Helix 02Figure source ↗Helix 02 vs GR00T N1.7 3B
At a Glance
| Compare | Helix 02Figure | GR00T N1.7 3BNVIDIA |
|---|---|---|
| Pricing and Limits | ||
| Context windowMaximum documented tokens | Not reported | Not reported |
| Model facts checked | Aug 29, 2026View model evidence → | Aug 29, 2026View model evidence → |
Available Benchmarks
Helix 02Figure source ↗
GR00T N1.7 3BNVIDIA source ↗Side-by-Side Facts
| Field | Helix 02 | GR00T N1.7 3B |
|---|---|---|
| Developer | Figure | NVIDIA |
| Family | Helix | Isaac GR00T |
| Model | Helix 02 | GR00T N1.7 3B |
| Version | 02 | N1.7 |
| Lifecycle | active | active |
| Released | 2026-01-05 | 2026-07-07 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Robot state | Text, Image, Robot state |
| Output modalities | Robot action | Robot action |
| Context window | Unknown | Unknown |
| Total parameters | Unknown | 3B |
| Active parameters | Unknown | Unknown |
| License | Unknown | apache-2.0 |
| Open weights | No | Yes |
| API available | No | No |
| Self-hostable | No | Yes |
| Provider access | Unknown | Unknown |
| Capabilities | dexterous-manipulation, long-horizon-control, tactile-control, whole-body-control | cross-embodiment, dexterous-manipulation, whole-body-control, fine-tuning |
| Robotics model type | Vision-language-action model | Vision-language-action model |
| Action representation | Full-body joint targets | Predictive chunks of relative joint motions |
| Control architecture | Semantic reasoning, visuomotor policy, and kHz whole-body controller | Vision-language backbone with action expert |
| Inference location | On device | On device |
| Native control rate (Hz) | Unknown | Unknown |
| Supported embodiments | Figure 03 | Unitree G1, AgiBot Genie-1, Fourier GR-1, bimanual manipulation platforms |
| Training data | Figure reports more than 1,000 hours of human motion data plus sim-to-real reinforcement learning for its whole-body controller. | Mixture of real teleoperation, synthetic robot data, and internet-scale video described by NVIDIA. |
Helix 02 Capabilities
GR00T N1.7 3B Capabilities
Primary Evidence
Sources and Freshness
Questions
Helix 02 vs GR00T N1.7 3B FAQs
Is Helix 02 or GR00T N1.7 3B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Helix 02 and GR00T N1.7 3B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Helix 02 or GR00T N1.7 3B?+
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, Helix 02 or GR00T N1.7 3B?+
Neither model has a sourced context window in this comparison, so a larger limit cannot be determined.
Which performs better in benchmarks, Helix 02 or GR00T N1.7 3B?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Helix 02 or GR00T N1.7 3B be self-hosted?+
GR00T N1.7 3B is documented as self-hostable; Helix 02 is documented as not self-hostable. Helix 02 is not marked open weight; GR00T N1.7 3B is open weight.
Can Helix 02 and GR00T N1.7 3B understand images?+
Helix 02 is documented with image input; GR00T N1.7 3B is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Helix 02 or GR00T N1.7 3B?+
Neither model has a sourced maximum output in this comparison, so a larger limit cannot be determined.
Do Helix 02 and GR00T N1.7 3B support reasoning and tool use?+
Helix 02: image input. GR00T N1.7 3B: image input. Feature support does not establish relative quality.
Which is available from more inference providers, Helix 02 or GR00T N1.7 3B?+
Helix 02 has 0 sourced provider routes; GR00T N1.7 3B has 0, a tie.
Which offers better value, Helix 02 or GR00T N1.7 3B?+
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.