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Reka unveils Rho-1, a 19B 'omni' model that reads and generates text, images, streaming video and robot actions in one context

★★after cutoffresearchReka AIconfidence: high

On Oct 5, 2026 Reka published Rho-1, a 19B-parameter model trained from scratch that puts text, vision, video and robot actions as tokens in a single context, streaming steerable video at about 0.79x real time; a distilled Flash variant makes a 5.3-second clip in about one second. It is a research preview only: no public weights or API.

Key facts

What happened

Reka, the small multimodal lab behind the Reka Core/Flash/Edge models, published a research post on Rho-1. It is a single 19B transformer that handles text, images, video and robot actions as tokens in one context window, instead of chaining a language model with a separate video diffusion model. Reka says it is "among the fastest models in the world in every modality" and "faster than any other video model we timed" for 5.3-second clips, but it names no competing models. Every result comes from a checkpoint trained on "just 320 H100 GPUs for three months".

Why it matters

This is a compute-light attempt at the unified "omni" world-model and robotics stack that larger labs pursue with far more compute. The speed claims are Reka's own. No independent benchmarks, weights or API exist yet.

Changelog

  • 2026-10-05: created (21:30 full run)

Sources (3)

id: 2026-10-05-reka-rho-1-omni-model · updated 2026-10-05 · open in the interactive timeline