ChatGPT 4 often boasts superior translation capabilities and nuanced language understanding compared to its predecessor, 3.5. However, anecdotal evidence suggests a significant performance disparity when it comes to coding tasks. While GPT-4 excels at building upon a train of thought and problem-solving, GPT-4 often falters, ignoring instructions, repeating ineffective suggestions, and struggling to maintain conversational context.
GPT-4 vs. 3.5 in Coding: A User’s Perspective
Extensive daily use of both the ChatGPT API and chat interface across various tasks like translation and coding reveals a clear trend: GPT-4 struggles with complex coding problems where 3.5 often shines. GPT-4 frequently hits dead ends, necessitating a switch back to GPT-4 or even 3.5 for successful resolution. Surprisingly, GPT-3.5 often provides more insightful and relevant suggestions, even when a perfect solution remains elusive.
GPT-4’s Shortcomings: Ignoring Instructions and Context
Beyond coding, GPT-4 exhibits frustrating behaviors like disregarding explicit instructions, refusing to explore alternative approaches, and repetitively offering rejected solutions. Its “memory” feature, while intended to enhance context retention, proves unreliable, often failing to adhere to user-defined parameters even after acknowledging them. This contrasts sharply with GPT-3.5’s generally better adherence to instructions and conversational history.
GPT-4’s Strengths: Translation and Nuance
Despite its coding struggles, GPT-4 demonstrates marked improvement in translation quality over 3.5 and previous iterations. Both the API and chat interface consistently deliver more accurate and nuanced translations, showcasing progress in language understanding. This suggests a trade-off: enhanced language processing might come at the expense of certain logical reasoning abilities crucial for coding.
Conclusion: A Mixed Bag
Ultimately, the comparison between ChatGPT 4 and 3.5 reveals a complex picture. GPT-4 excels in translation and nuanced language tasks but falls short in coding scenarios where 3.5 often proves more effective. The issues with GPT-4’s instruction following and context retention raise concerns about its reliability for certain complex tasks. While acknowledging the anecdotal nature of these observations, the significant performance differences warrant attention as developers continue refining future iterations.
