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pi 0.7 vs Ternary Bonsai 1.7B

At a Glance

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pi 0.7Physical Intelligence
Pricing and Limits
Context windowMaximum documented tokensNot reported33K
Model facts checkedAug 29, 2026View model evidence →Sep 18, 2026View model evidence →
Different Model RolesThese models do not share a sourced market category. Their primary-source facts remain comparable below, while performance claims require matched evidence.

Available Benchmarks

All benchmark results →
No Protocol-Matched Benchmark Yet.Results appear here only when both models share the same benchmark version, metric, evaluation protocol, and evidence class.

Side-by-Side Facts

Fieldpi 0.7Ternary Bonsai 1.7B
DeveloperPhysical IntelligencePrismML
FamilypiBonsai 1 7b
Modelpi 0.7Ternary Bonsai 1.7B
Version0.7Ternary Bonsai 1.7B
Lifecycleactiveactive
Released2026-04-162026-04-18
Knowledge cutoffUnknownUnknown
Input modalitiesText, Image, Robot stateText
Output modalitiesRobot actionText
Context windowUnknown33K
Total parametersUnknown1.7B
Active parametersUnknownUnknown
LicenseUnknownapache-2.0
Open weightsNoYes
API availableUnknownNo
Self-hostableUnknownYes
Provider accessUnknownUnknown
Capabilitiescross-embodiment, dexterous-manipulation, language-steering, visual-subgoalschat, generation
Effective bit widthUnknown1.58 bits per weight
Weight sizeUnknown0.46 GB
Weight formatUnknownTernary Q2_0
Robotics model typeVision-language-action modelUnknown
Action representationContinuous robot actions conditioned by multimodal promptsUnknown
Control architectureHigh-level policy, world model, and action expertUnknown
Inference locationUnknownUnknown
Native control rate (Hz)UnknownUnknown
Supported embodimentsmobile manipulators, bimanual UR5e, multiple fixed manipulatorsUnknown
Training dataRobot demonstrations, autonomous data, egocentric human data, and multimodal web data described by the publisher.Unknown

pi 0.7 Capabilities

cross-embodimentdexterous-manipulationlanguage-steeringvisual-subgoals
Model typeVision-language-action model
InferenceUnknown
Action representationContinuous robot actions conditioned by multimodal prompts
Supported embodiments3
Canonical IDphysical-intelligence/pi-0.7

Ternary Bonsai 1.7B Capabilities

chatgeneration
Model typeUnknown
InferenceUnknown
Action representationUnknown
Supported embodimentsUnknown
Canonical IDprism-ml/Ternary-Bonsai-1.7B

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Primary Evidence

Sources and Freshness

pi 0.7: a Steerable Model with Emergent CapabilitiesPhysical Intelligence · observed Aug 29, 2026Bonsai 1.7B | PrismML DocsPrismML · observed Sep 18, 2026

Questions

pi 0.7 vs Ternary Bonsai 1.7B FAQs

Is pi 0.7 or Ternary Bonsai 1.7B better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both pi 0.7 and Ternary Bonsai 1.7B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, pi 0.7 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, pi 0.7 or Ternary Bonsai 1.7B?+

Neither model has a larger sourced context window in this comparison. pi 0.7 is — and Ternary Bonsai 1.7B is 33K.

Which performs better in benchmarks, pi 0.7 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 pi 0.7 or Ternary Bonsai 1.7B be self-hosted?+

Ternary Bonsai 1.7B is the only model in this pair currently marked as self-hostable. pi 0.7 is not marked open weight; Ternary Bonsai 1.7B is open weight.

Can pi 0.7 and Ternary Bonsai 1.7B understand images?+

pi 0.7 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, pi 0.7 or Ternary Bonsai 1.7B?+

Neither has a larger sourced maximum output. pi 0.7 is — and Ternary Bonsai 1.7B is —.

Do pi 0.7 and Ternary Bonsai 1.7B support reasoning and tool use?+

pi 0.7: 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, pi 0.7 or Ternary Bonsai 1.7B?+

pi 0.7 has 0 sourced provider routes; Ternary Bonsai 1.7B has 0, a tie.

Which offers better value, pi 0.7 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.

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