pi 0.7 vs Hy4 preview

At a Glance

Compare
pi 0.7Physical Intelligence
Pricing and Limits
Context windowMaximum documented tokensNot reported1,000K
Model facts checkedAug 29, 2026View model evidence →Sep 2, 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 0.7Hy4 preview
DeveloperPhysical IntelligenceTencent
FamilypiHy4
Modelpi 0.7Hy4 preview
Version0.7Hy4 preview
Lifecycleactivepreview
Released2026-04-162026-08-28
Knowledge cutoffUnknownUnknown
Input modalitiesText, Image, Robot stateText
Output modalitiesRobot actionText
Context windowUnknown1,000K
Total parametersUnknown770B
Active parametersUnknown49B
LicenseUnknownapache-2.0
Open weightsNoYes
API availableUnknownYes
Self-hostableUnknownYes
Provider accessUnknownOpenrouter (Standard)
Capabilitiescross-embodiment, dexterous-manipulation, language-steering, visual-subgoalschat, generation, reasoning, tools
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

Hy4 preview Capabilities

chatgenerationreasoningtools
Model typeUnknown
InferenceUnknown
Action representationUnknown
Supported embodimentsUnknown
Canonical IDtencent/Hy4-preview

Primary Evidence

Sources and Freshness

Questions

pi 0.7 vs Hy4 preview FAQs

Is pi 0.7 or Hy4 preview better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both pi 0.7 and Hy4 preview, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, pi 0.7 or Hy4 preview?+

Only Hy4 preview has a directly sourced input price: $0.834 per million tokens. Only Hy4 preview has a directly sourced output price: $2.501 per million tokens.

Which has a larger context window, pi 0.7 or Hy4 preview?+

Neither model has a larger sourced context window in this comparison. pi 0.7 is — and Hy4 preview is 1,000K.

Which performs better in benchmarks, pi 0.7 or Hy4 preview?+

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 Hy4 preview be self-hosted?+

Hy4 preview is the only model in this pair currently marked as self-hostable. pi 0.7 is not marked open weight; Hy4 preview is open weight.

Can pi 0.7 and Hy4 preview understand images?+

pi 0.7 is documented with image input; Hy4 preview is not documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, pi 0.7 or Hy4 preview?+

Neither has a larger sourced maximum output. pi 0.7 is — and Hy4 preview is —.

Do pi 0.7 and Hy4 preview support reasoning and tool use?+

pi 0.7: image input. Hy4 preview: reasoning and tool calling. Feature support does not establish relative quality.

Which is available from more inference providers, pi 0.7 or Hy4 preview?+

pi 0.7 has 0 sourced provider routes; Hy4 preview has 1, so Hy4 preview has broader tracked availability.

Which offers better value, pi 0.7 or Hy4 preview?+

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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