Ren AI · Flagship model
Astra
Astra is Ren AI's flagship language model, designed for reasoning, software engineering, long-context understanding, and agentic workflows. It serves as the intelligence layer powering every Ren AI product.
Advanced Language Model · Active Development
Astra is Ren AI's flagship language model — built for reasoning, software engineering, long-context understanding, and agentic workflows.
It is the intelligence layer powering every Ren AI product. Ren Code is the first application built on Astra, and future products will be powered by Astra as well.
Current model
The generation in active development today — what actually ships, with no dates or results invented before they exist.
Current generation
The model in active development today, powering Ren Code.
The Journey of Astra
Astra began as a highly capable foundation model and is being continuously transformed through research, evaluation, training, software engineering workflows, and proprietary development techniques.
The goal is not merely to generate code. The goal is to build a language model capable of understanding software systems, reasoning about architecture, and assisting developers throughout the entire engineering lifecycle.
Astra continues to evolve through
- 01
Data curation
Assembling and screening high-signal training data from code, diffs, and engineering traces.
- 02
Model training
Transforming the foundation with proprietary techniques aimed at software reasoning.
- 03
Evaluation
A fixed, contamination-screened suite run before and after every training cycle.
- 04
Engineering feedback loops
Findings from real engineering work feed directly back into training priorities.
- 05
Real-world software workflows
Astra improves where it is actually used — inside real repositories and tasks.
Current research focus
These are research areas of Astra — directions of the model, not separate products.
Software Engineering
Resolving real engineering tasks end-to-end, across the files a change actually touches.
Reasoning
Multi-step problem solving with the discipline to admit uncertainty rather than guess.
Agent Systems
Planning, executing, and verifying multi-step work, then returning results that hold up.
Long Context Understanding
Holding large codebases and documents in context so decisions are made globally.
Repository Intelligence
Understanding architecture, dependencies, and conventions as one connected system.
Reliability
Calibrated confidence and consistent behavior — a tool you can trust, not double-check.
We will not publish benchmark numbers before we publish the evaluation harness that produces them. When capability results arrive, they will arrive with the method to reproduce them.