Language generation research increasingly spans three paradigms: autoregressive decoding, discrete masked diffusion, and continuous flow-matching.
Comparing them is difficult because each lives in a separate codebase, so measured differences often reflect implementation details rather than the paradigms themselves.
We present DantinoX, an open-source JAX/Flax library in which a single modular Transformer backbone serves all three paradigms.
Switching the generation paradigm, attention mechanism, or hardware topology requires only a configuration change, while the backbone architecture, tokenizer, initialization strategy, and training infrastructure remain consistent.
This enables controlled cross-paradigm comparisons within one API for training, streaming inference, and benchmarking.