There are some cons of this model. The output cap is 4,000 max tokens only, which was a lag part of this model. The knowledge base cutoff is June 2024, which is over a year and a half old now. It should be updated with the latest cutoff data. If this model supported a web tool with RAG and web search inbuilt, that would be very great and the model would be very perfect. For complex coding and multi-step logic, this model is of no use because it does not give accurate answers. This model should work only to make RAG better and better. There should be a model known by the name of RAG only, Retrieval-Augmented Generation, that will be used as RAG only for different platforms where users do not have to create a RAG pipeline and pass a tool. This model can help improve RAG and web search. If this model does not find data in the document and if users allow web search, then at runtime this model will perform web search and return the output. This way there is less chance the user will get a better output and this way the model can be improved. The large context window is a limitation. Suppose I want large output from this model, but the max output tokens are 4,000 only, so I cannot retrieve large answers from this model. This is one of the drawbacks, which is why I cut one point. This model lacks web search, so web search is not available. If web search were there, then this model could give answers from the web if the data is not present in that document, which is why I cut one point from this as well. The third point is the knowledge cutoff that this model is trained on, which is June 2024. It has been 1.5 years and it is now May 2026. The knowledge cutoff is very poor for this model, which is why I cut three points for this model. This is why I rate it 7 out of 10.
