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Llama 4 Scout 17B 16E Instruct vs pi 0.7

At a Glance

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pi 0.7Physical Intelligence
Pricing and Limits
Context windowMaximum documented tokens10,000KNot 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

FieldLlama-4-Scout-17B-16E-Instructpi 0.7
DeveloperMetaPhysical Intelligence
FamilyLlama 4 Scout 17b 16e Instructpi
ModelLlama-4-Scout-17B-16E-Instructpi 0.7
VersionLlama-4-Scout-17B-16E-Instruct0.7
Lifecycleactiveactive
Released2025-04-052026-04-16
Knowledge cutoffUnknownUnknown
Input modalitiesText, ImageText, Image, Robot state
Output modalitiesTextRobot action
Context window10,000KUnknown
Total parameters108.6BUnknown
Active parameters17BUnknown
LicenseotherUnknown
Open weightsYesNo
API availableYesUnknown
Self-hostableYesUnknown
Provider accessDeepinfra (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard)Unknown
Capabilitieschat, generation, 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.

Llama 4 Scout 17B 16E Instruct Capabilities

chatgenerationtools
Model typeUnknown
InferenceUnknown
Action representationUnknown
Supported embodimentsUnknown
Canonical IDmeta-llama/Llama-4-Scout-17B-16E-Instruct

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

meta-llama/Llama-4-Scout-17B-16E-Instruct developer model repositoryMeta · observed Aug 28, 2026pi 0.7: a Steerable Model with Emergent CapabilitiesPhysical Intelligence · observed Aug 29, 2026

Questions

Llama 4 Scout 17B 16E Instruct vs pi 0.7 FAQs

Is Llama 4 Scout 17B 16E Instruct or pi 0.7 better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Llama 4 Scout 17B 16E Instruct and pi 0.7, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Llama 4 Scout 17B 16E Instruct or pi 0.7?+

Only Llama 4 Scout 17B 16E Instruct has a directly sourced input price: $0.10 per million tokens. Only Llama 4 Scout 17B 16E Instruct has a directly sourced output price: $0.30 per million tokens.

Which has a larger context window, Llama 4 Scout 17B 16E Instruct or pi 0.7?+

Neither model has a larger sourced context window in this comparison. Llama 4 Scout 17B 16E Instruct is 10,000K and pi 0.7 is —.

Which performs better in benchmarks, Llama 4 Scout 17B 16E Instruct 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 Llama 4 Scout 17B 16E Instruct or pi 0.7 be self-hosted?+

Llama 4 Scout 17B 16E Instruct is the only model in this pair currently marked as self-hostable. Llama 4 Scout 17B 16E Instruct is open weight; pi 0.7 is not marked open weight.

Can Llama 4 Scout 17B 16E Instruct and pi 0.7 understand images?+

Llama 4 Scout 17B 16E Instruct is 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, Llama 4 Scout 17B 16E Instruct or pi 0.7?+

Neither has a larger sourced maximum output. Llama 4 Scout 17B 16E Instruct is — and pi 0.7 is —.

Do Llama 4 Scout 17B 16E Instruct and pi 0.7 support reasoning and tool use?+

Llama 4 Scout 17B 16E Instruct: tool calling and image input. pi 0.7: image input. Feature support does not establish relative quality.

Which is available from more inference providers, Llama 4 Scout 17B 16E Instruct or pi 0.7?+

Llama 4 Scout 17B 16E Instruct has 4 sourced provider routes; pi 0.7 has 0, so Llama 4 Scout 17B 16E Instruct has broader tracked availability.

Which offers better value, Llama 4 Scout 17B 16E Instruct 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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