(GPT-4 Turbo with Vision has a knowledge cutoff of Dec 2023, so filter to Jan 2024+ to minimize the chance of contamination.)
In general, my take is that each model has its own personality, which can cause it to do better or worse on different sorts of tasks. From evaluating many LLMs, I've found that it's almost never the case that one model is better than an another at everything. When an eval only has a certain type of problem (e.g., only edits to long codebases, or only short self-contained competition problems), it's not clear how homogeneously its performance rankings will generalize to other coding tasks. Unfortunately, if you're a developer using an LLM API, the best thing to do is to test all of the models from all the providers to see which works best for your use case.
(I work at OpenAI, so feel free to discount my opinions as much as you like.)
(GPT-4 Turbo with Vision has a knowledge cutoff of Dec 2023, so filter to Jan 2024+ to minimize the chance of contamination.)
In general, my take is that each model has its own personality, which can cause it to do better or worse on different sorts of tasks. From evaluating many LLMs, I've found that it's almost never the case that one model is better than an another at everything. When an eval only has a certain type of problem (e.g., only edits to long codebases, or only short self-contained competition problems), it's not clear how homogeneously its performance rankings will generalize to other coding tasks. Unfortunately, if you're a developer using an LLM API, the best thing to do is to test all of the models from all the providers to see which works best for your use case.
(I work at OpenAI, so feel free to discount my opinions as much as you like.)