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Select Appropriate LLM

This document is designed to guide you in selecting an appropriate model, in order to enhance the response quality of Vanus AI based on your application scenarios.

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For differences among different models, please refer to Large Language Models. This guide is for reference only.

Differences between ChatGPT 3.5, Azure GPT-3.5 and ChatGPT-4

ChatGPT 3.5 and ChatGPT-4 are large language models developed by OpenAI, but they differ in terms of functionality and capacity:

ModelDescriptionAdvantages
ChatGPT 3.5A versatile model capable of answering questions, engaging in deep discussions, drafting emails, writing code, and generating written content.Its training involves reinforcement learning from human feedback, enabling it to adapt and respond more specifically to dialogue context.
Azure GPT-3.5Essentially identical to ChatGPT-3.5, but offered through the Azure platform./
ChatGPT-4OpenAI's most advanced system, capable of producing safer and more useful responses.GPT-4 scores higher among testers than ChatGPT 3.5. It can tackle harder problems with greater accuracy, thanks to its broader general knowledge and problem-solving capabilities. Creatively, GPT-4 is more inventive and collaborative than ever. When handling long texts, GPT-4 can manage texts with over 25,000 words, making it suitable for creating long-form content, extending conversations, and document search and analysis.

1. Capability Differences:

GPT-4, the latest and most advanced large language model from OpenAI, boasts a wider range of general knowledge and stronger problem-solving abilities than ChatGPT 3.5, while capable of answering questions and generating content, falls short when it comes to complex queries and extensive content.

2. Innovation:

GPT-4 excels in the realm of innovation, demonstrating a superior ability to understand and generate innovative content, such as storytelling or code writing. In comparison, ChatGPT 3.5 may not match GPT-4's performance in this area.

3. Handling Long Texts:

GPT-4 can process texts exceeding 25,000 words, making it suitable for creating long-form content, extending conversations, and conducting document search and analysis. On the other hand, ChatGPT 3.5 might face constraints when dealing with lengthy content.

While the cost in message credits for ChatGPT-4 is higher, it can produce more accurate and context-aware responses, Pricing- Frequently Asked Questions can address your queries.

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If you need high-quality responses, the ChatGPT-4 model is recommended. This model excels in language skills, knowledge capacity, reasoning abilities, and understanding.

Differences between ERNIE Bot and ERNIE Bot Turbo

ERNIE Bot and ERNIE Bot Turbo are large language models developed by Baidu. Both can answer a variety of questions and provide users with information and assistance. However, some differences exist between the two:

ModelDescriptionAdvantages
ERNIE BotThe latest AI technology developed by Baidu, capable of interactive dialogues, answering questions, and assisting in content creation.It has strong language understanding and generation capabilities. Supports multiple natural languages, including Chinese and English.
ERNIE Bot TurboAn improved version of ERNIE Bot, with faster response and processing speed, and more accurate answers.Compared to ERNIE Bot, ERNIE Bot Turbo has stronger accuracy in answering, processing speed, and learning capabilities.
  1. ERNIE Bot Turbo has much faster response and processing speed than ERNIE Bot.

  2. ERNIE Bot Turbo is more accurate in answering content than ERNIE Bot.

  3. Learning capabilities: ERNIE Bot Turbo has stronger learning abilities, capable of updating its knowledge automatically after continuous learning, while ERNIE Bot needs to be updated manually.

  4. Usage: ERNIE Bot Turbo supports more usage methods, satisfying users' various needs in different scenarios. ERNIE Bot is more suitable for fixed scenarios and usage methods.

Generally speaking, ERNIE Bot Turbo outperforms ERNIE Bot in terms of processing speed, answer accuracy, learning capabilities, and usage methods. However, both have their specific application scenarios, and which model to choose depends on your usage needs and actual test results.

As for which large model to choose, it depends on your usage needs and actual test results. Each model has its unique advantages and specific application scenarios. We recommend that you select the most suitable model based on your usage needs and actual test results.