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Short answer: Choose Kyutai REKI when you need a smaller policy, explicit short-term visual memory, a published low-latency result, or a released checkpoint for your robot. Choose OpenVLA when your team wants the established 7B Open X-Embodiment baseline, its existing repository and fine-tuning workflow, or broad pretraining coverage. Neither project has published a universal, same-hardware head-to-head result, so the right choice depends on your cameras, action space, control loop, hardware and licence requirements.
REKI and OpenVLA at a glance
| Decision factor | Kyutai REKI | OpenVLA |
|---|---|---|
| Model scale | 2.4 billion parameters | 7 billion parameters |
| Core design | Vision-language model followed by a flow-matching action expert | Prismatic prism-dinosiglip-224px vision-language model using DINOv2 and SigLIP features with a Llama-2 language model |
| Temporal mechanism | Register tokens in the action expert persist in the cache and summarize recent VLM outputs | The cited OpenVLA materials do not document an equivalent register-token memory mechanism |
| Published training evidence | 200,000 pretraining steps on 32 H100 GPUs; pretraining uses public data | Training mixture spans 970,000 robot trajectories from Open X-Embodiment |
| Published inference result | 21.8 ms for one compiled action chunk from two 256×256 images on one H100 with ten Euler steps, under Kyutai’s stated setup | No directly matched latency figure is established in the cited materials |
| Named released robot checkpoints | Franka Panda, ALOHA, PandaOmron and Fourier GR-1 | Open X-Embodiment baseline with an open fine-tuning stack; a comparable robot-by-robot checkpoint list is not established here |
| Code and weights | Kyutai releases code, training and evaluation pipelines, deployment support, and pretrained or fine-tuned weights | Repository code is MIT-licensed; pretrained-model terms can inherit restrictions from upstream base models |
| Commercial-use signal | The REKI LIBERO card lists CC BY-NC-SA 4.0 weights and MIT code | The repository warns that pretrained models may inherit upstream restrictions, including Llama licensing |
The table separates published facts from interpretation. Parameter count is a useful first approximation for memory and serving cost, but it does not guarantee a particular frame rate on a robot computer. Camera resolution, batching, action-chunk length, compiler settings, quantization, host-device transfers and the control policy around the model can dominate real performance.
What Kyutai REKI is
Kyutai describes REKI as a 2.4B vision-language-action model. Its policy is split into a vision-language model that interprets images and instructions, and a flow-matching action expert that produces the robot action chunk. The action expert’s register tokens remain in the inference cache. They condense recent outputs from the vision-language stage, giving the policy a short-term temporal state without requiring the entire visual history to be recomputed at every action step.
That design matters when a robot must keep track of motion that is not visible in the current frame: a gripper approaching an object, a partially occluded part, or the effect of the previous command. The cache is a model feature, not a substitute for a state estimator, force sensor or safety controller. Teams should still define what happens when frames are dropped, the camera is moved, or the cached context becomes stale after an operator intervention.
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Kyutai says REKI was pretrained exclusively on public data and releases the surrounding engineering pieces rather than only a checkpoint. The release includes code, training and evaluation pipelines, deployment support, and pretrained or fine-tuned weights. That can reduce the amount of infrastructure a team must reconstruct when adapting the policy, although reproducing published behavior still requires matching the data processing, action representation, camera arrangement and evaluation protocol.
What OpenVLA is
OpenVLA’s repository describes openvla-7b as a seven-billion-parameter model built from the Prismatic prism-dinosiglip-224px vision-language model. Its visual representation combines DINOv2 and SigLIP features, while the language component is based on Llama 2. The model was trained on a mixture from Open X-Embodiment containing 970,000 robot trajectories.
That large, heterogeneous mixture is OpenVLA’s central practical attraction. A team that already understands the repository, preprocessing and fine-tuning path can begin from a widely used baseline instead of designing an entire training workflow. The trade-off is a larger model footprint and a more complicated licensing review for the resulting weights.
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Latency, model size and serving requirements
What the REKI latency number means
Kyutai reports 21.8 milliseconds for one compiled action chunk generated from two 256×256 images on one H100, using ten Euler steps. Every part of that condition is important: the result is for a compiled implementation, a particular accelerator, two image inputs, a defined solver setting and an action chunk rather than an unspecified end-to-end robot cycle.
It is a useful reference for the computational shape of the model, not a promise that a workstation GPU, an embedded computer or a production robot will achieve the same timing. A robot’s observed loop also includes image capture, synchronization, preprocessing, host-to-device transfers, action decoding, safety checks and actuator communication. Measure those stages separately.
Why the smaller parameter count helps
REKI’s lower parameter count generally reduces weight memory and can make compilation, loading and cache management easier. The benefit is strongest when the deployment computer is constrained or when several camera streams and safety processes must share the accelerator. It does not automatically determine throughput: an implementation with inefficient kernels can be slower than a larger model with highly optimized operators.
OpenVLA’s larger model may require more accelerator memory and can increase transfer and compilation costs. In exchange, its size and training mixture may be advantageous when the model’s existing representation and fine-tuning recipes match your data. Before selecting hardware, profile the exact checkpoint and precision you plan to ship; do not size a system from parameter count alone.
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How to compare latency fairly
- Fix the image count, resolution, color format and camera synchronization for both policies.
- Use the same action-chunk definition and report both model-only inference and full control-loop latency.
- Record warm-cache and cold-start timings separately; compilation and first-use overhead can distort short tests.
- Measure a distribution, not only an average: median, high-percentile latency and dropped-deadline rate are more useful for control.
- Run the test on the computer that will be mounted on or connected to the robot, then repeat with safety and logging processes enabled.
Robot and embodiment coverage
REKI’s official release table names robot-specific checkpoints for four settings. The checkpoint names connect the policy to a robot and dataset family, but they do not guarantee zero-shot success on every mechanical variant. Camera placement, gripper geometry, joint limits, calibration and action units still need to match.
| Robot or platform | REKI checkpoint context named by Kyutai | What to verify before deployment |
|---|---|---|
| Franka Panda | LIBERO | Camera viewpoint, task split, joint or end-effector action representation, workspace limits and reset procedure |
| ALOHA | RoboTwin | Whether your bimanual layout, teleoperation conventions and gripper calibration match the checkpoint |
| PandaOmron | RoboCasa365 | Object set, scene layout, action scaling and any changes to the mobile or manipulation configuration |
| Fourier GR-1 | RoboCasa-GR1 | Body configuration, camera height, locomotion or whole-body interfaces and emergency-stop behavior |
OpenVLA’s strongest cited distinction is not a similarly enumerated checkpoint table. It is the Open X-Embodiment pretraining mixture and the repository’s fine-tuning workflow. That makes it attractive when your robot is close to an existing data format or when your team intends to adapt the model with its own demonstrations. For a robot outside either project’s named examples, treat both as starting points and plan an adaptation and validation phase.
Data, training and reproducibility
REKI’s public-data emphasis
Kyutai reports 200,000 pretraining steps on 32 H100 GPUs and says the pretraining corpus is public. Public-only pretraining can simplify provenance checks and make it easier to identify the source of demonstrations, but it does not by itself grant commercial rights to every released weight. Dataset licences, checkpoint licences and code licences remain separate questions.
The released training and evaluation pipelines are valuable if you need to inspect preprocessing, reproduce metrics or add a new embodiment. Reproducibility still depends on matching the stated data, image transformations, action normalization, solver settings and hardware. A pipeline that runs is not evidence that your robot is covered by the published evaluation.
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OpenVLA’s Open X-Embodiment mixture
OpenVLA was trained on 970,000 trajectories from Open X-Embodiment. The breadth can expose the model to more robot behaviors and visual situations than a narrow single-platform dataset. It also means that transfer quality depends on whether your robot’s sensors, action vocabulary and task distribution resemble the mixture.
When fine-tuning, preserve a held-out set that reflects deployment rather than relying only on training loss. Include camera changes, object variation, partial occlusion and recovery states. A model can improve offline action prediction while becoming less reliable at the exact moments when a physical system needs a recovery command.
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Franka Panda or LIBERO-style work
REKI is the more direct starting point when your Panda setup matches the released LIBERO checkpoint’s observation and action conventions. You can then spend effort on calibration, safety envelopes and task-specific validation instead of first building a new embodiment adapter. OpenVLA remains reasonable when your lab already has its fine-tuning pipeline or when your demonstrations are organized around the Open X-Embodiment format.
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For an ALOHA system, REKI has a named RoboTwin checkpoint, which lowers the uncertainty about the intended embodiment. Confirm that the bimanual camera arrangement, action ordering and gripper commands are identical to your hardware. Choose OpenVLA if your team has a mature OpenVLA data and training process that can absorb those differences faster than a new REKI integration.
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A constrained on-robot computer
REKI is the logical first profile because its model is smaller and Kyutai publishes a detailed low-latency measurement. Do not skip an on-device benchmark: the H100 result is not an embedded-system guarantee. If the robot computer cannot sustain the required deadline, consider lower image rates, shorter action horizons or a separate high-level policy, but validate any change for safety and task success.
A research team standardizing on one open baseline
OpenVLA can be the better organizational choice when collaborators, internal tools and existing experiments already target its repository. The value is reduced integration friction and a common baseline across projects. REKI may still win for a particular robot if its checkpoint and temporal mechanism remove more engineering work than the shared OpenVLA infrastructure saves.
A robot not named in either release
Compare the adaptation burden rather than choosing by reputation. Check whether your camera count, image size, action space, control frequency and demonstration format can be translated cleanly. Then fine-tune and evaluate both on identical hardware and tasks if the decision affects a production deployment.
A practical evaluation plan
- Describe the deployment contract. Write down the robot model, cameras, image dimensions, action representation, control frequency, maximum acceptable latency, safety controller and recovery behavior.
- Select the closest starting checkpoint. For REKI, use the named embodiment checkpoint only when its robot and dataset conventions match. For OpenVLA, document the repository revision, base model and fine-tuning configuration you will use.
- Normalize the inputs and actions. Verify color order, cropping, camera calibration, joint ordering, units, action limits and chunk timing. Silent mismatches can look like model failures.
- Build matched offline tests. Use the same held-out demonstrations and report success-relevant metrics, action error, temporal consistency and recovery behavior. Keep scenes and tasks that were not used for tuning.
- Measure the complete loop. Timestamp capture, preprocessing, model inference, decoding, safety checks and actuator command. Report deadline misses and high-percentile latency in addition to average timing.
- Run staged physical trials. Start with low speed, soft or empty scenes and a reachable emergency stop. Add occlusion, camera perturbations and operator interruptions only after basic behavior is bounded.
- Stress the memory path. For REKI, test cache reset, stale-frame handling and behavior after a pause or manual intervention. For OpenVLA, test the temporal strategy used by your deployment rather than assuming one from the model name.
- Record failure boundaries. Log object types, viewpoints, lighting, contact conditions and action magnitudes that cause degradation. Use those records to define a fallback policy or collect targeted demonstrations.
- Review licences before shipping. Keep code, checkpoint, dataset and any upstream base-model terms in the release file. Obtain legal review for the exact commercial use case.
Commercial use and licensing
Commercial deployment cannot be decided from the word open in a project name. REKI’s LIBERO model card lists CC BY-NC-SA 4.0 for the weights and MIT for the code. The noncommercial condition on the stated weights is a material restriction for a product, paid service or other commercial operation unless you have an additional permission that covers your use. Code permission does not automatically change the weight licence.
OpenVLA’s repository states that its code is MIT-licensed but warns that pretrained models may inherit restrictions from the underlying base models. The warning specifically includes Llama licensing. A commercial user must identify the exact checkpoint, every upstream component and the intended distribution or service model. Fine-tuning a checkpoint does not necessarily remove upstream obligations.
Keep a written licence inventory for the model weights, code, training data, tokenizer, vision encoders and deployment dependencies. Ask counsel to review redistribution, hosted inference, customer access, modification, attribution and share-alike obligations. If the terms do not clearly permit your business model, treat the model as unsuitable until permission is confirmed.
What the published evidence does not prove
- There is no same-hardware, same-task benchmark in the cited material that establishes a universal REKI winner over OpenVLA or the reverse.
- REKI’s 21.8 ms figure cannot be generalized to every GPU, robot computer, image pipeline or control loop.
- A lower parameter count does not guarantee lower end-to-end latency or higher task success.
- A named robot checkpoint does not guarantee performance on a mechanically different unit or a changed camera layout.
- Open X-Embodiment coverage does not guarantee transfer to a robot whose sensors and action space differ from the training mixture.
- The available OpenVLA description does not document the same explicit register-token memory mechanism as REKI; it does not establish that all temporal behavior is absent.
Bottom line for your selection
Start with REKI when deployment constraints, short-term visual memory or one of its named robot checkpoints are the deciding factors. Start with OpenVLA when the seven-billion-parameter Open X-Embodiment baseline and its established fine-tuning workflow fit your team’s data and tooling. In either case, make the final decision from a matched test on the target cameras, action space, hardware and safety envelope, followed by a licence review of the exact checkpoint you intend to ship.
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