I tested the new LLaMA for Scout model on five different providers using the same prompt. The results were quite surprising. There are multiple API providers hosting the same LLaMA 4 models at different price points, but the question is whether they provide the same level of performance or if there are other differences.

I used the same prompt across the five providers: Meta AI, Open Router, Grock, Together AI, and Fireworks AI. The prompt involved creating an HTML program that simulates 20 balls bouncing inside a spinning heptagon, with various requirements around the ball behavior and heptagon rotation.

The key findings from my testing:

• Meta AI had a limitation on the maximum number of tokens it could generate for this prompt, which was a significant issue.
• Open Router provided two versions of the generated code, with the "improved" version still having issues with the heptagon and ball movements.
• Grock was extremely fast, generating the output in just 3 seconds, but the resulting code had the same issues as the other providers.
• Together AI generated a lot of extraneous text along with the code, and the code itself had problems with the heptagon and ball behavior.
• Fireworks AI also struggled to generate a working solution, with the balls simply falling out of the heptagon.

I then tested the larger LLaMA for Maverick model on some of the providers (Open Router, Together AI, Fireworks AI) using the same prompt. The results were somewhat better, with the Maverick model producing more realistic ball and heptagon movements, but there were still issues with the output.

Based on these tests, a few key takeaways:

1. There seem to be differences in how the same LLaMA 4 models are implemented and hosted by different API providers, which can impact the performance and quality of the generated output.
2. It's important to thoroughly test any LLaMA 4 model on your specific use cases and prompts across multiple providers to find the one that works best.
3. The LLaMA for Maverick model generally performed better than the Scout model, but there is still room for improvement in the model's capabilities.
4. Pay close attention to the maximum context window and output token limits provided by each API provider, as this can significantly impact the usability of the model for certain tasks.

Overall, these tests highlight the importance of independent benchmarking and testing when working with large language models like LLaMA 4, as the performance can vary across different hosting and inference providers.

I tested the new Llama for Scout on five different providers with the same prompt, and the results were really surprising. There are multiple different API providers hosting the same Llama 4 models at different price points, but the question is whether they provide the same level of performance or if there's something else going on.

I used the same prompt on five different providers: the official Meta, Hosting, Open Router, Grock, Together AI, and Fireworks. The prompt asked the model to write an HTML program that shows 20 balls bouncing inside a spinning heptagon, with specific requirements for the ball behavior, colors, and heptagon rotation.

The results were mixed across the different providers. Some were able to generate the initial code, but struggled with the realistic bouncing and rotation of the heptagon. Others had issues with the heptagon shape or the ball movements. The speed of generation also varied, with Grock being the fastest at around 500 tokens per second, while Together AI was the slowest at around 100 tokens per second.

To further investigate, I also tested the larger Llama for Maverick model on some of the providers. The results were generally better, with the balls bouncing and the heptagon rotating more realistically. However, there were still some issues with the ball movements and the heptagon shape.

Overall, the performance of the Llama 4 models seems to vary across different API providers, even when using the same prompt and hyperparameters. It's important to test multiple providers and benchmark the models on your specific use cases to find the best fit for your needs.

The results from testing the Llama 4 models across different API providers reveal some interesting differences in their behavior and performance. Despite using the same underlying model, the outputs generated by the various providers varied significantly.

One key observation is that the providers seem to have different configurations or optimizations for the models, which can impact the quality and consistency of the generated output. For example, some providers were able to generate the desired spinning heptagon with bouncing balls, while others struggled to maintain the correct geometric shape and ball movement.

The speed of generation also varied widely, with Grock being the fastest at around 500 tokens per second, while Together AI and Fireworks AI were significantly slower at around 100 tokens per second. This suggests that the hardware and infrastructure used by the providers can have a substantial impact on the model's performance.

Another notable difference was the providers' handling of the output length limitations. While some, like Meta AI, hit their token limit and were unable to complete the full code, others like Open Router provided multiple versions of the code, showcasing some level of reasoning and optimization capabilities.

The results also highlight the importance of independent benchmarking and testing when selecting a model or provider for a specific use case. The academic benchmarks, while informative, may not always align with the real-world performance of the models. It's crucial to evaluate the models on your own datasets and requirements to ensure they meet your needs.

Furthermore, the differences in the context window and output token limits across the providers underscore the need to carefully consider these technical specifications when choosing an API provider. A model with a large context window may not be fully utilized if the provider imposes a low output token limit.

In summary, the variations observed in the Llama 4 model's behavior across different API providers emphasize the importance of thorough testing and evaluation when selecting a model and provider for your specific use case. The performance and capabilities of these large language models can be heavily influenced by the infrastructure and configurations used by the hosting providers.

Based on the transcript, it appears that the Maverick model, part of the Llama 4 series, may be a more capable performer compared to the Scout model. Some key points:

• The independent benchmarking shows that the Maverick model ranks higher than the Scout model in overall intelligence and performance, though the individual benchmark results can vary.
• The Maverick model has a larger context window of 1 million tokens, compared to the Scout model's 10 million tokens. However, the availability of API providers hosting the models with such large context windows is limited.
• When testing the same prompt on various API providers, the Maverick model generally performed better than the Scout model, with fewer issues and more consistent output.
• The availability of Maverick model hosting is better than the Scout model, with some providers offering up to 1 million output tokens, which is more suitable for tasks like coding.

In summary, the Maverick model appears to be a more capable and reliable performer compared to the Scout model, based on the independent benchmarks and the author's own testing. However, the availability and configuration of the models across different API providers can still vary, so it's important to test and evaluate the models based on your specific use case and requirements.

While benchmark scores can provide valuable insights, it's important to consider their limitations and approach them with nuance. Benchmark results are often influenced by the specific tasks and datasets used, and may not fully capture a model's overall capabilities or suitability for real-world applications.

When analyzing benchmark scores, it's crucial to:

1. Understand the Benchmarks: Examine the individual benchmarks and their specific focus areas. Different benchmarks may prioritize different aspects of model performance, such as reasoning, language understanding, or task-specific capabilities.

2. Consider Context and Limitations: Recognize that benchmark scores are not the sole determinant of a model's quality or suitability. Factors like model size, training data, and intended use case should also be taken into account.

3. Perform Independent Evaluations: Complement benchmark scores with your own testing and evaluation on datasets and tasks relevant to your specific needs. This can provide a more comprehensive understanding of a model's strengths and weaknesses.

4. Prioritize Relevant Metrics: Depending on your application, certain benchmark scores may be more important than others. Focus on the metrics that align with your specific requirements and use cases.

5. Expect Ongoing Evolution: Benchmark scores and model rankings can change over time as new models are developed and benchmarks are updated. Stay informed about the latest advancements and reevaluate your choices accordingly.

By approaching benchmark scores with this nuanced perspective, you can make more informed decisions about which models to use and how to best leverage their capabilities for your specific needs.

When selecting an API inference provider for the Llama 4 series, there are several key factors to consider:

1. Model Performance: Test multiple providers using the same prompt and benchmark dataset to evaluate the quality of output. Different providers may have varying performance, even when hosting the same model.

2. Max Context Window: Ensure the provider supports a sufficiently large context window, especially for tasks like coding where a larger context is crucial. Many providers limit the max context, so this should be a key consideration.

3. Max Output Tokens: Similar to the context window, the max output tokens supported by the provider can impact the usability of the model. Providers with higher token limits are preferable.

4. Precision: Most providers use 8-bit precision, but some, like the free version on OpenRouter, may offer 16-bit precision which can improve accuracy.

5. Price and Speed: Compare the pricing and generation speed across different providers to find the best balance of cost and performance for your use case.

6. Customization Options: Some providers offer more flexibility in terms of setting temperature, repetition penalties, and other hyperparameters, which can be important for fine-tuning the model's behavior.

By considering these factors, you can select the inference provider that best meets the requirements of your specific application and ensures optimal performance from the Llama 4 series models.

Based on the extensive testing across multiple API providers hosting the Llama 4 models, a few key takeaways emerge:

1. Performance Variability: The same Llama 4 model can exhibit varying performance when hosted by different API providers, even with the same prompt and hyperparameters. This highlights the importance of testing across multiple providers to find the best fit for your specific use case.

2. Importance of Benchmarking: While academic benchmarks provide a general sense of a model's capabilities, it is crucial to conduct your own independent testing to understand how the model performs on your specific tasks and datasets. Relying solely on published benchmarks can be misleading.

3. Context Window and Output Limits: The maximum context window and output token limits imposed by different API providers can significantly impact the model's usability, especially for tasks that require longer-form generation. Carefully evaluate these limits when selecting a provider.

4. Precision Differences: Some API providers may host the Llama 4 models in different precision formats (e.g., 8-bit vs. 16-bit), which can affect performance. This is an important consideration when choosing a provider.

5. Ongoing Optimization: As the Llama 4 models and their hosting infrastructure continue to evolve, it is recommended to periodically re-evaluate the performance of different API providers to ensure you are using the most optimal solution for your needs.

In summary, the choice of API provider for Llama 4 models can have a significant impact on the model's performance and usability. Thorough testing, benchmarking, and consideration of provider-specific limitations are essential to making an informed decision that best fits your requirements.