Why high: an exact fingerprint shared with 6 models.
inclusionAI: Ling 3.0 Flash is InclusionAI's model, and its measured tokenizer sits in InclusionAI's own signature group alongside its sibling models. No other maker's measured model shares it so far.
| We checked | What we found | Where it came from |
|---|---|---|
| How it counts tokens | Counts every one of the 50 test strings exactly like 6 other models | tk_fc5a4585 |
| How its API is set up | A combination of settings no other listing in the catalogue uses | api_98bcaeec |
| How much it can read and write | Reads up to 262,144 tokens at once, writes up to 32,768 | its own listing |
| Whether it thinks before answering | Reasons when asked to | its own listing |
| Who served our requests | Novita | we measured it, 21 August 2026 |
Click a row to open the full comparison.
We sent inclusionAI: Ling 3.0 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: 20 prompt tokens.
*Ling-3.0-flash* is a *124B-parameter Mixture-of-Experts (MoE) model*, with approximately *5.1B parameters activated per token*. The model is designed with *token efficiency and production-scale agentic inference* as key priorities, enabling developers...
Supported parameters
frequency_penaltyinclude_reasoninglogit_biaslogprobsmax_tokensmin_ppresence_penaltyreasoningrepetition_penaltyresponse_formatseedstoptemperaturetool_choicetoolstop_ktop_logprobstop_p
defaults: {}
