The two major LLMs expose their functionality via API to be used in 3rd party products. ChatGPT API has been around for a while, but Google's Palm API, which taps into the Bard functionality, is just emerging in beta. This analysis aims to provide insights into the strengths and weaknesses of both, based on extensive research and hands-on experience.
Status: Currently in beta testing and free.
Bandwidth: Provides a much higher bandwidth at no cost during the beta phase. If you have a lot of prompts to run now, this may be the decisive factor in choosing your LLM API.
Speed: Appears to have greater computing resources and generally offers faster responses.
Accuracy: Tends to produce lower-quality responses in certain areas compared to ChatGPT. In other areas it seems to be better. So you really have to test and decide for yourself. I've created a comparative testing tool for the two LLM APIs, https://apiscout.ai - let me know what you think.
Geographical Restrictions: Primarily accessible in the US; other regions face limitations.
Cost: Can become costly if you have a lot of prompts to run.
Speed: Has faced some performance issues and outages, also it takes more time to produce an output than Google's solution.
Accuracy: A mixed bag, but it does seem to deliver more accurate responses in a number of areas. You will have to compare the two for your particular use.
Reliability: Currently more reliable for long-term projects as it's in live commercial use.
Geographical Reach: Globally accessible without heavy restrictions.
Performance and Cost: Palm API's lack of cost and high bandwidth can be enticing for projects requiring a significant number of prompts. ChatGPT's pricing structure can make it prohibitive for large-scale applications.
Reliability: Palm API's beta status implies uncertain longevity, potential pricing changes, and potential performance challenges in the future. In contrast, ChatGPT currently offers a more reliable solution for long-term projects.
Documentation Quality: Palm API's documentation, at present, seems inadequate, with some request parameters being particularly challenging to comprehend. This could signal broader issues with the project's maturity.
Geographical Considerations: Palm API has significant geographical restrictions. A US-based key cannot be used from a server in Canada, and non-US regions might face challenges in obtaining beta testing keys.
API vs. Web Access Discrepancies: It's important to note that there can be variations in the responses received from direct API access as opposed to web user interface access, even with identical parameters. As a solution, I've introduced a tool, https://apiscout.ai, which allows users to view side-by-side responses from both APIs while customizing parameters. The tool is especially handy for non-developers designing prompts.
While both APIs offer unique strengths, developers must weigh the pros and cons based on the specific requirements of their projects. The rapid development in the AI industry further underscores the importance of staying updated on these platforms.
Should you have any feedback, updates, or insights on the current state of LLM APIs, your input would be invaluable to the community.