Beginner's Guide to Large AI Models: How ChatGPT Works and How It Compares to Chinese Open-Source Models

The Evolution of Large AI Models: From Keyword Matching to Intelligent Conversation

Before ChatGPT arrived, chatbots like Microsoft Xiaoice were essentially keyword-matching systems. These systems had obvious limitations: identical questions always returned identical answers, there was no memory of context, and there was no real understanding behind the responses.

In late 2022, OpenAI's ChatGPT changed the game with three breakthrough capabilities:

• Extended context comprehension: Remembers conversation history, understands pronoun references, and enables deep multi-turn dialogue
• Proactive questioning and logical reasoning: Not only answers questions but also asks users clarifying questions and grasps underlying logic
• Natural, fluent language expression: Passes the Turing test, making it difficult to tell whether you're talking to a human or a machine

ChatGPT's free-access strategy accelerated its adoption — users began using it to get answers, fuel creative projects, and even as an emotional outlet. This marked the beginning of AI's practical era.

What Makes a Model "Large"? Two Core Dimensions

The word "large" in large language models refers to two distinct dimensions:

Massive Training Data

Take GPT-3.5 as an example: its training data covers virtually all publicly available content on the internet, plus digitized human literature and publications. This scale of data ingestion gives large models an almost encyclopedic breadth of knowledge.

Enormous Parameter Count

Parameters can be thought of as the storage units of knowledge — the more parameters a model has, the richer the knowledge it can store and retrieve. This is the technical foundation that allows large models to answer such a wide variety of questions accurately.

The Rise of Chinese Open-Source Large Models

As of now, the total number of open-source large models worldwide has reached 289, and China has contributed a significant share of high-quality entries. Data from HuggingFace shows that China has risen to the top globally in open-source large model contributions. Notable Chinese open-source models include:

• Zhipu GLM series: Open-sourced by a Tsinghua University team and operated by Zhipu AI
• Alibaba Qwen (Tongyi Qianwen): A multimodal large model under Alibaba Cloud
• DeepSeek: An open-source model from DeepSeek Inc. that broke industry pricing barriers with extremely low API costs
• Kimi (Moonshot AI): Known for its exceptional long-context processing capabilities
• Xiaomi MIMO: A high-performance model developed by Xiaomi's internet team

DeepSeek deserves special mention. The company not only open-sourced high-quality models and allowed developers to self-host them, but also slashed remote API pricing to a fraction of what competitors charged — truly democratizing large model access. Its founder, Liang Wenfeng, even met with national leadership, a testament to the company's outsized influence.

How Large Models Actually Work: Probability Prediction, Not True Understanding

Many users mistakenly believe that large models genuinely "know everything." In reality, the output of a large model is fundamentally probabilistic prediction:

1. Interpret the input: Perform semantic analysis on the user's question
2. Retrieve from the knowledge base: Search training data for relevant information (some models also support real-time web search)
3. Generate token by token: Predict and output the most statistically likely words one at a time

Why AI Hallucinations Happen

This mechanism is the root cause of "AI hallucinations." Early ChatGPT frequently produced fabrications — inventing events that never happened or attributing statements to the wrong people. Two main factors drive this:

• Limits of probabilistic prediction: The model doesn't truly understand content; it generates text based on statistical patterns
• Training data has a cutoff: Training large models takes months to over a year and costs millions of dollars. Once training is complete, the knowledge base is frozen and cannot be updated in real time.

For this reason, you should always approach outputs from large models with caution when dealing with rigorous domains like law or medicine — never take their answers at face value.

Understanding Token-Based Pricing

AI services are typically billed by the token. A token is the smallest unit of text computation, but it doesn't map neatly to "one Chinese character = one token" or "one English word = one token."

Using Doubao's online token calculator as an example, a passage of Chinese text gets split into multiple tokens in a way similar to Chinese word segmentation. For instance, "推理能力大大增强" might be tokenized as: 推理 (1 token) + 能力 (1 token) + 大大 (1 token) + 增强 (1 token).

Current Mainstream Pricing

• Approximately 2–3 RMB per million tokens (representative of Chinese models like DeepSeek)
• Compared to early OpenAI pricing, Chinese large models have compressed costs by tens of times

This means everyday conversation costs almost nothing. An 81-token input costs less than 0.0002 RMB — essentially negligible. These low costs make it perfectly practical for developers to integrate large model APIs into their applications without worrying about runaway bills.

Large Models in Cybersecurity: A Look at the Potential

The capabilities of large models extend well beyond conversation and content creation — they show tremendous promise in cybersecurity as well:

• Automated penetration testing: Using AI agents to conduct intelligent security assessments
• Code auditing: Rapidly identifying security vulnerabilities in source code
• Threat intelligence analysis: Processing and correlating massive volumes of security logs
• Security knowledge Q&A: Providing technical support to security professionals

As Chinese open-source large models mature and API costs continue to fall, integrating large model capabilities into security toolchains has become both technically feasible and economically viable. For those looking to break into the security industry from scratch, developing a combined skill set in AI and security will become a meaningful competitive advantage.