The original Kohnex article highlights MiniCPM5-2B, a 2.5-billion-parameter model from OpenBMB, as an example of compact models benefiting from deep-thinking post-training.
The article cites official reported scores of 94.6 on MATH-500, 69.1 on LiveCodeBench, 46.4 on SWE-bench Verified, and 86.5 on AIME. These are the original article’s reported figures, not results independently measured by Kohnex.
The case for structured thinking
A compact model has a smaller parameter budget. A disciplined approach can still help it break a problem down, check assumptions, and evaluate its answer. MiniCPM5 pairs a compact model with deep-thinking post-training.
The important question is how much of a model’s capability comes from the structure of its reasoning. Model size is only one part of that picture.
What our own evaluation shows
In the internal KB-100 evaluation, DeepSeek V4 Flash improved from 42.7% to 65.2% with Kohnex reasoning. The model and maximum reasoning effort stayed the same.
That result applies to this specific custom evaluation. It is not a guarantee of the same gain for every model or every task. The complete breakdown and methodology are available on the benchmarks page.
One brain, whatever you run
Kohnex connects through MCP, so the same reasoning workflow can work with flagship APIs and compatible local-model setups. Keep the model that fits your work. Add a more deliberate way to use it.