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 3.1 Pro Preview 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 2 other listings, field for field | api_16435537 |
| How much it can read and write | Reads up to 1,048,576 tokens at once, writes up to 65,536 | its own listing |
| Whether it thinks before answering | Always reasons, and cannot be turned off, at high, medium, low, medium by default | its own listing |
| Who served our requests | we measured it, 5 October 2026 | |
| Measured twice | The 5 October 2026 reading agreed with the 3 September 2026 reading on every one of the 50 strings clean in both: the tokenizer behind this name held. | 50/50 agree |
Click a row to open the full comparison.
We sent Google: Gemini 3.1 Pro Preview 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 3.1 Pro Preview is Google’s frontier reasoning model, delivering enhanced software engineering performance, improved agentic reliability, and more efficient token usage across complex workflows. Building on the multimodal foundation...
Supported parameters
include_reasoningmax_tokensreasoningreasoning_effortresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_p
defaults: {"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null}
