ERNIE
Description
️ 🖼Tool Name:
ERNIE
🔖 Tool Category:
Large Language Models & Vision-Language Models based on Mixture-of-Experts (MoE) structure
️ ✏What does this tool offer?
High-performance text and visual models for understanding language, images, and video.
Advanced textual and visual thinking capabilities (Thinking Mode / Non-Thinking Mode).
Extract and analyze data from text, images, tables, and graphs.
Efficient training and deployment using tools such as ERNIEKit and FastDeploy.
Support for model compression (Quantization) to reduce size while maintaining accuracy.
⭐ What does the tool actually deliver based on user experience?
Superiority in benchmark tests such as BBH, CMATH, IFEval, Multi-IF, SimpleQA.
Visual models (ERNIE-4.5-VL) offer strong performance in visual perception and mathematical reasoning.
Smaller models (e.g. 21B and 0.3B) achieve results comparable to larger models such as Qwen3-30B.
Flexibility in use: From giant models (300B and 424B) to lightweight models for everyday applications.
🤖 Does it include automation?
It does not act as a standalone Agent, but provides a kernel that can be integrated into automated intelligent systems via APIs and integration tools.
💰 Pricing Model:
Open source under the Apache-2.0 license, completely free.
Fees only when used via cloud providers (e.g. Novita AI).
🆓 Free Plan Details:
Available for free via GitHub, Hugging Face, ModelScope, and Aistudio.
Training and deployment tools (ERNIEKit and FastDeploy) are open source at no cost.
💳 Paid Plan Details:
There are no paid plans from Baidu or PaddlePaddle.
When used via cloud services such as Novita AI:
ERNIE-4.5-VL-424B-A47B: About $0.42 per million input tokens and $1.25 per million output tokens.
ERNIE-4.5-300B-A47B: About $0.30 per million input tokens and $1 per million output tokens.
Smaller models are often free within certain contextual limits.
🧭 Access Method:
Via GitHub (official repository).
Via Hugging Face and ModelScope to download ready-made models.
Via ERNIEKit for training and FastDeploy for deployment.
Can be run locally with PaddlePaddle or PyTorch.
🔗 Experience Link:
https://github.com/PaddlePaddle/ERNIE?utm_source=