Foreign tokenizers shatter Azerbaijani into far too many tokens. Prometheus uses a native tokenizer that handles the ə character and agglutinative morphology, making it 4.6× more efficient on Azerbaijani text.
A tokenizer built for English splits Azerbaijani words into many small, meaningless fragments — inflating cost, latency and misunderstanding. Prometheus rebuilds the tokenizer for the language itself: it treats the ə character and Azerbaijani’s agglutinative word-building as meaningful units, which makes the model 4.6× more efficient on Azerbaijani text.
Handles the ə character correctly instead of mangling it.
Treats agglutinative morphology as meaningful units, not random fragments.
4.6× more efficient on Azerbaijani text, cutting both cost and latency.
Better tokenization means better understanding of Azerbaijani meaning.
Part of a model built natively for Azerbaijani, deployed on your own infrastructure.
Foreign tokenizers split Azerbaijani into far more tokens, inflating cost and latency and losing meaning. Prometheus’s native tokenizer keeps words as meaningful units.
Prometheus processes Azerbaijani text 4.6× more efficiently than models using foreign tokenizers.
Yes. The native tokenizer handles the ə character and Azerbaijani’s agglutinative morphology directly.
The first large language model built natively for Azerbaijani — not adapted from English.
Runs fully inside your own network — data never leaves your infrastructure.
Three model sizes to match your accuracy needs and hardware budget.
Proven on 38,139 native Azerbaijani questions across 11 disciplines — no translations.
See the complete product: problem, features, how it works and deployment.
Request a demo to see Prometheus running on-premise — Azerbaijani understanding, model sizing and API integration, end to end.