Baidu took a deliberate route with ERNIE 5.5 Flash Base: ship the pre-training weights and keep the chat and tool-use versions to itself. The checkpoint released on November 5, 2026 is a sparse Mixture-of-Experts model with 280B total parameters and 14B activated per token, published on Hugging Face in both FP8 and BF16, and it stops exactly at the end of pre-training with no mid-training or post-training applied. The official release page is at https://ernie.baidu.com/release/ernie-5-5-flash-base/

What the model can actually do

This is a base model, not an assistant you can talk to. Baidu is explicit that the weights are the checkpoint at the end of pre-training, leaving developers to decide where to train it next. The post-trained ERNIE 5.5 Flash will follow with no date given. The model card documents serving through the completions API, meaning you send text and it continues the text, with no chat format at all.

The Decoder framed the release as shipping deliberately without fine-tuning or post-training, MarkTechPost attached a self-reported label to the scorecard, and eesel concluded plainly that teams wanting a customer-service agent should wait for the post-trained version. All three readings point at the same thing: this launch is not about a stronger model, it is about a clean starting point.

The part that got the most engineering: KV cache

Forty-three Transformer layers, hidden size 4096, 64 attention heads. Thirty-seven of those layers use HCA, Heavily Compressed Attention, which squeezes every 128 tokens into a single cached entry plus a short window of recent uncompressed tokens. The remaining six use MLA to keep token-level access to the full context, and layer in DSA sparse attention where a lightweight indexer scores past positions and core attention is computed only over the top-k, so per-query cost stays constant as context grows.

Baidu explains the trade-off plainly: under a similar KV-cache budget it tried stacking many CSA layers, then settled on a few MLA layers, because the HCA plus MLA combination won out on pre-training loss. Head dimensions are also cost-assigned: HCA layers, compressed 128 times, get wide 512-dim heads, while the expensive MLA layers get narrower 256-dim heads. The savings translate directly into the 1M-token context window.

Routing, the output head, and speculative decoding all changed

The MoE is not single-router. Several independently initialized sigmoid routers score the experts jointly, and Baidu reasoning is practical: a single router scores correlate only weakly with how useful each expert really is, and experts left out of the top-k receive no gradient, so wrong selections reinforce themselves. With multiple routers, routing scores correlate noticeably better with expert usefulness, the largest gains land in the middle and deeper layers, and the number of activated experts, expert FLOPs, and communication volume all stay the same. Each layer has 512 routed experts at top-10 plus one shared expert, running in a 2048-dimensional latent space that roughly halves dispatch traffic and expert FLOPs.

Two more pieces stand out. mHC replaces the standard residual connection with four parallel residual streams. And ECHO replaces the common MTP module, reusing the backbone own final layer to predict future tokens with no extra module, while that same layer doubles as the draft model for speculative decoding, drafting several tokens ahead despite being trained to predict only one extra token.

The scorecard, and who interprets it

The evaluation compares against DeepSeek-V4-Flash-Base, Hy3-preview-Base, and MiMo-V2.5-Base, with all four models run through the same harness, prompts, few-shot examples, and LLM judge. Counting the bolded rows in the table, ERNIE 5.5 Flash Base takes the top score on 24 of 30 benchmarks, a figure MarkTechPost reports identically and labels as a self-reported result from the unified pipeline. Head to head against DeepSeek-V4-Flash-Base, it leads on 26.

The weaknesses are equally clear. On ChineseSimpleQA it scores 73.0 against DeepSeek 75.3, on HELMET-Rerank 56.2 against 60.0, and on InfiniteBench 46.5 against 48.0. All three are narrow gaps that read more like a close fight between same-generation models than a generational lead. Worth noting that the comparison targets the older V4 Flash base rather than the newer DeepSeek V4.1 Flash, and no outside lab has yet reproduced these numbers.

The license is the part worth reading most

The weights are genuinely free to download, but the ERNIE 5.5 Flash Base Model License Agreement v1.0 draws far tighter boundaries than the word open source usually implies. Territory is defined as all geographical areas worldwide other than the European Union, the United Kingdom, and South Korea, and Section 5.3 further bars using, reproducing, distributing, or displaying the materials outside that territory, outputs included.

Commercial use splits into two tiers. At or below US$50 million in annual revenue for related products, commercial use carries no model license charge, but requires prominent Powered by ERNIE branding plus a one-time use notice to Baidu. Above that threshold you need separate written commercial authorization before starting, and if revenue later crosses the line, affected commercial use lacking authorization has to be suspended until it takes effect, with Baidu able to request a written compliance statement, normally no more than once per calendar year. Section 5.5 adds another lock: you may not use the model to improve any other AI model.

So what

On the model side this is real engineering density. Long context, sparse MoE, and a speculative decoding draft model all fit inside a 280B-parameter frame, and it leads on nearly every head-to-head against the three comparable base models chosen for the comparison. On the distribution side, what you get is a half-open release with geographic exclusions and a revenue threshold. For teams working on open ecosystems, the base is clean, a single checkpoint, and convenient for comparing training methods side by side. For teams wanting to ship a service straight into Europe, this license has to be resolved legally before engineering even starts.

The real story may not be the base model at all, but the chat version that has not shipped yet. Baidu says the post-trained version is coming, and when it lands, this base plus post-training will be the actual answer on the ERNIE 5.5 line.