SOMA X v0.3.0 vs OpenVLA 7B

At a Glance

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OpenVLA 7BOpenVLA Research Team
Pricing and Limits
Context windowMaximum documented tokensNot reportedNot reported
Model facts checkedSep 2, 2026View model evidence →Aug 29, 2026View model evidence →

Available Benchmarks

All benchmark results →
No Protocol-Matched Benchmark Yet.Results appear here only when both models share the same benchmark version, metric, evaluation protocol, and evidence class.

Side-by-Side Facts

FieldSOMA-X v0.3.0OpenVLA 7B
DeveloperNVIDIAOpenVLA Research Team
FamilySoma XOpenVLA
ModelSOMA-X v0.3.0OpenVLA 7B
VersionSOMA-X v0.3.07B
Lifecycleactiveactive
Released2026-09-022024-06-13
Knowledge cutoffUnknownUnknown
Input modalitiesModel-specific inputText, Image, Robot state
Output modalities3DRobot action
Context windowUnknownUnknown
Total parametersUnknown7B
Active parametersUnknownUnknown
Licenseapache-2.0Unknown
Open weightsYesYes
API availableNoNo
Self-hostableYesYes
Provider accessUnknownUnknown
Capabilitiesanimation, hand-modeling, human-body-modeling, motion-retargeting, pose-inversion, simulationcross-embodiment, fine-tuning, generalist-manipulation
Robotics model typeUnknownVision-language-action model
Action representationUnknownTokenized actions decoded to continuous robot controls
Control architectureUnknownFused SigLIP and DINOv2 visual encoder with Llama 2 7B backbone
Inference locationUnknownFlexible
Native control rate (Hz)UnknownUnknown
Supported embodimentsUnknownWidowX, Google Robot, Franka Panda
Training dataUnknown970,000 robot manipulation trajectories from Open X-Embodiment described by the authors.

SOMA X v0.3.0 Capabilities

animationhand-modelinghuman-body-modelingmotion-retargetingpose-inversionsimulation
Model typeUnknown
InferenceUnknown
Action representationUnknown
Supported embodimentsUnknown
Canonical IDnvidia/SOMA-X-v0.3.0

OpenVLA 7B Capabilities

cross-embodimentfine-tuninggeneralist-manipulation
Model typeVision-language-action model
InferenceFlexible
Action representationTokenized actions decoded to continuous robot controls
Supported embodiments3
Canonical IDopenvla/openvla-7b

Primary Evidence

Sources and Freshness

Questions

SOMA X v0.3.0 vs OpenVLA 7B FAQs

Is SOMA X v0.3.0 or OpenVLA 7B better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both SOMA X v0.3.0 and OpenVLA 7B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, SOMA X v0.3.0 or OpenVLA 7B?+

Neither model has a directly sourced input price in this comparison. Neither model has a directly sourced output price in this comparison.

Which has a larger context window, SOMA X v0.3.0 or OpenVLA 7B?+

Neither model has a larger sourced context window in this comparison. SOMA X v0.3.0 is — and OpenVLA 7B is —.

Which performs better in benchmarks, SOMA X v0.3.0 or OpenVLA 7B?+

There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.

Can SOMA X v0.3.0 or OpenVLA 7B be self-hosted?+

Both models have the same recorded self-hosting status: supported. SOMA X v0.3.0 is open weight; OpenVLA 7B is open weight.

Can SOMA X v0.3.0 and OpenVLA 7B understand images?+

SOMA X v0.3.0 is not documented with image input; OpenVLA 7B is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, SOMA X v0.3.0 or OpenVLA 7B?+

Neither has a larger sourced maximum output. SOMA X v0.3.0 is — and OpenVLA 7B is —.

Do SOMA X v0.3.0 and OpenVLA 7B support reasoning and tool use?+

SOMA X v0.3.0: none of these features are definitively sourced. OpenVLA 7B: image input. Feature support does not establish relative quality.

Which is available from more inference providers, SOMA X v0.3.0 or OpenVLA 7B?+

SOMA X v0.3.0 has 0 sourced provider routes; OpenVLA 7B has 0, a tie.

Which offers better value, SOMA X v0.3.0 or OpenVLA 7B?+

There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.

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