Why high: an exact fingerprint shared with 21 models that declare the same family, without a second signal of a different kind yet.
Google: Gemini 2.5 Flash is Google's model. Its measured tokenizer sits with the Gemini group, which is evidence that it builds on the Gemini tokenizer. Tokenizer reuse is normal engineering, open vocabularies travel between labs, and this says nothing against Google's authorship of the model itself.
| We checked | What we found | Where it came from |
|---|---|---|
| How it counts tokens | Counts every one of the 50 test strings exactly like 21 other models | tk_59ca250f |
| How its API is set up | Set up identically to 1 other listing, field for field | api_742308b0 |
| How much it can read and write | Reads up to 1,048,576 tokens at once, writes up to 65,535 | its own listing |
| Whether it thinks before answering | Reasons when asked to | its own listing |
| Who served our requests | we measured it, 5 October 2026 |
Click a row to open the full comparison.
We sent Google: Gemini 2.5 Flash 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: 0 prompt tokens.
Gemini 2.5 Flash is Google's state-of-the-art workhorse model, specifically designed for advanced reasoning, coding, mathematics, and scientific tasks. It includes built-in "thinking" capabilities, enabling it to provide responses with greater...
Supported parameters
include_reasoningmax_tokensreasoningresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_p
defaults: {"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null}
