Helix 02 vs Muse Spark 1.3

At a Glance

Compare
Helix 02Figure
Pricing and Limits
Context windowMaximum documented tokensNot reportedNot reported
Model facts checkedAug 29, 2026View model evidence →Sep 4, 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

FieldHelix 02Muse Spark 1.3
DeveloperFigureMeta
FamilyHelixMuse Spark
ModelHelix 02Muse Spark 1.3
Version02Muse Spark 1.3
Lifecycleactivepreview
Released2026-01-052026-09-02
Knowledge cutoffUnknownUnknown
Input modalitiesText, Image, Robot stateText, Image, Video
Output modalitiesRobot actionText
Context windowUnknownUnknown
Total parametersUnknownUnknown
Active parametersUnknownUnknown
LicenseUnknownUnknown
Open weightsNoNo
API availableNoYes
Self-hostableNoNo
Provider accessUnknownUnknown
Capabilitiesdexterous-manipulation, long-horizon-control, tactile-control, whole-body-controlchat, computer-use, generation, reasoning, research, tools
Robotics model typeVision-language-action modelUnknown
Action representationFull-body joint targetsUnknown
Control architectureSemantic reasoning, visuomotor policy, and kHz whole-body controllerUnknown
Inference locationOn deviceUnknown
Native control rate (Hz)UnknownUnknown
Supported embodimentsFigure 03Unknown
Training dataFigure reports more than 1,000 hours of human motion data plus sim-to-real reinforcement learning for its whole-body controller.Unknown

Helix 02 Capabilities

dexterous-manipulationlong-horizon-controltactile-controlwhole-body-control
Model typeVision-language-action model
InferenceOn device
Action representationFull-body joint targets
Supported embodiments1
Canonical IDfigure/helix-02

Muse Spark 1.3 Capabilities

chatcomputer-usegenerationreasoningresearchtools
Model typeUnknown
InferenceUnknown
Action representationUnknown
Supported embodimentsUnknown
Canonical IDmeta-llama/muse-spark-1.3

Primary Evidence

Sources and Freshness

Questions

Helix 02 vs Muse Spark 1.3 FAQs

Is Helix 02 or Muse Spark 1.3 better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Helix 02 and Muse Spark 1.3, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Helix 02 or Muse Spark 1.3?+

Neither model has a directly sourced input price in this comparison. Neither model has a directly sourced output price in this comparison.

Which has a larger context window, Helix 02 or Muse Spark 1.3?+

Neither model has a larger sourced context window in this comparison. Helix 02 is — and Muse Spark 1.3 is —.

Which performs better in benchmarks, Helix 02 or Muse Spark 1.3?+

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

Can Helix 02 or Muse Spark 1.3 be self-hosted?+

Both models have the same recorded self-hosting status: unsupported. Helix 02 is not marked open weight; Muse Spark 1.3 is not marked open weight.

Can Helix 02 and Muse Spark 1.3 understand images?+

Helix 02 is documented with image input; Muse Spark 1.3 is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Helix 02 or Muse Spark 1.3?+

Neither has a larger sourced maximum output. Helix 02 is — and Muse Spark 1.3 is —.

Do Helix 02 and Muse Spark 1.3 support reasoning and tool use?+

Helix 02: image input. Muse Spark 1.3: reasoning, tool calling, and image input. Feature support does not establish relative quality.

Which is available from more inference providers, Helix 02 or Muse Spark 1.3?+

Helix 02 has 0 sourced provider routes; Muse Spark 1.3 has 0, a tie.

Which offers better value, Helix 02 or Muse Spark 1.3?+

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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