Baidu AI – Keeping Up Appearances

Baidu joins the efficiency game. 

• Keen to catch up in the China AI game, Baidu has launched two newer and cheaper models, but it is not clear whether Baidu is offering further innovation on efficiency or just a cheaper price to stem market share loss to its rivals.
• Baidu has released the latest version of its foundation model ERNIE and a “reasoning” model that is upon it called ERNIE X1.
  • First, ERNIE 4.5: which Baidu claims is fully multimodal being able to handle videos, photos and text and beats GPT-4o on selected benchmarks.
  • Baidu produced data which showed ERNIE 4.5 beating GPT-4o on multimodal benchmarks such as CCBench, ChartQA, and DocVQA but losing on MMMU.
  • Over its selected benchmarks it beat 4o with an average score of 77.8 vs. 4o on 73.9.
  • On text capability, it also scored very well against DeepSeek R1, GPT-4.5 and so on.
  • Baidu has also moved into line with its Chinese peers and will be making its model available to open source for the first time.
  • This is an accelerating trend in China and I expect that this will soon become standard procedure for Chinese models as it helps drive adoption by developers which is what winning the AI ecosystem is all about.
  • ERNIE 4.5 is priced to sell at $0.55 per million input tokens and $2.20 per million output tokens which is 136x and 68x cheaper than OpenAI’s current price for GPT-4.5.
  • I suspect this is more a factor of Open AI being ridiculously overpriced as opposed to ERNIE 4.5being very cheap.
  • Second, ERNIE X1: which is based on ERNIE 4.5 but has been fine-tuned to “reason” with more inference time dedicated to producing multiple answers and then distilling a more detailed answer.
  • Baidu claims good performance for X1 but has not provided any benchmark data, but I suspect that it will measure up reasonably well when it is properly tested.
  • Here the target is clearly DeepSeek R1 and Baidu has again priced its model to sell with $0.28m per million input tokens and $1.10 per million output tokens which is roughly half what DeepSeek is currently charging for access to R1.
• Baidu is pricing its AI services to attract users, but this is no indication of what gains in efficiency (if any) that it has made to be able to offer service at this price point.
• Instead, Baidu may have decided that it will lose money to ensure that the likes of Alibaba or DeepSeek do not steal the fledgling AI ecosystem that it is building.
• Price cuts are already common in China which are fuelling a brutal price war where I suspect only a few will be able to make money.
• This is made doubly difficult by the fact that China remains cut off from the advanced silicon that would allow cost per token to fall meaning that Chinese companies will need to find other ways to become more efficient.
• This is precisely what is going on and I suspect that the Chinese are going to continue to lead when it comes to finding increasingly efficient ways of running AI while maintaining leading-edge performance.
• The most recent release was Alibaba’s QWB-32B model which is very small in size but quite mighty when it came to performance according to Alibaba (see here).
• The net result is that despite the deluge of unsubstantiated claims, I think that the Chinese are ahead when it comes to training and inferencing AI efficiently and it is likely to stay that way.
• That means that when the correction comes and everyone is forced to do more with less, China will find itself with a significant advantage.
• I continue to think that this advantage lies in the methods and techniques that Chinese companies use to train and run these models which are not part of the models and weights that they are publishing to the open source.
• This is how China can grow its standing in AI, see its models get adopted outside of China and at the same time retain its IP and maintain a lead.
• I continue to have a position in Alibaba as it has pivoted towards AI which has had a good effect on the valuation my position should continue to reduce its losses.