Why high: an exact fingerprint shared with 5 models.
Microsoft: Phi 4 is Microsoft's model. Its measured tokenizer sits with the OpenAI cl100k group, which is evidence that it builds on the OpenAI cl100k tokenizer. Tokenizer reuse is normal engineering, open vocabularies travel between labs, and this says nothing against Microsoft's authorship of the model itself.
cl100k_base is an open vocabulary table anyone can build on. Sharing it says which dictionary the model reads with, and nothing about who made the model.
Phi-4's repository tokenizer, run over the 50 strings offline, matches OpenAI's open cl100k_base encoding on every string (vocabulary 100,352, the padded cl100k table). IBM's Granite 4.0 and 4.1 declare the same vocabulary size and their endpoints return identical counts, which is why Microsoft and IBM share an exact signature group: both build on the tokenizer OpenAI released as open source with tiktoken.
Phi-4 repository (config.json: vocab_size 100352)Granite 4.0 H Micro repository (config.json: vocab_size 100352)OpenAI tiktoken (cl100k_base, open source)
| We checked | What we found | Where it came from |
|---|---|---|
| How it counts tokens | Counts every one of the 50 test strings exactly like 5 other models | tk_ba918336 |
| How its API is set up | A combination of settings no other listing in the catalogue uses | api_b985bdd1 |
| How much it can read and write | Reads up to 16,384 tokens at onceOpenRouter's listing shows 14,745 output tokens, which is 90% of the context window, the figure it carries where the provider declares no maximum. | its own listing |
| Who served our requests | DeepInfra | we measured it, 21 August 2026 |
Click a row to open the full comparison.
We sent Microsoft: Phi 4 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: 7 prompt tokens.
[Microsoft Research](/microsoft) Phi-4 is designed to perform well in complex reasoning tasks and can operate efficiently in situations with limited memory or where quick responses are needed. At 14 billion...
Supported parameters
frequency_penaltylogit_biasmax_tokensmin_ppresence_penaltyrepetition_penaltyresponse_formatseedstopstructured_outputstemperaturetop_ktop_p
defaults: {}
