On November 5 Baidu (official blog + @BaiduResearch) open-sourced ERNIE-5.5-Flash-Base: a text-only MoE base checkpoint at end of pre-training (no mid/post-training), 280B total / 14B active per token, 33.5T pretrain tokens, up to 128K train context and 1M for fine-tune/inference. FP8 and BF16 weights are on Hugging Face and ModelScope. Under Baidu's unified eval pipeline vs similar bases: MMLU 93.3, HumanEval+ 82.3, LiveCodeBench 40.1 (vendor-reported). Post-trained ERNIE 5.5 Flash "will follow". License is custom (excludes EU/UK/South Korea; commercial use above $50M related revenue needs prior written authorization).

Key Takeaways

  • ✓Nov 5 open base at end of pre-training: 280B total / 14B active — not a chat/instruct model
  • ✓33.5T pretrain tokens; up to 128K train context, 1M for fine-tune and inference
  • ✓Vendor unified eval: MMLU 93.3, HumanEval+ 82.3, LiveCodeBench 40.1 vs similar bases
  • ✓Architecture: 37 HCA + 6 MLA-DSA layers; Multi-Router Latent MoE (512 routed top-10 + 1 shared)
  • ✓Custom license (not Apache): excludes EU/UK/South Korea; >$50M related revenue needs Baidu written authorization
Baidu Opens ERNIE-5.5-Flash-Base: 280B-A14B MoE, 33.5T Pretrain, Vendor-Reported MMLU 93.3
🖼️Official Media
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In-Depth Technical Analysis

Core Background & Industry Pain Points

Many open drops are post-trained chat weights, so continued pre-training is hard to control. On November 5 Baidu released ERNIE-5.5-Flash-Base as an end-of-pretraining text base for domain adaptation, instruction tuning and RL. The post-trained ERNIE 5.5 Flash "will follow"; this is not a chat product.

Architecture Highlights & Internals

280B total / 14B active per token; 43 layers (1 dense + 42 MoE) with 512 routed experts (top-10) plus 1 shared. Hybrid attention: 37 HCA layers (one cache entry per 128 tokens) and 6 MLA+DSA layers, plus Multi-Router, VHA and ECHO. FP8 and BF16 weights ship with SGLang/vLLM examples.

Authoritative Benchmarks & Measured Scores

Vendor-reported results under Baidu's unified harness versus similar bases: MMLU 93.3, MMLU-Pro 78.4; HumanEval+ 82.3, LiveCodeBench 40.1; RepoQA 91.7, LongBench-v2 48.7. No independent third-party replication is claimed here; do not invent extra scores.

Developer Hands-on Guide

Get weights from Hugging Face baidu/ERNIE-5.5-Flash-Base, ModelScope and the ERNIE blog. Serve via completions with BOS (add_special_tokens=True). Custom license excludes the EU, UK and South Korea; related revenue above $50M needs prior written authorization. Read LICENSE before deploy.

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