Why high: an exact fingerprint shared with 21 models that declare the same family, without a second signal of a different kind yet.
Google: Gemma 3 4B 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_130ec2d7 |
| How much it can read and write | Reads up to 131,072 tokens at once, writes up to 16,384 | its own listing |
| Who served our requests | DeepInfra | we measured it, 22 August 2026 |
Click a row to open the full comparison.
We sent Google: Gemma 3 4B 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: 9 prompt tokens.
Gemma 3 introduces multimodality, supporting vision-language input and text outputs. It handles context windows up to 128k tokens, understands over 140 languages, and offers improved math, reasoning, and chat capabilities,...
Supported parameters
frequency_penaltylogit_biasmax_tokensmin_ppresence_penaltyrepetition_penaltyresponse_formatseedstopstructured_outputstemperaturetop_ktop_p
defaults: {}
