The concept of “tokens” in the context of models like GPT-4 refers to the basic units of text that the model processes. When we talk about GPT-4 “8k token” or “32k token,” we’re referring to the model’s capability to handle inputs and generate outputs within a limit of 8,000 or 32,000 tokens, respectively. This token limit impacts how much text the model can consider in a single prompt or generate in a single response.

Understanding Tokens

Tokens can be words, parts of words, or even punctuation marks, depending on how the model’s tokenizer breaks down the text. For instance, the sentence “AI is revolutionary” might be tokenized into [“AI”, “is”, “revolution”, “ary”] by a model’s tokenizer, resulting in four tokens.

The tokenizer’s approach to splitting text into tokens can vary, especially between languages and contexts. In English, common tokens include individual words, punctuation, and sometimes subwords or wordpieces for longer words not commonly found in the model’s training data.

Examples

Let’s illustrate what the 8k and 32k token limits might look like with examples:

8k Token Example

Imagine a comprehensive report on cloud computing trends, including sections on market analysis, technological advancements, future predictions, and case studies of successful deployments. If this report is detailed and includes numerous subsections, it could reach the 8k token limit. This limit would allow for an in-depth exploration of the topic, including detailed examples, technical descriptions, and possibly even appendices with additional data or code snippets.

32k Token Example

A 32k token document could be an entire short book or a detailed research paper covering multiple aspects of a complex subject like artificial intelligence ethics. This could include a literature review, methodology, results, discussion, and conclusions, along with extensive references and appendices. The 32k token limit allows for much longer narratives or analyses, enabling authors or researchers to delve deeply into their subjects, present comprehensive arguments, and include substantial evidence or data.

Visualization

To visualize the difference:

• An 8k token limit might cover a detailed blog post, a long-form article, or a brief technical report.
• A 32k token limit could encompass a series of articles, a short book, or an extensive research paper.

These examples show how the token limit of a model like GPT-4 affects the length and depth of content that can be generated or analyzed in a single interaction. This capability has significant implications for applications in content creation, summarization, conversation, and analysis, allowing users to tailor their use of the model to their specific needs, whether for concise summaries or in-depth explorations.

Can we say that 8k token approach can goes more to wide of topics but 32k can go more deeper?

Yes, that’s a concise and accurate way to distinguish between the 8k and 32k token approaches in GPT-4. The 8k token limit is more suited for covering a broader range of topics with less depth in each, enabling the discussion to span across various subjects or points within a single piece of content. It’s ideal for content that requires breadth over depth, such as summarizing multiple aspects of a topic, providing overviews, or addressing several questions or points in one go.

On the other hand, the 32k token limit allows for much deeper exploration of topics. With this extended capacity, you can delve into complex subjects with detailed analysis, comprehensive explanations, and intricate discussions. This makes the 32k token approach particularly valuable for in-depth research papers, extensive technical guides, long-form narratives, and detailed analysis or critiques that require significant elaboration.

In summary, the 8k token model excels in breadth, making it suitable for content that needs to cover a wide range of ideas or topics in a concise manner. The 32k token model, however, excels in depth, enabling detailed exploration of complex topics, thorough analysis, and extensive narrative or argument development. This distinction is crucial for tailoring your content strategy to the specific needs of your audience and the objectives of your blog post.