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On October 5, 2026, Reka announced Rho-1, a 19-billion-parameter model trained from scratch. Reka says a single network can understand and generate text, images and video, reason over them, and output robot actions. It is a research preview, not a consumer product. Everything below about what it does comes from Reka’s own announcement, and the demos have not been independently tested.

What Rho-1 is

Reka’s announcement opens with this statement: “Today, we are releasing a research preview of Rho-1, our 19B omni-reasoning model trained from scratch.” That is company-authored, not an outside assessment. No independent expert commentary on Rho-1 was available at the time of writing.

The pitch is unification. Many systems split these jobs across separate models: a language model for reasoning, a diffusion model for images and video, and a policy model for robots. Reka says Rho-1 handles all of them in one model with shared context. That is an architecture claim, not an established general capability.

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How Reka says it works

Two expert streams, one attention space

Reka describes two expert weight streams inside the transformer blocks:

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  • Understanding stream: handles language and parsing of visual input.
  • Generation stream: denoises image and video latents.

The two streams share attention and a KV cache. In Reka’s account, this is how state passes between understanding and generation without handing work to a separate model.

Discrete and continuous tokens

Token type Used for (per Reka) Training objective (per Reka)
Discrete Text, symbolic reasoning, high-level commands Next-token prediction
Continuous Image latents, video frames, robot actions, proprioception Flow matching

What the demos show

Five-turn multimodal assistant

Across five turns, the demo creates an image, locates an object in it, animates the scene into video, edits that video, and explains the change. Reka says the shared context carries state between turns, including the representation used to generate the image and video.

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Simulated robotics episode

Reka also shows a simulated LIBERO robotics episode with observations, a predicted view and seven action channels. This is a simulation, and it shows what Reka chose to present. It does not establish real-world robot reliability.

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Speed figures, with attribution

All of these numbers are reported by Reka and were not independently reproduced.

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Figure What Reka reports Caveat
0.79× real time Median video-generation speed of the base model Below real time; test protocol not detailed in the summary available
About 6 seconds Time until a watchable stream starts Reka’s estimate (“roughly”)
99 to 8 steps Distilled variant shortens the denoising trajectory with minimal quality loss No independent quality measurement available
7.0 s vs 13.8 s First clip from Rho-1 (measured) vs a multi-agent pipeline (illustrative) Not a controlled benchmark; one side is labelled illustrative
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Availability, price and requirements

The announcement calls Rho-1 a research preview but does not state access conditions, pricing, public weights, a public API, or hardware requirements. Reka’s August 2026 article describes an earlier real-time video-generation offering as a closed beta. That is a separate product, so do not read it as Rho-1’s availability. Nothing in the announcement indicates a particular GPU, robot arm or accessory is needed.

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How to read the claim

  • Not yet established: independent benchmarks, reproduction of the demos, real-world robot performance, and production reliability.
  • Comparisons: the announcement supports no head-to-head ranking against other models. Fair comparisons would need matching protocols for supported modalities, action output, latency, evaluation data and deployment requirements.
  • What to watch: Reka’s documentation for access terms and any third-party evaluations. Performance figures may change, so check Reka’s current pages.

Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.