Why are almost all new benchmarks and leaderboards coding focused?

I know in in this community LLM's are generally used for coding but there are other usecases besides coding and those usecases should be tested too. I also know benchmarks can sometimes be benchmaxxed and the model can still turn out shit but it can give a good outline on how a model should perform in a certain task. Maybe I'm too behind on the latest developments but we need more benchmarks for all other use-cases. I use LLM's mainly for foreign language learning, creative writing and STEM/Medical/Biochemistry reasoning and inquiries and I rarely find any new benchmarks that tell me how a model might perform in those areas. MMLU-Pro-2 and a solid benchmark that tells how a model will perform for language learning would be so good for my usecase, however in general we need more new diverse benchmarks for models in order to have a general outline for advancements in other areas. submitted by /u/Dance-Till-Night1 [link] [comments]

Related

• A collection of small domain-specific benchmarks for local models (30+ and growing)
• MTP benchmark results: the nature of the generative task dictates whether you will benefit (coding) or get slower inference (creative) from speculative inference. No other factor comes close.
• Is it just me, or are current LLM benchmarks failing to capture actual usability? (Gemma 4 vs. Gemini/Claude Opus)

Source: r/LocalLLaMA | 2026-08-02