Baidu Open-Sources ERNIE 5.5 Flash Base, Setting SOTA for Same-Size Models on Multiple Benchmarks
On November 5, Baidu officially open-sourced the base model ERNIE 5.5 Flash Base, which adopts a MoE architecture with a total parameter count of 280 billion and 14 billion parameters activated per token. The model was pre-trained on 33.5 trillion tokens, with a maximum pre-training context length of 128K, and supports a context length of up to 1 million tokens during fine-tuning and inference. The company claims it achieves SOTA among same-size models on benchmarks covering knowledge, reasoning, mathematics, code, multilingual capabilities, and long context, surpassing models such as DeepSeek-V4-Flash-Base, Hy3-preview-Base, and MiMo-V2.5-Base.
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The original text provides the parameter count, activation scale, and pre-training data volume of ERNIE 5.5 Flash Base, allowing comparison of specification differences with same-size open-source base models.
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On November 5, Baidu officially open-sourced its latest foundation model, ERNIE 5.5 Flash Base. The model adopts a MoE architecture, with a total of 280 billion parameters, activating 14 billion parameters per token. It was pre-trained on 33.5 trillion tokens, with a pre-training context length of up to 128K, and supports a context of up to 1 million tokens during fine-tuning and inference. It achieves SOTA performance among models of the same size on multiple benchmarks covering knowledge, reasoning, mathematics, code, multilingual capabilities, and long context, surpassing models such as DeepSeek-V4-Flash-Base, Hy3-preview-Base, and MiMo-V2.5-Base.
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