AI Desk · AI FingerprintingNew arrival
Why high: an exact fingerprint shared with 2 models that declare the same family, without a second signal of a different kind yet.
Perceptron: Perceptron Mk1.5 is Perceptron's model. Its measured tokenizer sits with the Qwen group, which is evidence that it builds on the Qwen tokenizer. Tokenizer reuse is normal engineering, open vocabularies travel between labs, and this says nothing against Perceptron'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 2 other models | tk_c5c0a8ed |
| How its API is set up | A combination of settings no other listing in the catalogue uses | api_b668bc35 |
| How much it can read and write | Reads up to 36,864 tokens at once, writes up to 8,192 | its own listing |
| Whether it thinks before answering | Reasons when asked to, at high, medium, low, minimal, none, high by default | its own listing |
| Who served our requests | Perceptron | we measured it, 5 October 2026 |
Click a row to open the full comparison.
We sent Perceptron: Perceptron Mk1.5 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: 6 prompt tokens.
Perceptron Mk1.5 is Perceptron's embodied reasoning model for physical agents. It accepts text, image, video, and audio input, and answers with text plus optional structured annotations: points, boxes, polygons, tracks,...
Supported parameters
frequency_penaltyinclude_reasoningmax_tokenspresence_penaltyreasoningreasoning_effortstructured_outputstemperaturetool_choicetoolstop_ktop_p
defaults: {}
