Pick the Right AI for YOU (Are They all The Same?!)

The speaker introduces GMTech, a platform for comparing various large language and image models. GMTech is accessible at gm.com and allows users to compare models from Open AI, Google, Anthropics, Meta Coherence, Amazon, and AI 21. It also includes image generation models like Stable Diffusion, Open AI, Amazon's, and Google's. The speaker notes the absence of Llama 3 and Cloud 3 Opus but appreciates the tool's user interface and experience. GMTech is a paid platform costing $15 per month, which covers the use of APIs from the compared platforms. The speaker also mentions a free month offer with a specific coupon code. The platform offers two options: comparing multiple AI models side by side or switching between models in a single chat interface. The speaker is particularly interested in the comparative feature and proceeds to test the creativity of different models using a prompt for business ideas.

The speaker discusses the results of using the GMTech platform to compare the creativity of various language models by providing them with a business ideas prompt. The models tested include Anthropic GPT, Llama 2, Gemini Pro, and Mistal Large. The speaker observes the formatting differences in the responses, noting that some models provided better-formatted answers. Response times and costs for each model are also compared. The speaker highlights the similarity in the ideas generated by the models and the overlap in their outputs. They also express the challenge of creating visually appealing content when discussing large language models and mention their struggle to find significant differences between the models in creative tasks. The speaker then tests the models' humor capabilities by asking them to tell a joke, noting that many provided the same joke, indicating a lack of diversity in their humor generation. Additionally, the speaker references an article about the prevalence of the number 42 in model responses and tests this by asking the models to pick a number between 1 and 100, observing that a majority chose 42.

The speaker explores GMTech's capabilities for comparing image generation models. They select several models and use a standard prompt of a wolf howling at the moon to compare the outputs. The speaker then presents a more complex prompt involving a three-headed dragon wearing cowboy boots, watching TV, and eating nachos to test how each model captures multiple elements in a single image. The results vary, with some models capturing all elements while others miss certain details. The speaker notes the cost associated with generating images through the platform and expresses their satisfaction with the tool's ability to compare both language and image models side by side. They also mention the absence of certain models like Mid Journey and Adobe Firefly, which are not yet available for testing in the tool.

The speaker reflects on the convergence of capabilities among large language models, noting that for most common use cases, such as creative writing, brainstorming, number picking, and joke telling, the outputs are quite similar across different models. They suggest that the choice of model may come down to factors like cost, ease of use, and API accessibility rather than the quality of outputs. The speaker predicts that models will continue to improve and become more comparable over time. They also share their thoughts on the challenges of creating comparison videos given the models' similarities and mention their intention to focus on single-topic videos based on community feedback. The speaker concludes by inviting viewers to explore GMTech and other AI tools and to subscribe to their newsletter for curated AI news and tools.