A recent post on a forum dedicated to LLM models raises an interesting question: why does it seem that every new model is evaluated primarily for its code generation capabilities?

The user laments how the focus has shifted away from models optimized for creative writing and other conversational use cases, which had flourished with Llama 2. He wonders if this trend is due to an overemphasis on the model's "coding" abilities.

Other use cases

The user emphasizes the importance of models capable of understanding context more deeply and handling long-form writing, highlighting a potential market for advanced conversational applications, beyond simple code generation. For those evaluating on-premise deployments, there are trade-offs to consider, as discussed in AI-RADAR's analytical frameworks on /llm-onpremise.