# Baidu Ships ERNIE 5.5 Flash Preview, a Post-Trained 280B MoE on Its Raw Base, API-First With Apache 2.0 Weights Promised Times of AI Desk · 2026-11-07 · Models [https://timesof.ai/2026/11/baidu-ernie-5-5-flash-base-280b-moe](https://timesof.ai/2026/11/baidu-ernie-5-5-flash-base-280b-moe) > Updated Nov. 12: Baidu on November 11 released ERNIE 5.5 Flash Preview, the first post-trained model built on ERNIE 5.5 Flash Base, with 280 billion total and 14 billion active parameters and a 1 million-token context. The API is available on Baidu's Qianfan platform, and Baidu says the weights will be open-sourced under Apache 2.0 soon; TokenPost reports before November 25. Baidu calls the Preview an intermediate post-training checkpoint. Earlier: on November 5 Baidu released the Base checkpoint itself, pretrained on 33.5 trillion tokens, under its own licence. **Updated November 12, 2026:** Baidu has released ERNIE 5.5 Flash Preview, the post-trained model this base was built for. The new section directly below covers it; the November 7 report follows unchanged. ## November 12: the post-trained Preview arrives, API first The follow-up Baidu promised came within a week. On **November 11** it released **ERNIE 5.5 Flash Preview**, which it calls "the first post-trained model built on the ERNIE 5.5 Flash Base." It keeps the base's shape: **280 billion** total parameters, **14 billion** active and a **1 million-token** context. Baidu says it is strong at coding, 3D modeling, knowledge work and content creation, and scores "competitively across major benchmarks while keeping decoding fast." Access is API-first. The API is available on Baidu's **Qianfan** platform, and Baidu says the weights "will be open-sourced under Apache 2.0 soon." TokenPost reports the weights are due before **November 25**; Baidu's post gives no date. Apache 2.0 would be a looser licence than the base checkpoint's own agreement described below. Baidu's showcase leans on long agentic jobs, including a third-person Unreal Engine 5.8 game it says the model wrote in C++ in a single **11-hour** coding task inside an agent harness, and nearly 100 mobile mini-games built from design documents. Baidu also says the Preview is "an intermediate checkpoint from post-training" and that "some capabilities may still fall short" before an official release. Alibaba took a similar preview-first route this week [with Qwen4-Max, offered by subscription only](https://timesof.ai/2026/11/alibaba-qwen4-max-preview-1m-context-token-plan-only). ### Limits of the November 12 update - Benchmark and capability claims are Baidu's; we found no independent leaderboard result we could check, and posts on X citing arena scores were not verified. - The weights are not yet published, so the Apache 2.0 licence is a promise, not a release. - The demonstrations were chosen by Baidu and used external assets it credits. Baidu is publishing the raw pretrain, not a chat model, and asking other people to do the expensive part that comes next. On **November 5** it released **ERNIE 5.5 Flash Base**, a sparse mixture-of-experts language model with **280 billion** total parameters and **14 billion** activated per token. The company says the model was pretrained from scratch on **33.5 trillion** tokens, that training context was extended to **128,000** tokens, and that fine-tuning and inference support a **1 million-token** context. The weights are the checkpoint at the end of pretraining, before any mid-training or post-training. That distinction matters for anyone comparing it with instruction-tuned peers. Baidu positions the release as a shared starting point for domain adaptation, instruction tuning and reinforcement-learning research, and says a post-trained **ERNIE 5.5 Flash** will follow. The weights are on Hugging Face in **FP8** and **BF16** safetensors under the **ERNIE 5.5 Flash Base Model License Agreement v1.0**, not Apache or MIT. Baidu says **SGLang** and **vLLM** serve the model through the completions API. ## How Baidu scores it The company evaluated the checkpoint against three other recently released open base models of similar size — DeepSeek-V4-Flash-Base, Hy3-preview-Base and MiMo-V2.5-Base — using, it says, the same harness, prompts, few-shot examples and LLM judge. In that setup Baidu reports, among other figures, MMLU at **93.3** versus **89.4** for DeepSeek-V4-Flash-Base, HumanEval+ pass@1 at **82.3** versus **69.5**, and LiveCodeBench pass@1 at **40.1** versus **31.9**. Those are vendor-run base-model scores, not an independent leaderboard snapshot. Architecturally, Baidu describes a hybrid of full-context and heavily compressed attention, a multi-router latent MoE, and several other efficiency choices aimed at further training and long context. We are treating those descriptions as the lab's account of its own design. ## Limits - All benchmark figures are from Baidu's controlled comparison; we have not re-run them or found a matching independent board entry this run. - The checkpoint is pretrained only. Chat quality, tool use and refusal behavior will depend on whoever post-trains it. - The license is Baidu's own agreement, not a standard permissive open-source license; downstream users need to read it. - "Supports a 1 million-token context for fine-tuning and inference" is Baidu's claim; we have not measured long-context quality at that length. ## Sources - [Baidu ERNIE: Introducing ERNIE 5.5 Flash Preview (November 11, 2026)](https://ernie.baidu.com/release/ernie-5-5-flash-preview/) - [TokenPost: Baidu Releases ERNIE 5.5 Flash Preview, Open Weights Due Before Nov. 25 (November 11, 2026)](https://www.tokenpost.com/news/technology/54709) - [Baidu ERNIE: ERNIE 5.5 Flash Base (November 5, 2026)](https://ernie.baidu.com/release/ernie-5-5-flash-base/)