Thanks for your work on this! It's a very legit domain of problem for LLMs to optimize for. I've produced a comprehensive eval based on your post and run it against 30 models, each tasked with recalling specific data from 500 rows in different tabular formats. Have a look at the results here: https://weval.org/analysis/table-format-sensitivity__combine...

As you can see it's near 100% recall across all formats for a good chunk of frontier models, with a few (curiously, mostly Claude) failing a basic prompt adherance ("Return just the number") but still returning the right answers. The major failures are from Mistral Medium, Llama Maverick, Llama 3 70b Instruct, Mistral Nemo, Gemma 3 12b It, GPT 4o/4.1 Mini etc.

Based on these limited tests, here's the leaderboards on formats FWIW:

    CSV: 84.25%
    Markdown Table: 82.65%
    YAML: 81.85%
    JSON Lines (jsonl): 79.85%
    Markdown key-value: 79.83%
    Pipe-delimited: 79.45%
    Natural language summary: 78.65%
    JSON: 77.73%
    HTML table: 75.80%
    XML: 73.80%

So, the biggest takeaway really is: Use the best model you can reasonably afford, then format will matter less. The cheapest 100% coverage models are Gemini 2.5 Flash and Deepseek Chat V3.1

And if you have no control over model, then use CSV or Markdown Table.