GR00T N1.7 3B vs Ternary Bonsai 27B
At a Glance
| Compare |  GR00T N1.7 3BNVIDIA |  Ternary Bonsai 27BPrismML |
|---|---|---|
| Pricing and Limits | ||
| Context windowMaximum documented tokens | Not reported | 262K |
| Model facts checked | Aug 29, 2026View model evidence → | Sep 18, 2026View model evidence → |
Available Benchmarks
Side-by-Side Facts
| Field | GR00T N1.7 3B | Ternary Bonsai 27B |
|---|---|---|
| Developer | NVIDIA | PrismML |
| Family | Isaac GR00T | Bonsai 27b |
| Model | GR00T N1.7 3B | Ternary Bonsai 27B |
| Version | N1.7 | Ternary Bonsai 27B |
| Lifecycle | active | active |
| Released | 2026-07-07 | 2026-07-04 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Robot state | Text, Image |
| Output modalities | Robot action | Text |
| Context window | Unknown | 262K |
| Total parameters | 3B | 27B |
| Active parameters | Unknown | Unknown |
| License | apache-2.0 | apache-2.0 |
| Open weights | Yes | Yes |
| API available | No | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Unknown | Together Ai (Standard) |
| Capabilities | cross-embodiment, dexterous-manipulation, whole-body-control, fine-tuning | chat, generation, reasoning, tools, vision |
| Base model | Unknown | Qwen3.6 27B |
| Effective bit width | Unknown | 1.58 bits per weight |
| Language model size | Unknown | 6.66 GiB |
| Weight format | Unknown | Ternary Q2_0 |
| 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
Ternary Bonsai 27B Capabilities
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Primary Evidence
Sources and Freshness
Questions
GR00T N1.7 3B vs Ternary Bonsai 27B FAQs
Is GR00T N1.7 3B or Ternary Bonsai 27B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both GR00T N1.7 3B and Ternary Bonsai 27B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, GR00T N1.7 3B or Ternary Bonsai 27B?+
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 Ternary Bonsai 27B?+
Neither model has a larger sourced context window in this comparison. GR00T N1.7 3B is — and Ternary Bonsai 27B is 262K.
Which performs better in benchmarks, GR00T N1.7 3B or Ternary Bonsai 27B?+
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 Ternary Bonsai 27B be self-hosted?+
Both models have the same recorded self-hosting status: supported. GR00T N1.7 3B is open weight; Ternary Bonsai 27B is open weight.
Can GR00T N1.7 3B and Ternary Bonsai 27B understand images?+
GR00T N1.7 3B is documented with image input; Ternary Bonsai 27B is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, GR00T N1.7 3B or Ternary Bonsai 27B?+
Neither has a larger sourced maximum output. GR00T N1.7 3B is — and Ternary Bonsai 27B is —.
Do GR00T N1.7 3B and Ternary Bonsai 27B support reasoning and tool use?+
GR00T N1.7 3B: image input. Ternary Bonsai 27B: 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 Ternary Bonsai 27B?+
GR00T N1.7 3B has 0 sourced provider routes; Ternary Bonsai 27B has 1, so Ternary Bonsai 27B has broader tracked availability.
Which offers better value, GR00T N1.7 3B or Ternary Bonsai 27B?+
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.