What are Token Limits?

What are Token Limits? A Comparative Analysis of Top Large Language Model

August 3, 2023

LLMs are characterized by three main parameters:

  1. Accuracy

  2. Efficiency

  3. Token limits

While the first two are quite intuitive, token limits demand a discussion.

What are token limits?

Token limits are restrictions on the number of tokens that an LLM can process in a single interaction. A token is a unit of text that is used to represent a word, phrase, or other piece of text. For example, the phrase "I love you" would consist of 5 tokens: "I", "love", "you", ".", and " ".

Every generative LLM has what is commonly referred to as a 'maximum token limit' or 'context limit'. Token limits represent the maximum amount of text that can be inputted into the AI model. This limit is of a technical nature and arises due to computational constraints, such as memory and processing resources.

Why are token limits relevant?

Token limits are relevant because they can affect the performance of LLMs. If the token limit is too low, the LLM may not be able to generate the desired output.

For example, if you are trying to generate a 1000-word document but the token limit is 1000, the LLM will only be able to generate the first 1000 tokens.

Whereas if itโ€™s too high, the LLM is going to be very slow and require very high computational power.

Can we bypass token limits?

There are a few ways to do this:

  1. Break down your input into smaller chunks. This will help you stay within the token limit.

  2. Use a tokenizer to count the number of tokens in your input. This will help you to make sure that you are not exceeding the token limit.

If you are want to find out more about LLMs, you can click on this link for more details.

Comparison of Various Large Language Models (LLMs) & Their Capabilities

1. GPT4: GPT4 is a powerful language model with a token limit of 32,768 and an estimated word count of 25,000 words. It excels in complex reasoning and creative tasks, making it ideal for advanced language processing applications. However, its main weakness lies in its slow processing speed. GPT4 lacks the ability to understand images, run code, or read files. It has an impressive parameter count of 1.7 trillion, indicating its complexity. While not open-source, GPT4 is available for use at a reasonable price of $20/month, with training data from September 2021. It supports multimodal inputs, including text and images.

2. GPT3.5: GPT3.5 is a language model with a token limit of 4,096 and an estimated word count of 3,083 words. It is suitable for tasks involving simple reasoning and providing fast answers. However, GPT3.5 can get stuck in reasoning loops and lacks current-world data. Similar to GPT4, it doesn't understand images, run code, or read files. With 175 billion parameters, it is a capable model, available for free since September 2021, but lacks fine-tuneability. GPT3.5 is unimodal and supports text inputs only.

3. Llama2: Llama2 is a language model designed for natural language processing and better comprehension of conversations. It has a token limit of 2,048 and an estimated word count of 1,563 words. While it performs well in language tasks, it may struggle with coding-related tasks. Unlike the previous models, Llama2 can understand images, but it doesn't have the capability to run code or read files. With 70 billion parameters, it offers some fine-tuneability and is open-source. Llama2 is available for free and has training data from September 2022. It supports multimodal inputs, including images, text, and audio.

4. Claude 2: Claude 2 is a language model with an impressive token limit of 100,000 and an estimated word count of 60,000 words. It boasts stronger reasoning capabilities compared to GPT3.5, making it suitable for tasks requiring complex logic. However, Claude 2 has the downside of occasionally hallucinating and is currently available only in the US and UK. Like the previous models, it lacks the ability to understand images, run code, or read files. With 860 million parameters, it is less complex than some other models. Claude 2 is free to use and its training data is from early 2023. It is designed for unimodal text inputs.

5. PaLM: PaLM is a high-efficiency language model with a token limit of 8,000 and an estimated word count of 6,200 words. It outperforms GPT3 in certain reasoning tasks, making it valuable for specific language processing applications. However, PaLM has limited memory and token capacity, making it unsuitable for handling extensive information. Unlike the previous models, it can understand images, run code, and read files. With 540 billion parameters, PaLM offers significant fine-tuneability. It is open-source and free to use, with training data from mid-2021. PaLM supports multimodal inputs, including images, text, and audio.

Index of column headers in the above Tables

Estimated Word Count:

The estimated word count refers to the approximate number of words that can fit within the token limit or character limit of an AI language model. It is essential to consider this limit when using the model for text generation or processing, as input text that exceeds the limit may need to be truncated or split.

Best Use-Cases:

AI language models are widely used for natural language processing tasks, including but not limited to chatbots, language translation, text summarization, sentiment analysis, content generation, question answering, and language understanding tasks like sentiment analysis.

Weaknesses:

AI language models have several weaknesses, including the potential to generate plausible but incorrect responses, sensitivity to input phrasing, and susceptibility to bias learned from training data. They may also struggle with context retention beyond their token limit and may not have a true understanding of the meaning of the text.

Parameters:

Parameters in an AI language model are the variables that store its knowledge. Larger models with more parameters tend to perform better but also require more computational resources for training and inference.

Fine Tuneability:

Fine tuneability is the capability of an AI language model to be further trained on specific data or tasks. Fine-tuning allows users to customize the model's behavior for more domain-specific or specialized use-cases.

Pricing:

AI language models are typically offered under various pricing plans, which may include free tiers for limited usage and paid subscriptions based on usage volume or additional features. The pricing structure can vary among different AI service providers.

Training Data Date:

The training data date indicates when the AI model's training data was last updated. More recent training data can help the model stay up-to-date with current trends and events, making it more relevant for certain applications.

Open Source:

Some AI language models are open-source, meaning their architecture and training data are publicly available for research, development, and customization. Open-source models promote transparency and encourage collaboration within the AI community.

Multimodal Capability:

Multimodal AI models can process and generate information from multiple modalities, such as text, images, and even audio. These models are valuable for tasks that involve both textual and visual content, enabling more sophisticated and context-aware applications.

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