AI Desk · AI FingerprintingNew arrival
Why high: an exact fingerprint shared with 5 models that declare the same family, without a second signal of a different kind yet.
Nex AGI: Nex-N2.5-Pro is Nex Agi's model. Its measured tokenizer sits with the Qwen group, which is evidence that it builds on the Qwen tokenizer. Tokenizer reuse is normal engineering, open vocabularies travel between labs, and this says nothing against Nex Agi'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 5 other models | tk_06ffa14a |
| How its API is set up | A combination of settings no other listing in the catalogue uses | api_adecfcee |
| How much it can read and write | Reads up to 262,144 tokens at onceOpenRouter's listing shows 235,929 output tokens, which is 90% of the context window, the figure it carries where the provider declares no maximum. | its own listing |
| Whether it thinks before answering | Reasons when asked to, at high, medium, none, high by default | its own listing |
| Who served our requests | Nex AGI | we measured it, 5 October 2026 |
Click a row to open the full comparison.
We sent Nex AGI: Nex-N2.5-Pro 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: 9 prompt tokens.
Nex-N2.5 is an agentic model built to turn goals into working, verified outcomes. Its core strength is agentic coding within a visual feedback loop: it can explore codebases, implement multi-file...
Supported parameters
include_reasoninglogprobsmax_tokensreasoningreasoning_effortstructured_outputstemperaturetool_choicetoolstop_ktop_logprobstop_p
defaults: {"temperature":0.7,"top_p":0.95,"top_k":40}
