ERNIE 5.1 vs Helix 02

At a Glance

Compare
Helix 02Figure
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 5.1Helix 02
DeveloperBaiduFigure
FamilyErnie 5Helix
ModelERNIE 5.1Helix 02
VersionERNIE 5.102
Lifecycleactiveactive
ReleasedUnknown2026-01-05
Knowledge cutoffUnknownUnknown
Input modalitiesTextText, Image, Robot state
Output modalitiesTextRobot action
Context window131KUnknown
Total parametersUnknownUnknown
Active parametersUnknownUnknown
LicenseUnknownUnknown
Open weightsNoNo
API availableYesNo
Self-hostableNoNo
Provider accessBaidu Qianfan (Standard)Unknown
Capabilitiesagents, chat, reasoning, search, toolsdexterous-manipulation, long-horizon-control, tactile-control, whole-body-control
Robotics model typeUnknownVision-language-action model
Action representationUnknownFull-body joint targets
Control architectureUnknownSemantic reasoning, visuomotor policy, and kHz whole-body controller
Inference locationUnknownOn device
Native control rate (Hz)UnknownUnknown
Supported embodimentsUnknownFigure 03
Training dataUnknownFigure reports more than 1,000 hours of human motion data plus sim-to-real reinforcement learning for its whole-body controller.

ERNIE 5.1 Capabilities

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

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

Primary Evidence

Sources and Freshness

Questions

ERNIE 5.1 vs Helix 02 FAQs

Is ERNIE 5.1 or Helix 02 better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both ERNIE 5.1 and Helix 02, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, ERNIE 5.1 or Helix 02?+

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 Helix 02?+

Neither model has a larger sourced context window in this comparison. ERNIE 5.1 is 131K and Helix 02 is —.

Which performs better in benchmarks, ERNIE 5.1 or Helix 02?+

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 Helix 02 be self-hosted?+

Both models have the same recorded self-hosting status: unsupported. ERNIE 5.1 is not marked open weight; Helix 02 is not marked open weight.

Can ERNIE 5.1 and Helix 02 understand images?+

ERNIE 5.1 is not documented with image input; Helix 02 is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, ERNIE 5.1 or Helix 02?+

Neither has a larger sourced maximum output. ERNIE 5.1 is 66K and Helix 02 is —.

Do ERNIE 5.1 and Helix 02 support reasoning and tool use?+

ERNIE 5.1: reasoning and tool calling. Helix 02: image input. Feature support does not establish relative quality.

Which is available from more inference providers, ERNIE 5.1 or Helix 02?+

ERNIE 5.1 has 1 sourced provider route; Helix 02 has 0, so ERNIE 5.1 has broader tracked availability.

Which offers better value, ERNIE 5.1 or Helix 02?+

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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