Gemini vs. OpenAI API Platform

Gemini is well suited to help track issues and change requests, projects, bugs, time logs, etc. It is less appropriate for reporting needs or general office management needs.

For smaller organizations that run lean and would like to get to deploy a solution quickly. This is a solution that is easy and quick to develop. It has a good amount of customization. However, for advanced customization this might not be a good solution. I suggest experimenting with OpenAI API and then if the experimentation is successful then it is a good idea to optimize and try other LLM models.

Incentivized

• It has been pretty good with rewrites and slight edits.
• It has been helpful in project management.
• It has been helpful with easy writing tasks like social media captions or holiday descriptions.

Incentivized

• The developer experience is top notch. Their SDKs are super easy to use
• Organization and project billing separation. You know where everything was consumed.
• Playground. The playground is super useful to prototype without writing a single line of code

Incentivized

• Speed of processing (on pro model)
• Accuracy of answers (on flash model)
• Veo 3 should be able to use picture prompts when creating videos (without errors)

Incentivized

• Restrictions are sometimes too strong

Incentivized

Gemini's development team continues to improve the product and provides a comprehensive roadmap of upcoming features that makes you want to upgrade as soon as a new version comes out.

Like learning to play checkers, the interface is easy, but the strategy has more of a learning curve. Figuring out the best prompts to use to get the desired outcome with less tries is taking me awhile to develop that skill. The images that I am able to generate are close to "camera-ready", but most do require some tweaking in image editing software to fix AI artifacts like distorted faces and randomly spelled words.

Incentivized

Easy to setup, develop and deploy. The payload for the API is simple and has all the inputs required for simple projects. There are a good number of options of LLM models to optimize for speed, cost or quality of the answers. A larger token input might improve the overall usability.

Incentivized

I've never had any problems with the support for the Gemini application itself, but it seems every time I re-install (on a single machine), I run into a licensing issue. As a result, I need to go to the app's website and request that my activation key gets reset or resent. In either case, it's a pain, but as I have to reinstall infrequently, it's a small price to pay for an otherwise solid application.

Incentivized

In my case, it's not that Gemini won; I simply use Gemini regularly as a backup plan to compare results obtained with the leading AI in my corporate environment. I believe it's important to have this comparison of results, especially when dealing with critical issues. I think the most powerful AIs for the general public today are ChatGPT and Gemini, in that order, although CoPilot is very well positioned due to its integration with widely used Microsoft products in the corporate world.

Incentivized

Anthropic is only the best for coding and its really really expensive. So, if you're not making a coding app, I would stay away from it. On the other hand, Gemini models are dirt cheap but come with a bit of performance limitations, so i would use it for big volume non sofisticated use cases. The OpenAI API platform excels at providing best in class performance models, at not outrageous anthropic-like pricing.

Incentivized

• It's sped up some workflows
• It's provided not accurate information sometimes
• It's a great tool when used in the GSuite with sheets and other drive functions

Incentivized

• Big question about functionality

Incentivized