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Granite Embedding 278m Multilingual vs pi 0.7

At a Glance

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pi 0.7Physical Intelligence
Pricing and Limits
Context windowMaximum documented tokens1KNot 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

Fieldgranite-embedding-278m-multilingualpi 0.7
DeveloperIBMPhysical Intelligence
FamilyGranite Embedding 278m Multilingualpi
Modelgranite-embedding-278m-multilingualpi 0.7
Versiongranite-embedding-278m-multilingual0.7
Lifecycleactiveactive
Released2024-12-182026-04-16
Knowledge cutoffUnknownUnknown
Input modalitiesTextText, Image, Robot state
Output modalitiesEmbeddingRobot action
Context window1KUnknown
Total parameters278MUnknown
Active parametersUnknownUnknown
Licenseapache-2.0Unknown
Open weightsYesNo
API availableYesUnknown
Self-hostableYesUnknown
Provider accessHugging Face (Standard), IBM watsonx.ai (Pay as you go)Unknown
Capabilitiesembeddingscross-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.

Granite Embedding 278m Multilingual Capabilities

embeddings
Model typeUnknown
InferenceUnknown
Action representationUnknown
Supported embodimentsUnknown
Canonical IDibm-granite/granite-embedding-278m-multilingual

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

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Primary Evidence

Sources and Freshness

ibm-granite/granite-embedding-278m-multilingual developer model repositoryIBM · observed Aug 28, 2026pi 0.7: a Steerable Model with Emergent CapabilitiesPhysical Intelligence · observed Aug 29, 2026

Questions

Granite Embedding 278m Multilingual vs pi 0.7 FAQs

Is Granite Embedding 278m Multilingual or pi 0.7 better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Granite Embedding 278m Multilingual and pi 0.7, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Granite Embedding 278m Multilingual or pi 0.7?+

Only Granite Embedding 278m Multilingual has a directly sourced input price: $0.106 per million tokens. Neither model has a directly sourced output price in this comparison.

Which has a larger context window, Granite Embedding 278m Multilingual or pi 0.7?+

Neither model has a larger sourced context window in this comparison. Granite Embedding 278m Multilingual is 1K and pi 0.7 is —.

Which performs better in benchmarks, Granite Embedding 278m Multilingual 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 Granite Embedding 278m Multilingual or pi 0.7 be self-hosted?+

Granite Embedding 278m Multilingual is the only model in this pair currently marked as self-hostable. Granite Embedding 278m Multilingual is open weight; pi 0.7 is not marked open weight.

Can Granite Embedding 278m Multilingual and pi 0.7 understand images?+

Granite Embedding 278m Multilingual 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, Granite Embedding 278m Multilingual or pi 0.7?+

Neither has a larger sourced maximum output. Granite Embedding 278m Multilingual is — and pi 0.7 is —.

Do Granite Embedding 278m Multilingual and pi 0.7 support reasoning and tool use?+

Granite Embedding 278m Multilingual: 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, Granite Embedding 278m Multilingual or pi 0.7?+

Granite Embedding 278m Multilingual has 2 sourced provider routes; pi 0.7 has 0, so Granite Embedding 278m Multilingual has broader tracked availability.

Which offers better value, Granite Embedding 278m Multilingual 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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