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pi 0.7 vs Bonsai 27B

At a Glance

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pi 0.7Physical Intelligence
Bonsai 27BPrismML
Pricing and Limits
Context windowMaximum documented tokensNot reported262K
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 27B
DeveloperPhysical IntelligencePrismML
FamilypiBonsai 27b
Modelpi 0.7Bonsai 27B
Version0.7Bonsai 27B
Lifecycleactiveactive
Released2026-04-162026-07-04
Knowledge cutoffUnknownUnknown
Input modalitiesText, Image, Robot stateText, Image
Output modalitiesRobot actionText
Context windowUnknown262K
Total parametersUnknown27B
Active parametersUnknownUnknown
LicenseUnknownapache-2.0
Open weightsNoYes
API availableUnknownNo
Self-hostableUnknownYes
Provider accessUnknownUnknown
Capabilitiescross-embodiment, dexterous-manipulation, language-steering, visual-subgoalschat, generation, reasoning, tools, vision
Base modelUnknownQwen3.6 27B
Effective bit widthUnknown1 bit per weight
Language model sizeUnknown3.53 GiB
Weight formatUnknownBinary Q1_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

Bonsai 27B Capabilities

chatgenerationreasoningtoolsvision
Model typeUnknown
InferenceUnknown
Action representationUnknown
Supported embodimentsUnknown
Canonical IDprism-ml/Bonsai-27B

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Primary Evidence

Sources and Freshness

pi 0.7: a Steerable Model with Emergent CapabilitiesPhysical Intelligence · observed Aug 29, 2026Bonsai 27B | PrismML DocsPrismML · observed Sep 18, 2026

Questions

pi 0.7 vs Bonsai 27B FAQs

Is pi 0.7 or Bonsai 27B better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both pi 0.7 and Bonsai 27B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, pi 0.7 or 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, pi 0.7 or Bonsai 27B?+

Neither model has a larger sourced context window in this comparison. pi 0.7 is — and Bonsai 27B is 262K.

Which performs better in benchmarks, pi 0.7 or Bonsai 27B?+

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 27B be self-hosted?+

Bonsai 27B is the only model in this pair currently marked as self-hostable. pi 0.7 is not marked open weight; Bonsai 27B is open weight.

Can pi 0.7 and Bonsai 27B understand images?+

pi 0.7 is documented with image input; Bonsai 27B is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, pi 0.7 or Bonsai 27B?+

Neither has a larger sourced maximum output. pi 0.7 is — and Bonsai 27B is —.

Do pi 0.7 and Bonsai 27B support reasoning and tool use?+

pi 0.7: image input. Bonsai 27B: reasoning, tool calling, and image input. Feature support does not establish relative quality.

Which is available from more inference providers, pi 0.7 or Bonsai 27B?+

pi 0.7 has 0 sourced provider routes; Bonsai 27B has 0, a tie.

Which offers better value, pi 0.7 or 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.

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