GPT-3.4 VS GPT-4

The Evolution of AI Language Models


10/20/20233 min read

a computer keyboard with a green logo on it
a computer keyboard with a green logo on it

The GPT Series: A Fast Outline

Prior to jumping into the examination, we should momentarily return to what the GPT (Generative Pre-prepared Transformer) series is about:

GPT-3: GPT-3, the third cycle, stood out as truly newsworthy for its dumbfounding exhibition in producing human-like text. It had 175 billion boundaries and set new guidelines for language models.

GPT-3.5: While not an authority OpenAI discharge, GPT-3.5 addresses a sensible move toward the movement of man-made intelligence models. It's an envisioned model with considerably a bigger number of boundaries than GPT-3, intended to grandstand the potential outcomes of bigger scope models.

GPT-4: GPT-4 is the fourth authority cycle in the series, meaning to expand on the triumphs and restrictions of GPT-3


Man-made reasoning has been advancing at an astounding speed lately, with regular language handling (NLP) models driving the charge. Two of the main improvements in this field are GPT-3.5 and GPT-4. These models, created by OpenAI, address the forefront of language understanding and age. In this blog, we will thoroughly analyze GPT-3.5 and GPT-4 to reveal insight into the development of artificial intelligence language models.

Size Matters: Parameters and Scale

One of the most significant differences between GPT-3.5 and GPT-4 is the scale. GPT-3 had 175 billion parameters, while GPT-3.5 was hypothetically imagined to have even more. GPT-4, on the other hand, is an official release with over 1 trillion parameters, significantly outscaling its predecessors. This substantial increase in scale allows GPT-4 to handle more complex tasks and generate even more human-like text.

Performance and Capabilities

With its massive scale, GPT-4 offers several advantages over GPT-3.5:

  1. Improved Understanding: GPT-4 can comprehend and generate more nuanced and context-aware responses, making it more useful in a broader range of applications.

  2. Few-Shot and Zero-Shot Learning: GPT-4 exhibits enhanced few-shot and zero-shot learning capabilities. Few-shot learning allows the model to learn from a small amount of data, and zero-shot learning enables it to perform tasks it hasn't explicitly been trained on.

  3. Multimodal Integration: GPT-4 can handle text and other modalities, such as images and audio, more effectively. This makes it a versatile tool for various applications, from content generation to content analysis.

  4. Reduced Bias: Efforts have been made to reduce biases in GPT-4, addressing concerns about biases present in earlier models like GPT-3.

  5. Fine-Tuning: GPT-4 is more adaptable and customizable through fine-tuning, enabling developers to create more specialized applications.

Challenges and Ethical Considerations

While GPT-4 brings notable advancements, it also raises significant concerns:

  1. Environmental Impact: Larger models like GPT-4 require vast computational resources, leading to increased energy consumption and environmental impact.

  2. Safety and Misuse: As AI models become more powerful, concerns about their potential misuse for malicious purposes, including generating deepfakes or disinformation, become more prominent.

  3. Bias and Fairness: Although OpenAI has worked on reducing bias, GPT-4 may still exhibit biases in certain contexts, which could have ethical and social implications.

  4. Data Privacy: Generating human-like text at this scale raises concerns about data privacy, as the model can inadvertently reveal sensitive information or create content that invades privacy.


GPT-4 addresses a critical jump forward in the realm of simulated intelligence language models, offering further developed capacities and tending to a portion of the constraints of prior models. Its scale, barely any shot learning, multimodal reconciliation, and diminished predisposition make it a useful asset for a great many applications.

Nonetheless, the organization and utilization of such huge scope models additionally accompany moral and ecological difficulties that should be tended to. As simulated intelligence innovation keeps on developing, it's significant to work out some kind of harmony among advancement and obligation, guaranteeing that these amazing assets are utilized to help society while limiting possible mischief.

Eventually, GPT-4 is a demonstration of the fast movement of simulated intelligence, and it highlights the significance of continuous conversations about morals, predisposition, security, and dependable man-made intelligence improvement. As we push ahead, it's vital for saddle the capability of simulated intelligence models like GPT-4 while being watchful about the difficulties they present.