Baidu ERNIE: Model Family, ERNIE Bot and Open Weights

ERNIE is the name Baidu uses for its family of large AI models. The name started as an acronym, Enhanced Representation through kNowledge IntEgration, and today covers research language models, large multimodal models, and the consumer chatbot ERNIE Bot. When someone says "ERNIE" without a version number, it is worth asking which one they mean, because a 2019 ERNIE model and a 2025 ERNIE model share a brand but little else in size, design or capability.

The first ERNIE paper, posted to arXiv on April 19, 2019 by Yu Sun and colleagues at Baidu, changed one part of BERT's recipe. BERT, Google's 2018 encoder model, learns by hiding random word pieces in a sentence and predicting them. ERNIE instead hid whole units of meaning. Entity-level masking hides a full named entity such as a person or place, which usually spans several tokens, and phrase-level masking hides a multi-word phrase that acts as one concept. To fill those gaps, the model has to use what it has learned about the world rather than guessing from neighboring characters. The authors reported state-of-the-art results on five Chinese tasks: natural language inference, semantic similarity, named entity recognition, sentiment analysis and question answering, plus better scores on a cloze test of factual knowledge.

ERNIE 2.0, posted in July 2019 and accepted at AAAI 2020, kept the idea of richer training signals but changed how they were added. It used continual multi-task pretraining: new pretraining tasks were introduced over time and learned alongside earlier ones so the model did not forget them. The tasks targeted lexical, syntactic and semantic information, such as named entities, how close two sentences are in meaning, and discourse relations between sentences. The paper reports that ERNIE 2.0 beat BERT and XLNet across 16 tasks, including the English GLUE benchmark and several Chinese datasets.

ERNIE 3.0, posted on July 5, 2021, scaled the approach to 10 billion parameters trained on a 4 TB corpus that combined plain text with a large knowledge graph. It also merged two families of design: an auto-encoding network, which reads text in both directions and is good at understanding, and an auto-regressive network, which predicts text left to right and is good at generation. The paper reports better results than prior models on 54 Chinese tasks, and says its English version ranked first on the SuperGLUE leaderboard on July 3, 2021 with a score of 90.6, above the 89.8 human baseline listed on that leaderboard.

The consumer era began on March 16, 2023, when Baidu presented ERNIE Bot in Beijing. The Associated Press reported that the demonstration used pre-recorded video rather than a live session, that CEO Robin Li said 650 companies had signed up, and that Baidu's Hong Kong shares fell as much as 10% during the presentation before closing down 6.4%. Li said the product was not yet perfected but was being released because of demand. Two years later, on March 16, 2025, Baidu announced ERNIE 4.5, described as a native multimodal foundation model for text, images, audio and video, and ERNIE X1, a reasoning model that can call tools such as search, document question answering and a code interpreter. The same release made ERNIE Bot free to individual users ahead of a planned April 1 date.

The largest change in strategy came at the start of July 2025, when TechNode reported that Baidu had open-sourced 10 ERNIE 4.5 models on Hugging Face, GitHub and its PaddlePaddle platform under the Apache 2.0 license, which allows research and commercial use subject to its terms. The flagship text model, ERNIE-4.5-300B-A47B, is a mixture-of-experts (MoE) model. An MoE model splits parts of the network into many smaller "experts" and uses a small router to send each token to only a few of them, so a model can hold many parameters while spending compute on only some of them. According to the model card, this model has 300 billion total parameters, 47 billion active per token, 54 layers, 64 text experts with 8 active per token, and a context window of 131,072 tokens. The broader family, per TechNode, ranges up to 424 billion total parameters and includes smaller MoE models with 3 billion active parameters and 0.3 billion parameter dense models; Baidu also reported training efficiency of up to 47% model FLOPs utilization. On November 13, 2025, at Baidu World 2025, Baidu previewed ERNIE 5.0, a proprietary model it describes as natively omni-modal, available in preview through ERNIE Bot and the Qianfan platform, according to eWeek.

Compared with alternatives, ERNIE's distinctive traits are its long focus on Chinese language tasks, its tight integration with Baidu's own stack (the PaddlePaddle deep learning framework, Baidu Search, and Baidu AI Cloud), and a mixed release policy in which some generations are open-weight and others are closed. Readers comparing it with Western models such as GPT, Claude or Gemini, or with other Chinese families such as Qwen and DeepSeek, should rely on independent benchmarks rather than launch claims. Baidu's March 2025 release said ERNIE 4.5 outperformed GPT-4.5 on multiple benchmarks without naming them in the release, and such statements are company claims rather than verified results.

There are practical limits to keep in mind. ERNIE Bot is built for the Chinese market and operates under Chinese content rules, so answers on politically sensitive topics can differ from those of models built elsewhere. Version names are easy to confuse: ERNIE, ERNIE Bot, ERNIE 4.5 and ERNIE X1 refer to different things. Open weights cover specific ERNIE 4.5 checkpoints, not every model Baidu serves, and ERNIE 5.0 was announced as proprietary. Like all large language models, ERNIE models can produce confident but wrong statements, so outputs that matter should be checked.

In practice there are three ways to use ERNIE. Individuals can use ERNIE Bot for free. Businesses can pay per token through Qianfan, where Baidu priced ERNIE X1 from RMB 0.002 per thousand input tokens and RMB 0.008 per thousand output tokens at launch. Teams that want control can self-host the open ERNIE 4.5 checkpoints, but the flagship needs serious hardware: the model card lists 16 GPUs with 80 GB each in bf16, or 8 such GPUs with FP8 quantization, while the smaller 3 billion active parameter models fit far more modest setups.