Gemini Robotics 2 vs Ternary Bonsai 1.7B
At a Glance
| Compare | Gemini Robotics 2Google DeepMind | Ternary Bonsai 1.7BPrismML |
|---|---|---|
| Pricing and Limits | ||
| Context windowMaximum documented tokens | Not reported | 33K |
| Model facts checked | Aug 29, 2026View model evidence → | Sep 18, 2026View model evidence → |
Available Benchmarks
Side-by-Side Facts
| Field | Gemini Robotics 2 | Ternary Bonsai 1.7B |
|---|---|---|
| Developer | Google DeepMind | PrismML |
| Family | Gemini Robotics | Bonsai 1 7b |
| Model | Gemini Robotics 2 | Ternary Bonsai 1.7B |
| Version | 2 | Ternary Bonsai 1.7B |
| Lifecycle | preview | active |
| Released | 2026-07-30 | 2026-04-18 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Robot state | Text |
| Output modalities | Robot action | Text |
| Context window | Unknown | 33K |
| Total parameters | Unknown | 1.7B |
| Active parameters | Unknown | Unknown |
| License | Unknown | apache-2.0 |
| Open weights | No | Yes |
| API available | Unknown | No |
| Self-hostable | Unknown | Yes |
| Provider access | Unknown | Unknown |
| Capabilities | cross-embodiment, dexterous-manipulation, whole-body-control, multi-robot-collaboration | chat, generation |
| Effective bit width | Unknown | 1.58 bits per weight |
| Weight size | Unknown | 0.46 GB |
| Weight format | Unknown | Ternary Q2_0 |
| Robotics model type | Vision-language-action model | Unknown |
| Action representation | Motor-control actions | Unknown |
| Control architecture | Vision-language-action model | Unknown |
| Inference location | Unknown | Unknown |
| Native control rate (Hz) | Unknown | Unknown |
| Supported embodiments | Apptronik Apollo 2 with Inspire hands, Apptronik Apollo 2 with Sharpa hands, Franka Duo with Robotiq gripper | Unknown |
| Training data | Not disclosed in the cited model page. | Unknown |
Gemini Robotics 2 Capabilities
Ternary Bonsai 1.7B Capabilities
Primary Evidence
Sources and Freshness
Questions
Gemini Robotics 2 vs Ternary Bonsai 1.7B FAQs
Is Gemini Robotics 2 or Ternary Bonsai 1.7B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Gemini Robotics 2 and Ternary Bonsai 1.7B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Gemini Robotics 2 or Ternary Bonsai 1.7B?+
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, Gemini Robotics 2 or Ternary Bonsai 1.7B?+
Neither model has a larger sourced context window in this comparison. Gemini Robotics 2 is — and Ternary Bonsai 1.7B is 33K.
Which performs better in benchmarks, Gemini Robotics 2 or Ternary Bonsai 1.7B?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Gemini Robotics 2 or Ternary Bonsai 1.7B be self-hosted?+
Ternary Bonsai 1.7B is the only model in this pair currently marked as self-hostable. Gemini Robotics 2 is not marked open weight; Ternary Bonsai 1.7B is open weight.
Can Gemini Robotics 2 and Ternary Bonsai 1.7B understand images?+
Gemini Robotics 2 is documented with image input; Ternary Bonsai 1.7B is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Gemini Robotics 2 or Ternary Bonsai 1.7B?+
Neither has a larger sourced maximum output. Gemini Robotics 2 is — and Ternary Bonsai 1.7B is —.
Do Gemini Robotics 2 and Ternary Bonsai 1.7B support reasoning and tool use?+
Gemini Robotics 2: image input. Ternary Bonsai 1.7B: none of these features are definitively sourced. Feature support does not establish relative quality.
Which is available from more inference providers, Gemini Robotics 2 or Ternary Bonsai 1.7B?+
Gemini Robotics 2 has 0 sourced provider routes; Ternary Bonsai 1.7B has 0, a tie.
Which offers better value, Gemini Robotics 2 or Ternary Bonsai 1.7B?+
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.