Why high: an exact fingerprint shared with 13 models that declare the same family, without a second signal of a different kind yet.
StepFun: Step 3.5 Flash is StepFun's model. Its measured tokenizer sits with the DeepSeek group, which is evidence that it builds on the DeepSeek tokenizer. Tokenizer reuse is normal engineering, open vocabularies travel between labs, and this says nothing against StepFun's authorship of the model itself.
StepFun's own Step-3 repository ships a tokenizer behaviourally identical to DeepSeek-V3's: run over the 50 strings offline the two agree on every one, and both declare a 128,815-entry vocabulary. The endpoint's counts match as well, so this model sitting in the DeepSeek signature group is genuine lineage reuse by StepFun, not a serving artefact.
StepFun step3 repository (config.json: vocab_size 128815)DeepSeek-V3 repository (config.json: vocab_size 129280; tokenizer table shared)
| We checked | What we found | Where it came from |
|---|---|---|
| How it counts tokens | Counts every one of the 50 test strings exactly like 13 other models | tk_73c4f0dc |
| How its API is set up | A combination of settings no other listing in the catalogue uses | api_b9b93634 |
| How much it can read and write | Reads up to 262,144 tokens at once, writes up to 65,536 | its own listing |
| Whether it thinks before answering | Always reasons, and cannot be turned off | its own listing |
| Who served our requests | SiliconFlow | we measured it, 21 August 2026 |
Click a row to open the full comparison.
We sent StepFun: Step 3.5 Flash 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: 12 prompt tokens.
Step 3.5 Flash is StepFun's most capable open-source foundation model. Built on a sparse Mixture of Experts (MoE) architecture, it selectively activates only 11B of its 196B parameters per token....
Supported parameters
frequency_penaltyinclude_reasoningmax_tokensreasoningtemperaturetool_choicetoolstop_ktop_p
defaults: {"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null}
