phi-4 vs pi 0.7

At a Glance

Compare
phi-4Microsoft
pi 0.7Physical Intelligence
Pricing and Limits
Context windowMaximum documented tokens16KNot reported
Model facts checkedAug 28, 2026View model evidence →Aug 29, 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

Fieldphi-4pi 0.7
DeveloperMicrosoftPhysical Intelligence
FamilyPhi 4pi
Modelphi-4pi 0.7
Versionphi-40.7
Lifecycleactiveactive
Released2024-12-122026-04-16
Knowledge cutoffUnknownUnknown
Input modalitiesTextText, Image, Robot state
Output modalitiesTextRobot action
Context window16KUnknown
Total parameters14.7BUnknown
Active parametersUnknownUnknown
LicensemitUnknown
Open weightsYesNo
API availableYesUnknown
Self-hostableYesUnknown
Provider accessDeepinfra (Standard), Hugging Face (Standard), Openrouter (Standard)Unknown
Capabilitieschat, generationcross-embodiment, dexterous-manipulation, language-steering, visual-subgoals
Robotics model typeUnknownVision-language-action model
Action representationUnknownContinuous robot actions conditioned by multimodal prompts
Control architectureUnknownHigh-level policy, world model, and action expert
Inference locationUnknownUnknown
Native control rate (Hz)UnknownUnknown
Supported embodimentsUnknownmobile manipulators, bimanual UR5e, multiple fixed manipulators
Training dataUnknownRobot demonstrations, autonomous data, egocentric human data, and multimodal web data described by the publisher.

phi-4 Capabilities

chatgeneration
Model typeUnknown
InferenceUnknown
Action representationUnknown
Supported embodimentsUnknown
Canonical IDmicrosoft/phi-4

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

Primary Evidence

Sources and Freshness

Questions

phi-4 vs pi 0.7 FAQs

Is phi-4 or pi 0.7 better for coding?+

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

Which is cheaper, phi-4 or pi 0.7?+

Only phi-4 has a directly sourced input price: $0.070 per million tokens. Only phi-4 has a directly sourced output price: $0.14 per million tokens.

Which has a larger context window, phi-4 or pi 0.7?+

Neither model has a larger sourced context window in this comparison. phi-4 is 16K and pi 0.7 is —.

Which performs better in benchmarks, phi-4 or pi 0.7?+

There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.

Can phi-4 or pi 0.7 be self-hosted?+

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

Can phi-4 and pi 0.7 understand images?+

phi-4 is not documented with image input; pi 0.7 is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, phi-4 or pi 0.7?+

Neither has a larger sourced maximum output. phi-4 is — and pi 0.7 is —.

Do phi-4 and pi 0.7 support reasoning and tool use?+

phi-4: none of these features are definitively sourced. pi 0.7: image input. Feature support does not establish relative quality.

Which is available from more inference providers, phi-4 or pi 0.7?+

phi-4 has 3 sourced provider routes; pi 0.7 has 0, so phi-4 has broader tracked availability.

Which offers better value, phi-4 or pi 0.7?+

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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