Why high: an exact fingerprint shared with 40 models that declare the same family, without a second signal of a different kind yet.
OpenAI: o3 Mini is OpenAI's model. Its measured tokenizer sits with the GPT group, which is evidence that it builds on the GPT tokenizer. Tokenizer reuse is normal engineering, open vocabularies travel between labs, and this says nothing against OpenAI'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 40 other models | tk_d1411c9d |
| How its API is set up | Set up identically to 3 other listings, field for field | api_3da68b21 |
| How much it can read and write | Reads up to 200,000 tokens at once, writes up to 100,000 | its own listing |
| Whether it thinks before answering | Reasons when asked to | its own listing |
| Who served our requests | OpenAI | we measured it, 3 September 2026 |
Click a row to open the full comparison.
We sent OpenAI: o3 Mini 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.
OpenAI o3-mini is a cost-efficient language model optimized for STEM reasoning tasks, particularly excelling in science, mathematics, and coding. This model supports the `reasoning_effort` parameter, which can be set to...
Supported parameters
include_reasoningmax_tokensreasoningresponse_formatseedstructured_outputstool_choicetools
defaults: {"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null}
