Baidu Releases ERNIE 5.5 Flash Preview MoE Language Model

Introduction

On November 11, 2026, Baidu officially announced the release of ERNIE 5.5 Flash Preview via its ERNIE portal. This release marks a significant milestone in the ERNIE evolution, serving as the first post-trained model built upon the ERNIE 5.5 Flash Base checkpoint that was released to the community on October 21.

For developers and AI engineers, this model represents a strategic shift toward high-efficiency, high-throughput intelligence. While the previous ERNIE 5.1 Preview successfully topped the LMArena Text Leaderboard as the premier Chinese model, the 5.5 Flash Preview focuses on the practical demands of production environments: speed, massive context, and complex agentic execution.

• First post-trained iteration of the ERNIE 5.5 Flash architecture.
• Designed for high-speed, low-latency production inference.
• Optimized for agentic workflows and long-context reasoning.

Key Features & Architecture

The ERNIE 5.5 Flash Preview is built on a sophisticated Mixture-of-Experts (MoE) architecture. This design allows the model to maintain a massive knowledge base while remaining computationally efficient during inference. By utilizing a sparse activation strategy, the model achieves a balance between deep reasoning capabilities and rapid response times.

A standout technical specification is the model's ability to handle massive datasets in a single pass. With a 1-million-token context window, developers can feed entire codebases, lengthy technical manuals, or massive legal documents into the model without losing coherence or context. This makes it an ideal candidate for advanced RAG (Retrieval-Augmented Generation) systems and complex data analysis.

• Total Parameters: 280B
• Active Parameters per Token: 14B
• Context Window: 1,000,000 tokens
• Architecture: Sparse Mixture-of-Experts (MoE)
• Inference Speed: Reported above 200 tokens per second

Performance & Benchmarks

The ERNIE 5.5 Flash Preview demonstrates exceptional performance in specialized domains, particularly in coding and automated reasoning. Unlike standard chat models, this preview is fine-tuned for 'long-horizon' tasks—meaning it can plan, execute, and self-correct over multiple steps. This was evidenced by launch demos where the model operated within an agent harness to perform end-to-end tasks in game development and 3D modeling.

In rigorous benchmarking, the model has shown highly competitive results. It excels in automation and technical reasoning, outperforming many contemporary models on specialized evaluation sets. The performance gains are not just theoretical; the model's efficiency is further bolstered by optimizations like ERNIE Evolve, which has been shown to significantly increase throughput on Kunlunxin hardware.