There is some interesting comparisons found in the flan t5 paper. Checkout the paper "Scaling Instruction-Finetuned Language Models". Hope this helps.

For models, see my up-to-date list of models:

https://docs.google.com/spreadsheets/d/1O5KVQW1Hx5ZAkcg8AIRjbQLQzx2wVaLl0SqUu-ir9Fs/edit#gid=1158069878

For performance, Papers with code keep good benchmarks:

https://paperswithcode.com/area/natural-language-processing

https://paperswithcode.com/task/question-answering

For summarization: Tianyi Zhang, Faisal Ladhak, Esin Durmus, Percy Liang, Kathleen McKeown, Tatsunori B. Hashimoto. Benchmarking Large Language Models for News Summarization. arXiv:2301.13848.

thanks

> For models, see my up-to-date list of models:

Which tab is germane to OP's request?

> but I am specifically refering to performance after finetuning.

So far as I can tell, there is nothing here that is responsive to OP's query. But there is a lot here--perhaps I read too quickly.

Check out this great post that includes fine tuning Flan-T5, Language Models vs. The SAT Reading Test:

https://jeffq.com/blog/language-models-vs-the-sat-reading-test/