Cintex Beta 1
Medium-scale model for efficient reasoning and persona-native workflows.
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Stellr Intelligence Layer
Medium Language Models for compressed context, persona-native behavior, and lower token usage - without oversized compute or cost.
Medium-scale model for efficient reasoning and persona-native workflows.
Input
Output
Stellr is preparing a family of Medium Language Models around a custom plug-n-play transformer architecture. The goal is not to chase unnecessary scale, but to ship focused, adaptable models you can compose, tune, and deploy around specific workflows.
All models prioritize context switching, persona-native behavior, dense context maps, and lower token usage - so you expand capability without intensive compute or cost.
Heavy yet stable switching between roles, tools, tasks, and knowledge domains.
Personas shape behavior, priorities, tone, and decisions at the architecture level.
Long interactions become dense maps that preserve intent, constraints, and memory.
Optimized I/O and internal representation so meaningful work costs fewer tokens.
Offload inference batches to approved devices via Exegesis Websocket - steady links and cache state.
Fine-tune personas in batches for subtle, steady gains in ability and knowledge.
Specialized personas load on demand - fast context switches with capable tools.
We don’t price per-1M-tokens. Pick a request frequency tier and ship. All plans share the same context ceiling.
All plans · No per-token billing · Same 256K context ceiling