The main area of improvement can be performance on complex reasoning and coding tasks. Cohere Command R is strong for RAG and grounded generation, but I would not choose it for those tasks. There was a 4,000 token max output limit which can be restrictive when generating long reports or detailed summaries. Another limitation is that the model itself does not include native web search or fresh information; it still relies on an external retrieval or search layer, which could also be improved.
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.
Honestly, I have never needed technical support, but I think if you could improve on that, it would be acceptable. I do not know about the pricing; for me, it is kind of too much. Of course, I am using the free models, but if I could get the newer models, I think they are interesting. I know we are talking about Cohere Command R for now, but I think there are some other models that I have seen some interest in, like Embed 4. If the pricing could be adjusted, that would be better because the pricing is kind of high. Of course, it matters; for organizations, it is acceptable, but for personal use like mine, it is just a hobby project. Spending that much money on something that you do not earn from is not ideal. So for people testing or using it for hobby projects, I think you could reduce the pricing a bit. But for now, I am using Cohere Command R for free.
Cohere Command R offers a sophisticated suite of AI-driven tools tailored for advanced data analysis and language comprehension, making it a go-to option for businesses seeking efficiency.
Cohere Command R stands out as a versatile AI tool equipped to handle complex linguistic tasks and streamline data operations. It's designed to support developers and businesses in deriving actionable insights through advanced AI capabilities. With its robust performance, it caters to the growing needs of...
The main area of improvement can be performance on complex reasoning and coding tasks. Cohere Command R is strong for RAG and grounded generation, but I would not choose it for those tasks. There was a 4,000 token max output limit which can be restrictive when generating long reports or detailed summaries. Another limitation is that the model itself does not include native web search or fresh information; it still relies on an external retrieval or search layer, which could also be improved.
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.
Honestly, I have never needed technical support, but I think if you could improve on that, it would be acceptable. I do not know about the pricing; for me, it is kind of too much. Of course, I am using the free models, but if I could get the newer models, I think they are interesting. I know we are talking about Cohere Command R for now, but I think there are some other models that I have seen some interest in, like Embed 4. If the pricing could be adjusted, that would be better because the pricing is kind of high. Of course, it matters; for organizations, it is acceptable, but for personal use like mine, it is just a hobby project. Spending that much money on something that you do not earn from is not ideal. So for people testing or using it for hobby projects, I think you could reduce the pricing a bit. But for now, I am using Cohere Command R for free.
I do not know how Cohere Command R can be improved. I do not have anything at all I would like to see improved, even if it is something small.