GR00T N1.7 3B vs SOMA X v0.3.0

At a Glance

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Pricing and Limits
Context windowMaximum documented tokensNot reportedNot reported
Model facts checkedAug 29, 2026View model evidence →Sep 2, 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

FieldGR00T N1.7 3BSOMA-X v0.3.0
DeveloperNVIDIANVIDIA
FamilyIsaac GR00TSoma X
ModelGR00T N1.7 3BSOMA-X v0.3.0
VersionN1.7SOMA-X v0.3.0
Lifecycleactiveactive
Released2026-07-072026-09-02
Knowledge cutoffUnknownUnknown
Input modalitiesText, Image, Robot stateModel-specific input
Output modalitiesRobot action3D
Context windowUnknownUnknown
Total parameters3BUnknown
Active parametersUnknownUnknown
Licenseapache-2.0apache-2.0
Open weightsYesYes
API availableNoNo
Self-hostableYesYes
Provider accessUnknownUnknown
Capabilitiescross-embodiment, dexterous-manipulation, whole-body-control, fine-tuninganimation, hand-modeling, human-body-modeling, motion-retargeting, pose-inversion, simulation
Robotics model typeVision-language-action modelUnknown
Action representationPredictive chunks of relative joint motionsUnknown
Control architectureVision-language backbone with action expertUnknown
Inference locationOn deviceUnknown
Native control rate (Hz)UnknownUnknown
Supported embodimentsUnitree G1, AgiBot Genie-1, Fourier GR-1, bimanual manipulation platformsUnknown
Training dataMixture of real teleoperation, synthetic robot data, and internet-scale video described by NVIDIA.Unknown

GR00T N1.7 3B Capabilities

cross-embodimentdexterous-manipulationwhole-body-controlfine-tuning
Model typeVision-language-action model
InferenceOn device
Action representationPredictive chunks of relative joint motions
Supported embodiments4
Canonical IDnvidia/GR00T-N1.7-3B

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

Primary Evidence

Sources and Freshness

Questions

GR00T N1.7 3B vs SOMA X v0.3.0 FAQs

Is GR00T N1.7 3B or SOMA X v0.3.0 better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both GR00T N1.7 3B and SOMA X v0.3.0, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, GR00T N1.7 3B or SOMA X v0.3.0?+

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, GR00T N1.7 3B or SOMA X v0.3.0?+

Neither model has a larger sourced context window in this comparison. GR00T N1.7 3B is — and SOMA X v0.3.0 is —.

Which performs better in benchmarks, GR00T N1.7 3B or SOMA X v0.3.0?+

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

Can GR00T N1.7 3B or SOMA X v0.3.0 be self-hosted?+

Both models have the same recorded self-hosting status: supported. GR00T N1.7 3B is open weight; SOMA X v0.3.0 is open weight.

Can GR00T N1.7 3B and SOMA X v0.3.0 understand images?+

GR00T N1.7 3B is documented with image input; SOMA X v0.3.0 is not documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, GR00T N1.7 3B or SOMA X v0.3.0?+

Neither has a larger sourced maximum output. GR00T N1.7 3B is — and SOMA X v0.3.0 is —.

Do GR00T N1.7 3B and SOMA X v0.3.0 support reasoning and tool use?+

GR00T N1.7 3B: image input. SOMA X v0.3.0: none of these features are definitively sourced. Feature support does not establish relative quality.

Which is available from more inference providers, GR00T N1.7 3B or SOMA X v0.3.0?+

GR00T N1.7 3B has 0 sourced provider routes; SOMA X v0.3.0 has 0, a tie.

Which offers better value, GR00T N1.7 3B or SOMA X v0.3.0?+

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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