ERNIE 5.1 vs pi 0.7

At a Glance

Compare
pi 0.7Physical Intelligence
Pricing and Limits
Context windowMaximum documented tokens131KNot reported
Model facts checkedAug 29, 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

FieldERNIE 5.1pi 0.7
DeveloperBaiduPhysical Intelligence
FamilyErnie 5pi
ModelERNIE 5.1pi 0.7
VersionERNIE 5.10.7
Lifecycleactiveactive
ReleasedUnknown2026-04-16
Knowledge cutoffUnknownUnknown
Input modalitiesTextText, Image, Robot state
Output modalitiesTextRobot action
Context window131KUnknown
Total parametersUnknownUnknown
Active parametersUnknownUnknown
LicenseUnknownUnknown
Open weightsNoNo
API availableYesUnknown
Self-hostableNoUnknown
Provider accessBaidu Qianfan (Standard)Unknown
Capabilitiesagents, chat, reasoning, search, toolscross-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.

ERNIE 5.1 Capabilities

agentschatreasoningsearchtools
Model typeUnknown
InferenceUnknown
Action representationUnknown
Supported embodimentsUnknown
Canonical IDbaidu/ernie-5.1

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

ERNIE 5.1 vs pi 0.7 FAQs

Is ERNIE 5.1 or pi 0.7 better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both ERNIE 5.1 and pi 0.7, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, ERNIE 5.1 or pi 0.7?+

Only ERNIE 5.1 has a directly sourced input price: $4.00 per million tokens. Only ERNIE 5.1 has a directly sourced output price: $18.00 per million tokens.

Which has a larger context window, ERNIE 5.1 or pi 0.7?+

Neither model has a larger sourced context window in this comparison. ERNIE 5.1 is 131K and pi 0.7 is —.

Which performs better in benchmarks, ERNIE 5.1 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 ERNIE 5.1 or pi 0.7 be self-hosted?+

Neither model is the only model in this pair currently marked as self-hostable. ERNIE 5.1 is not marked open weight; pi 0.7 is not marked open weight.

Can ERNIE 5.1 and pi 0.7 understand images?+

ERNIE 5.1 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, ERNIE 5.1 or pi 0.7?+

Neither has a larger sourced maximum output. ERNIE 5.1 is 66K and pi 0.7 is —.

Do ERNIE 5.1 and pi 0.7 support reasoning and tool use?+

ERNIE 5.1: reasoning and tool calling. pi 0.7: image input. Feature support does not establish relative quality.

Which is available from more inference providers, ERNIE 5.1 or pi 0.7?+

ERNIE 5.1 has 1 sourced provider route; pi 0.7 has 0, so ERNIE 5.1 has broader tracked availability.

Which offers better value, ERNIE 5.1 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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