Baidu just dropped ERNIE-5.5-Flash-Base, and the weights are available. 280B MoE, only 14B active per token, pretrained from scratch on 33.5T tokens. It's a pure base model with no mid- or post-training, so labs can adapt it for domain-specific tasks, reasoning, and agents. What makes it exciting is how hard it pushes on efficiency: - Only 6 of 43 layers keep token-level access to the full context. The other 37 squeeze every 128 tokens into a single cached entry, which keeps the KV cache small. Context still goes up to 1M tokens for fine-tuning and inference. - Multi-Router MoE: several routers pick the experts together instead of just one, without increasing activated expert compute or communication costs. - ECHO: multi-token prediction runs through the model's own final layer, no separate MTP module needed. And the early numbers look strong. In Baidu's preliminary evals against DeepSeek-V4-Flash-Base, a model of almost the same size: - MMLU-Pro: 78.4 vs. 69.2 - HumanEval+: 82.3 vs. 69.5 - LiveCodeBench: 40.1 vs. 31.9 Weights are live on Hugging Face in FP8 and BF16: The model license excludes the EU, UK, and South Korea from its permitted territory.