SOTA) levels on 9 benchmarks, it is a good competitor for artificial general intelligence (AGI) and achieving human-level thinking.

Wu Dao 3.0 builds on this foundation with a more optimized architecture. It uses a sparse model approach that activates only a subset of parameters during inference, improving computational efficiency while maintaining high performance and making it more adaptable for real-world applications.

Capabilities

Based on available information, the Wu Dao 3.0 ecosystem includes several specialized tools:

AquilaChat Dialogue Models: This includes a 7-billion parameter model that BAAI claims outperforms similar open-source alternatives both in China and internationally. There’s also a larger 33-billion parameter version. The smaller model supports both English and Chinese, with Chinese materials making up about 40% of its training data.

AquilaCode Model: This text-to-code generator (still under development) can create everything from simple programs like Fibonacci sequences to more complex applications such as sorting algorithms and games.

Wu Dao Vision Series: This collection tackles computer vision challenges with several specialized tools:

• Multimodal Emu models
• EVA, a billion-scale visual representation model
• A general-purpose segmentation model
• Painter, which pioneers “in-context” visual learning
• EVA-CLIP, reportedly the best open-source CLIP model available
• vid2vid-zero for zero-shot video editing

The EVA foundation model stands out for using publicly available data to develop large-scale visual representation. With one billion parameters, it has set new benchmarks in image recognition, video action recognition, object detection, and various segmentation tasks without requiring extensive supervised training.

BAAI has also enhanced the FlagOpen platform they launched in early 2023. This system offers parallel training techniques, faster inference, evaluation tools, and data processing utilities—essentially providing everything needed to develop large AI models. ⁴

When Wu Dao 2.0 first debuted at the Beijing Zhiyuan Conference, its creators displayed Chinese poems and drawings generated by it.⁵ Following that event, a virtual student was created based on Wu Dao’s AI model, Zhibing Hua. Wu Dao powers the virtual student. Therefore, she can use her knowledge base and learning capabilities to write poems, draw, and compose music.

Although these features are not highlighted for Wu Dao 3.0, they are worth mentioning if you plan to utilize Wu Dao 2.0 for your enterprise instead of Wu Dao 3.0.

Poems generated by Wu Dao 2.0⁶

Zero-Shot learning benchmarks

1. ImageNet: Achieves state-of-the-art zero-shot performance, surpassing OpenAI’s CLIP.
2. UC Merced Land-Use: Records the highest zero-shot accuracy in aerial land-use classification, outperforming CLIP.

Few-Shot learning benchmark

1. SuperGLUE (FewGLUE): Outperforms GPT-3, achieving the best few-shot learning results.

Knowledge and language understanding benchmarks

1. LAMA Knowledge Detection: Demonstrates superior factual knowledge retrieval, surpassing AutoPrompt.
2. LAMBADA Cloze Test: Exceeds Microsoft Turing-NLG in reading comprehension and context understanding.

Text-to-Image and Image-to-Text retrieval benchmarks

1. MS COCO (Text-to-Image generation): Outperforms OpenAI’s DALL·E in generating images from text descriptions.
2. MS COCO (English Image-Text retrieval): Surpasses OpenAI’s CLIP and Google ALIGN in retrieving images from captions (and vice versa).
3. MS COCO (Multilingual Image-Text retrieval): Outperforms UC2 and M3P in multilingual image-text retrieval.
4. Multi30K (Multilingual Image-Text retrieval): Also surpasses UC2 and M3P, confirming its strong multilingual multimodal capabilities.

Wu Dao 3.0 vs. OpenAI GPT

Here’s a comprehensive comparison between Wu Dao 3.0 LLM models and different OpenAI models according to BAAI.⁷ We cannot provide more detailed and up-to-date comparisons for Wu Dao since it doesn’t have recent and consistent benchmarks available.

Long context benchmark

Long context benchmarks measure a model’s ability to process extended or multi-step context. In this benchmark, 4 tasks—VCSUM (Chinese summarization), LSHT (Chinese long-sequence handling), HotpotQA (English multi-hop reasoning), and 2WikiMQA (English multi-document question answering)— are evaluated under different training methods, and the total averages are given below.

Long context performances of LLMs⁸

Reasoning performance benchmark

Reasoning benchmarks measure how effectively a model can handle different reasoning types in textual contexts. In this evaluation, 6 tasks—bAbI #16 and CLUTRR (inductive reasoning), bAbI #15 and EntailmentBank (deductive reasoning), αNLI (abductive reasoning), and E-Care (causal reasoning)—are used to provide a view of the models’ logical capabilities and the total averages are given below.

Reasoning task performances of LLMs⁹

If you want to use Wu Dao, you can set it up on your computer by downloading it for free.¹⁰ To learn more about the capabilities of Chinese AI enterprises, you can explore the leaderboard created by BAAI.¹¹

What is the future of human-level thinking AI?

As large language models like Wu Dao 3.0 evolve with impressive parameters, the path to artificial general intelligence (AGI) remains complex. AGI, or singularity, is AI’s capability of human-level thinking. Projects like Wu Dao’s large-scale aim to push boundaries, yet experts remain divided on timing.

Approximately 90% of AI experts predict an AI singularity by 2075, though some believe modeling the human brain at this level may be impossible.

To learn more about AGI predictions and expert opinions, read our in-depth analysis: Will AI reach singularity by 2060? 995 experts’ views on AGI.

FAQs

Further Reading

• Top 5 Expectations Regarding the Future of NLP
• 141 Myth-Busting Statistics on Artificial Intelligence (AI)
• 100+ AI Use Cases & Applications: In-Depth Guide

External Links

• 1. https://mp.weixin.qq.com/s/NJYINRt_uoKAIgxjNyu4Bw
• 2. Mixture of experts - Wikipedia. Contributors to Wikimedia projects
• 3. [2103.13262] FastMoE: A Fast Mixture-of-Expert Training System.
• 4. https://flagopen.baai.ac.cn/#/home
• 5. China’s GPT-3? BAAI Introduces Superscale Intelligence Model ‘Wu Dao 1.0’ | Synced. Synced
• 6. China’s GPT-3? BAAI Introduces Superscale Intelligence Model ‘Wu Dao 1.0’ | Synced. Synced
• 7. https://model.baai.ac.cn/model-detail/220118
• 8. https://model.baai.ac.cn/model-detail/220118
• 9. https://model.baai.ac.cn/model-detail/220118
• 10. https://model.baai.ac.cn/models
• 11. https://flageval.baai.ac.cn/#/leaderboard/arena