Why high: an exact fingerprint shared with 1 model that declares the same family, without a second signal of a different kind yet.
Google: Gemma 4 31B is Google's model. Its measured tokenizer sits with the Gemma group, which is evidence that it builds on the Gemma 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 1 other model | tk_ba03436b |
| How its API is set up | A combination of settings no other listing in the catalogue uses | api_b3854435 |
| How much it can read and write | Reads up to 262,144 tokens at once, writes up to 16,384 | its own listing |
| Whether it thinks before answering | Reasons when asked to | its own listing |
| Who served our requests | DeepInfra | we measured it, 5 October 2026 |
| Measured twice | The 5 October 2026 reading agreed with the 21 August 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: Gemma 4 31B 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: 13 prompt tokens.
Gemma 4 31B Instruct is Google DeepMind's 30.7B dense multimodal model supporting text and image input with text output. Features a 256K token context window, configurable thinking/reasoning mode, native function...
Supported parameters
frequency_penaltyinclude_reasoninglogit_biaslogprobsmax_tokensmin_ppresence_penaltyreasoningrepetition_penaltyresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_ktop_logprobstop_p
defaults: {"temperature":1,"top_p":0.95,"top_k":64,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null}
