GR00T N1.7 3B vs Bonsai Image Binary 4B
At a Glance
| Compare |  GR00T N1.7 3BNVIDIA |  Bonsai Image Binary 4BPrismML |
|---|---|---|
| Pricing and Limits | ||
| Context windowMaximum documented tokens | Not reported | Not reported |
| Model facts checked | Aug 29, 2026View model evidence → | Sep 18, 2026View model evidence → |
Available Benchmarks
Side-by-Side Facts
| Field | GR00T N1.7 3B | Bonsai Image Binary 4B |
|---|---|---|
| Developer | NVIDIA | PrismML |
| Family | Isaac GR00T | Bonsai Image 4b |
| Model | GR00T N1.7 3B | Bonsai Image Binary 4B |
| Version | N1.7 | Bonsai Image Binary 4B |
| Lifecycle | active | active |
| Released | 2026-07-07 | 2026-05-18 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Robot state | Text |
| Output modalities | Robot action | Image |
| Context window | Unknown | Unknown |
| Total parameters | 3B | 4B |
| Active parameters | Unknown | Unknown |
| License | apache-2.0 | apache-2.0 |
| Open weights | Yes | Yes |
| API available | No | No |
| Self-hostable | Yes | Yes |
| Provider access | Unknown | Unknown |
| Capabilities | cross-embodiment, dexterous-manipulation, whole-body-control, fine-tuning | generation |
| Base model | Unknown | FLUX.2 Klein 4B |
| Default resolution | Unknown | 512 × 512 |
| Transformer size | Unknown | 0.93 GB |
| Weight format | Unknown | Binary weights with FP16 group scales |
| Robotics model type | Vision-language-action model | Unknown |
| Action representation | Predictive chunks of relative joint motions | Unknown |
| Control architecture | Vision-language backbone with action expert | Unknown |
| Inference location | On device | Unknown |
| Native control rate (Hz) | Unknown | Unknown |
| Supported embodiments | Unitree G1, AgiBot Genie-1, Fourier GR-1, bimanual manipulation platforms | Unknown |
| Training data | Mixture of real teleoperation, synthetic robot data, and internet-scale video described by NVIDIA. | Unknown |
GR00T N1.7 3B Capabilities
Bonsai Image Binary 4B Capabilities
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Primary Evidence
Sources and Freshness
Questions
GR00T N1.7 3B vs Bonsai Image Binary 4B FAQs
Is GR00T N1.7 3B or Bonsai Image Binary 4B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both GR00T N1.7 3B and Bonsai Image Binary 4B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, GR00T N1.7 3B or Bonsai Image Binary 4B?+
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 Bonsai Image Binary 4B?+
Neither model has a larger sourced context window in this comparison. GR00T N1.7 3B is — and Bonsai Image Binary 4B is —.
Which performs better in benchmarks, GR00T N1.7 3B or Bonsai Image Binary 4B?+
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 Bonsai Image Binary 4B be self-hosted?+
Both models have the same recorded self-hosting status: supported. GR00T N1.7 3B is open weight; Bonsai Image Binary 4B is open weight.
Can GR00T N1.7 3B and Bonsai Image Binary 4B understand images?+
GR00T N1.7 3B is documented with image input; Bonsai Image Binary 4B is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, GR00T N1.7 3B or Bonsai Image Binary 4B?+
Neither has a larger sourced maximum output. GR00T N1.7 3B is — and Bonsai Image Binary 4B is —.
Do GR00T N1.7 3B and Bonsai Image Binary 4B support reasoning and tool use?+
GR00T N1.7 3B: image input. Bonsai Image Binary 4B: 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 Bonsai Image Binary 4B?+
GR00T N1.7 3B has 0 sourced provider routes; Bonsai Image Binary 4B has 0, a tie.
Which offers better value, GR00T N1.7 3B or Bonsai Image Binary 4B?+
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.