Why they feel dumber? ... its not the model

Introducing the excitement around new openweight models with state-of-the-art results and the challenge of running them locally due to large parameter sizes.

Testing models from API providers reveals significant discrepancies in performance compared to advertised results, highlighting the importance of selecting the right inference provider.

An overview of open router models, focusing on the GLM 4.6 model as an example and discussing the impact of different hosting providers on model performance.

Discussion on benchmarking third-party models, academic benchmarks versus real-world use cases, and observations on model accuracy versus latency and cost considerations.

Exploring factors like quantization, precision usage, sampling methods, and hosting differences that impact the performance of models when deployed through API providers.

Explanation of how tools are called in backend systems, the role of connectors and service endpoints, and the emergence of tools-as-a-service solutions for developers.

Insights on schema validation errors in models, validation frequency, and the business opportunity of properly hosting models for creators and providers.