Same LLaMA 4 Models, 5 Providers—Very Different Results!
Updated: April 26, 2025
Summary
The video showcases a comparison of different API providers hosting the same Lama 4 models for Scout, testing them with a complex animation prompt. The speaker tests Grock, Open Router, Maverick, and Together AI providers, evaluating aspects like token output limits, generation speed, code quality, and output performance. The importance of selecting the right provider based on factors like context window, output requirements, and price points is emphasized throughout the analysis, shedding light on differences in performance and limitations among the providers.
Testing Different API Providers
The speaker tests the new Lama for Scout on five different providers with the same prompt to compare the performance. Multiple API providers host the same Lama 4 models, and the question is whether they provide the same level of performance or if there are differences.
Creating HTML Animation
The speaker describes creating a complex animation with a ball bouncing within a hexagon in HTML. Specific instructions include the ball's radius, colors, rotation, and movement within the hexagon.
Testing Grock Provider
Testing the Grock provider with specific settings such as token output limits and generation speed. Issues with quality and limitations due to token constraints are highlighted.
Testing Open Router Provider
Testing the Open Router provider with default hyperparameters and constraints. Observations on token output and limitations are discussed.
Testing Maverick Provider
Testing the Maverick provider with auto temperature settings and analyzing the generated output. Comparison of token generation speed and code quality is made.
Testing Together AI Provider
Testing the Together AI provider and analyzing the token generation speed and output quality. Comparison with other providers is highlighted.
Comparison of Inference Providers
Comparing different API providers hosting the same model based on output speeds, price points, and performance. The importance of selecting the right provider based on context window and output requirements is emphasized.
FAQ
Q: What is the purpose of testing the new Lama on five different providers with the same prompt?
A: The purpose is to compare the performance of the Lama 4 models across different API providers to determine if they provide consistent levels of performance or if there are variations.
Q: Can you explain what nuclear fusion is?
A: Nuclear fusion is the process by which two light atomic nuclei combine to form a single heavier one while releasing massive amounts of energy.
Q: What specific instructions were provided for creating a complex animation with a ball bouncing within a hexagon in HTML?
A: Instructions included details about the ball's radius, colors, rotation, and movement within the hexagon.
Q: What settings were used when testing the Grock provider, and what issues were highlighted?
A: Specific settings such as token output limits and generation speed were used. The issues highlighted included quality concerns and limitations due to token constraints.
Q: What were the default hyperparameters and constraints when testing the Open Router provider, and what observations were made?
A: Default hyperparameters and constraints were used. Observations included discussions on token output and limitations encountered.
Q: How was the Maverick provider tested, and what aspects were analyzed?
A: The Maverick provider was tested with auto temperature settings, and the generated output was analyzed. Comparison was made on token generation speed and code quality.
Q: In testing the Together AI provider, what aspects were analyzed, and how was it compared with other providers?
A: The token generation speed and output quality were analyzed. Comparison was made with other providers based on these aspects.
Q: What factors were considered when comparing different API providers hosting the same model?
A: Output speeds, price points, and overall performance were considered in the comparison of different API providers hosting the same model.
Q: Why is it important to select the right provider based on context window and output requirements?
A: It is important to ensure that the selected provider can meet the specific context window and output requirements of the task to achieve optimal performance.
Get your own AI Agent Today
Thousands of businesses worldwide are using Chaindesk Generative
AI platform.
Don't get left behind - start building your
own custom AI chatbot now!