pi 0.7 vs Bonsai 8B
At a Glance
| Compare | pi 0.7Physical Intelligence | Bonsai 8BPrismML |
|---|---|---|
| Pricing and Limits | ||
| Context windowMaximum documented tokens | Not reported | 66K |
| Model facts checked | Aug 29, 2026View model evidence → | Sep 18, 2026View model evidence → |
Available Benchmarks
Side-by-Side Facts
| Field | pi 0.7 | Bonsai 8B |
|---|---|---|
| Developer | Physical Intelligence | PrismML |
| Family | pi | Bonsai 8b |
| Model | pi 0.7 | Bonsai 8B |
| Version | 0.7 | Bonsai 8B |
| Lifecycle | active | active |
| Released | 2026-04-16 | 2026-03-18 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Robot state | Text |
| Output modalities | Robot action | Text |
| Context window | Unknown | 66K |
| Total parameters | Unknown | 8.2B |
| 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, language-steering, visual-subgoals | chat, generation |
| Effective bit width | Unknown | 1 bit per weight |
| Weight size | Unknown | 1.16 GB |
| Weight format | Unknown | Binary Q1_0 |
| Robotics model type | Vision-language-action model | Unknown |
| Action representation | Continuous robot actions conditioned by multimodal prompts | Unknown |
| Control architecture | High-level policy, world model, and action expert | Unknown |
| Inference location | Unknown | Unknown |
| Native control rate (Hz) | Unknown | Unknown |
| Supported embodiments | mobile manipulators, bimanual UR5e, multiple fixed manipulators | Unknown |
| Training data | Robot demonstrations, autonomous data, egocentric human data, and multimodal web data described by the publisher. | Unknown |
pi 0.7 Capabilities
Bonsai 8B Capabilities
Primary Evidence
Sources and Freshness
Questions
pi 0.7 vs Bonsai 8B FAQs
Is pi 0.7 or Bonsai 8B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both pi 0.7 and Bonsai 8B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, pi 0.7 or Bonsai 8B?+
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, pi 0.7 or Bonsai 8B?+
Neither model has a larger sourced context window in this comparison. pi 0.7 is — and Bonsai 8B is 66K.
Which performs better in benchmarks, pi 0.7 or Bonsai 8B?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can pi 0.7 or Bonsai 8B be self-hosted?+
Bonsai 8B is the only model in this pair currently marked as self-hostable. pi 0.7 is not marked open weight; Bonsai 8B is open weight.
Can pi 0.7 and Bonsai 8B understand images?+
pi 0.7 is documented with image input; Bonsai 8B is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, pi 0.7 or Bonsai 8B?+
Neither has a larger sourced maximum output. pi 0.7 is — and Bonsai 8B is —.
Do pi 0.7 and Bonsai 8B support reasoning and tool use?+
pi 0.7: image input. Bonsai 8B: none of these features are definitively sourced. Feature support does not establish relative quality.
Which is available from more inference providers, pi 0.7 or Bonsai 8B?+
pi 0.7 has 0 sourced provider routes; Bonsai 8B has 0, a tie.
Which offers better value, pi 0.7 or Bonsai 8B?+
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.