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Model details

ERNIE 4.5 21B A3B

ERNIE 4.5 21B A3B is an open-source large language model from Baidu, with the Base variant specifically designed for text completion rather than chat-style interaction. Released under the Apache 2.0 license, the model is available on Hugging Face under the baidu organization, with a separate PyTorch ("-PT") weights distribution that uses Transformer-style architecture alongside an alternative PaddlePaddle variant. Baidu frames ERNIE (Enhanced Representation through kNowledge IntEgration) around a knowledge-enhanced approach aimed at robust performance across natural language tasks including sophisticated text generation, nuanced conversational applications, and comprehensive summarization. The ERNIE 4.5 family overall draws on Mixture-of-Experts techniques for its larger variants to scale capability efficiently.

The practical appeal of ERNIE 4.5 21B A3B lies in its accessibility and deployment flexibility. Because weights are openly published, developers can self-host the model on cloud GPU infrastructure with one-click setups, and DigitalOcean's hands-on testing reported the model outperforming comparable models like Qwen3 on translation workloads. The Base variant is intended for completion-style use, with vLLM and FastDeploy recommended for evaluation, making it well suited to teams building text generation, summarization, translation, or knowledge-grounded pipelines who want an open-weight alternative to closed APIs. Its combination of permissive licensing, a long context window, and a knowledge-enhanced design philosophy positions it as a practical option for organizations seeking customizable natural language processing without vendor lock-in.

NovitaAIbaidu/ernie-4.5-21B-a3bernie

Quick Info

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Provider
NovitaAI
Model key
baidu/ernie-4.5-21B-a3b
Release date
Jun 30, 2025
Last updated
Jun 30, 2025
Knowledge cutoff
2025-03
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$0.07
Output token cost
$0.28

Limits

Output tokens
8,000 tokens
Context window
120,000 tokens

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