Why high: an exact fingerprint shared with 1 model that declares the same family, without a second signal of a different kind yet.
Meta: Llama 4 Maverick is Meta's model. Its measured tokenizer sits with the Llama4 group, which is evidence that it builds on the Llama4 tokenizer. Tokenizer reuse is normal engineering, open vocabularies travel between labs, and this says nothing against Meta'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_59e2520c |
| How its API is set up | A combination of settings no other listing in the catalogue uses | api_2b53aac3 |
| How much it can read and write | Reads up to 1,048,576 tokens at once, writes up to 16,384 | its own listing |
| Who served our requests | DigitalOcean | 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 Meta: Llama 4 Maverick 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: 10 prompt tokens.
Llama 4 Maverick 17B Instruct (128E) is a high-capacity multimodal language model from Meta, built on a mixture-of-experts (MoE) architecture with 128 experts and 17 billion active parameters per forward...
Supported parameters
frequency_penaltylogit_biaslogprobsmax_tokenspresence_penaltyrepetition_penaltyresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_ktop_logprobstop_p
defaults: {}
