GR00T N1.7 3B vs o3

At a Glance

Compare
o3OpenAI
Pricing and Limits
Context windowMaximum documented tokensNot reported200K
Model facts checkedAug 29, 2026View model evidence →Sep 3, 2026View model evidence →
Different Model RolesThese models do not share a sourced market category. Their primary-source facts remain comparable below, while performance claims require matched 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 3Bo3
DeveloperNVIDIAOpenAI
FamilyIsaac GR00TO3
ModelGR00T N1.7 3Bo3
VersionN1.7o3
Lifecycleactiveactive
Released2026-07-072025-04-16
Knowledge cutoffUnknown2024-06-01
Input modalitiesText, Image, Robot stateText, Image
Output modalitiesRobot actionText
Context windowUnknown200K
Total parameters3BUnknown
Active parametersUnknownUnknown
Licenseapache-2.0Unknown
Open weightsYesNo
API availableNoYes
Self-hostableYesNo
Provider accessUnknownOpenai (Standard), Openrouter (Standard)
Capabilitiescross-embodiment, dexterous-manipulation, whole-body-control, fine-tuningchat, generation, reasoning, structured_outputs, tools
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

o3 Capabilities

chatgenerationreasoningstructured outputstools
Model typeUnknown
InferenceUnknown
Action representationUnknown
Supported embodimentsUnknown
Canonical IDopenai/o3

Primary Evidence

Sources and Freshness

Questions

GR00T N1.7 3B vs o3 FAQs

Is GR00T N1.7 3B or o3 better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both GR00T N1.7 3B and o3, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, GR00T N1.7 3B or o3?+

Only o3 has a directly sourced input price: $1.00 per million tokens. Only o3 has a directly sourced output price: $4.00 per million tokens.

Which has a larger context window, GR00T N1.7 3B or o3?+

Neither model has a larger sourced context window in this comparison. GR00T N1.7 3B is — and o3 is 200K.

Which performs better in benchmarks, GR00T N1.7 3B or o3?+

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 o3 be self-hosted?+

GR00T N1.7 3B is the only model in this pair currently marked as self-hostable. GR00T N1.7 3B is open weight; o3 is not marked open weight.

Can GR00T N1.7 3B and o3 understand images?+

GR00T N1.7 3B is documented with image input; o3 is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, GR00T N1.7 3B or o3?+

Neither has a larger sourced maximum output. GR00T N1.7 3B is — and o3 is 100K.

Do GR00T N1.7 3B and o3 support reasoning and tool use?+

GR00T N1.7 3B: image input. o3: reasoning, tool calling, and image input. Feature support does not establish relative quality.

Which is available from more inference providers, GR00T N1.7 3B or o3?+

GR00T N1.7 3B has 0 sourced provider routes; o3 has 2, so o3 has broader tracked availability.

Which offers better value, GR00T N1.7 3B or o3?+

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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