One dimension is not a complete decision
Cost, token volume, capacity, and evidence coverage answer different questions. Compare them with a representative prompt, actual output length, retry behavior, and your quality bar.
Find a model for a taskMeasured tokenizer data
Token efficiency rankings by tokenizer
See which tokenizer produces fewer tokens for the same published English, Chinese, JSON, and code samples. This is a token-volume comparison, not a model quality ranking.
Measured tokenizer data
Measured token counts for the same text
The board sums the same source samples across each tokenizer. Different chat wrappers, system prompts, tools, and model-specific processing can change production counts.
| Rank | Tokenizer | Samples | Total tokens | Tokens / 100 chars | Basis |
|---|---|---|---|---|---|
| 1 | OpenAI Current | 3 / 3 | 114 | 27.2 | o200k_base |
| 2 | DeepSeek V4 Pro | 3 / 3 | 117 | 27.9 | Pinned tokenizer JSON |
| 3 | Llama 3 | 3 / 3 | 118 | 28.2 | Pinned tokenizer JSON |
| 4 | OpenAI Legacy | 3 / 3 | 121 | 28.9 | cl100k_base |
| 5 | Qwen 3.5 | 3 / 3 | 129 | 30.8 | Pinned tokenizer JSON |
| 6 | Mistral Medium 3.5 | 3 / 3 | 132 | 31.5 | Pinned tokenizer JSON |
| 7 | Gemma 3 | 3 / 3 | 135 | 32.2 | Pinned SentencePiece model |
Checked 2026-07-19. Plain text only; no chat templates, roles, tools, images, or API wrappers. Open the reproducible tokenizer comparison to inspect the source text and each result.
Turn a ranking into a test
FAQ
Ranking methodology questions
Does a higher ranking mean a better model?
No. Each board measures one defined dimension. Token count, price, context capacity, and evidence coverage do not substitute for testing your own completed tasks.