AI Desk · AI FingerprintingWithdrawn *
Why high: one template-boundary shift from a group whose members all declare the same family.
Google: Gemma 3n 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 | Counted cleanly, and no other model in the catalogue counts the same way | tk_a15512c7 |
| How its API is set up | A combination of settings no other listing in the catalogue uses | api_368482fa |
| How much it can read and write | Reads up to 32,768 tokens at once | its own listing |
| Who served our requests | Together | we measured it, 21 August 2026 |
Click a row to open the full comparison.
We sent Google: Gemma 3n 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: 16 prompt tokens.
Gemma 3n E4B-it is optimized for efficient execution on mobile and low-resource devices, such as phones, laptops, and tablets. It supports multimodal inputs—including text, visual data, and audio—enabling diverse tasks...
Supported parameters
frequency_penaltylogit_biasmax_tokensmin_ppresence_penaltyrepetition_penaltyresponse_formatstopstructured_outputstemperaturetop_ktop_p
defaults: {}
* Google: Gemma 3n 4B no longer appears in the OpenRouter catalogue, first absent from the snapshot of 3 September 2026. The record stays because the measurements above were real when they were taken and this address has been published.
