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pi 0.7 vs Bonsai Image Binary 4B

At a Glance

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pi 0.7Physical Intelligence
Pricing and Limits
Context windowMaximum documented tokensNot reportedNot reported
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 0.7Bonsai Image Binary 4B
DeveloperPhysical IntelligencePrismML
FamilypiBonsai Image 4b
Modelpi 0.7Bonsai Image Binary 4B
Version0.7Bonsai Image Binary 4B
Lifecycleactiveactive
Released2026-04-162026-05-18
Knowledge cutoffUnknownUnknown
Input modalitiesText, Image, Robot stateText
Output modalitiesRobot actionImage
Context windowUnknownUnknown
Total parametersUnknown4B
Active parametersUnknownUnknown
LicenseUnknownapache-2.0
Open weightsNoYes
API availableUnknownNo
Self-hostableUnknownYes
Provider accessUnknownUnknown
Capabilitiescross-embodiment, dexterous-manipulation, language-steering, visual-subgoalsgeneration
Base modelUnknownFLUX.2 Klein 4B
Default resolutionUnknown512 × 512
Transformer sizeUnknown0.93 GB
Weight formatUnknownBinary weights with FP16 group scales
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

Bonsai Image Binary 4B Capabilities

generation
Model typeUnknown
InferenceUnknown
Action representationUnknown
Supported embodimentsUnknown
Canonical IDprism-ml/Bonsai-Image-Binary-4B

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Primary Evidence

Sources and Freshness

pi 0.7: a Steerable Model with Emergent CapabilitiesPhysical Intelligence · observed Aug 29, 2026Bonsai Image 4B | PrismML DocsPrismML · observed Sep 18, 2026

Questions

pi 0.7 vs Bonsai Image Binary 4B FAQs

Is pi 0.7 or Bonsai Image Binary 4B better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both pi 0.7 and Bonsai Image Binary 4B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, pi 0.7 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, pi 0.7 or Bonsai Image Binary 4B?+

Neither model has a larger sourced context window in this comparison. pi 0.7 is — and Bonsai Image Binary 4B is —.

Which performs better in benchmarks, pi 0.7 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 pi 0.7 or Bonsai Image Binary 4B be self-hosted?+

Bonsai Image Binary 4B is the only model in this pair currently marked as self-hostable. pi 0.7 is not marked open weight; Bonsai Image Binary 4B is open weight.

Can pi 0.7 and Bonsai Image Binary 4B understand images?+

pi 0.7 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, pi 0.7 or Bonsai Image Binary 4B?+

Neither has a larger sourced maximum output. pi 0.7 is — and Bonsai Image Binary 4B is —.

Do pi 0.7 and Bonsai Image Binary 4B support reasoning and tool use?+

pi 0.7: 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, pi 0.7 or Bonsai Image Binary 4B?+

pi 0.7 has 0 sourced provider routes; Bonsai Image Binary 4B has 0, a tie.

Which offers better value, pi 0.7 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.

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