Why high: an exact fingerprint shared with 5 models.
OpenAI: GPT-3.5 Turbo 16k is OpenAI's model, and its measured tokenizer sits in OpenAI's own signature group alongside its sibling models. Measured models from IBM and Microsoft carry the same vocabulary, which reads as reuse of OpenAI's tokenizer line and questions nobody's authorship; they are listed under "Shares this fingerprint" below.
cl100k_base is an open vocabulary table anyone can build on. Sharing it says which dictionary the model reads with, and nothing about who made the model.
| We checked | What we found | Where it came from |
|---|---|---|
| How it counts tokens | Counts every one of the 50 test strings exactly like 5 other models | tk_ba918336 |
| How its API is set up | A combination of settings no other listing in the catalogue uses | api_7b2b1f22 |
| How much it can read and write | Reads up to 16,385 tokens at once, writes up to 4,096 | its own listing |
| Who served our requests | OpenAI | we measured it, 5 October 2026 |
Click a row to open the full comparison.
We sent OpenAI: GPT-3.5 Turbo 16k fifty short pieces of text and recorded what each one cost it in tokens. The bars below are those costs. Two models built on the same tokenizer produce the same bars; a model built on a different one produces a different set, which is what makes this a fingerprint.
Overhead subtracted: 7 prompt tokens.
This model offers four times the context length of gpt-3.5-turbo, allowing it to support approximately 20 pages of text in a single request at a higher cost. Training data: up...
Supported parameters
frequency_penaltylogit_biaslogprobsmax_completion_tokensmax_tokenspresence_penaltyresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_logprobstop_p
defaults: {}
