Helix 02 vs Bonsai 27B

At a Glance

Compare
Helix 02Figure
Bonsai 27BPrismML
Pricing and Limits
Context windowMaximum documented tokensNot reported262K
Model facts checkedAug 29, 2026View model evidence →Sep 18, 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 02Bonsai 27B
DeveloperFigurePrismML
FamilyHelixBonsai 27b
ModelHelix 02Bonsai 27B
Version02Bonsai 27B
Lifecycleactiveactive
Released2026-01-052026-07-04
Knowledge cutoffUnknownUnknown
Input modalitiesText, Image, Robot stateText, Image
Output modalitiesRobot actionText
Context windowUnknown262K
Total parametersUnknown27B
Active parametersUnknownUnknown
LicenseUnknownapache-2.0
Open weightsNoYes
API availableNoNo
Self-hostableNoYes
Provider accessUnknownUnknown
Capabilitiesdexterous-manipulation, long-horizon-control, tactile-control, whole-body-controlchat, generation, reasoning, tools, vision
Base modelUnknownQwen3.6 27B
Effective bit widthUnknown1 bit per weight
Language model sizeUnknown3.53 GiB
Weight formatUnknownBinary Q1_0
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

Bonsai 27B Capabilities

chatgenerationreasoningtoolsvision
Model typeUnknown
InferenceUnknown
Action representationUnknown
Supported embodimentsUnknown
Canonical IDprism-ml/Bonsai-27B

Primary Evidence

Sources and Freshness

Questions

Helix 02 vs Bonsai 27B FAQs

Is Helix 02 or Bonsai 27B better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Helix 02 and Bonsai 27B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Helix 02 or Bonsai 27B?+

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 Bonsai 27B?+

Neither model has a larger sourced context window in this comparison. Helix 02 is — and Bonsai 27B is 262K.

Which performs better in benchmarks, Helix 02 or Bonsai 27B?+

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

Can Helix 02 or Bonsai 27B be self-hosted?+

Bonsai 27B is the only model in this pair currently marked as self-hostable. Helix 02 is not marked open weight; Bonsai 27B is open weight.

Can Helix 02 and Bonsai 27B understand images?+

Helix 02 is documented with image input; Bonsai 27B is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Helix 02 or Bonsai 27B?+

Neither has a larger sourced maximum output. Helix 02 is — and Bonsai 27B is —.

Do Helix 02 and Bonsai 27B support reasoning and tool use?+

Helix 02: image input. Bonsai 27B: reasoning, tool calling, and image input. Feature support does not establish relative quality.

Which is available from more inference providers, Helix 02 or Bonsai 27B?+

Helix 02 has 0 sourced provider routes; Bonsai 27B has 0, a tie.

Which offers better value, Helix 02 or Bonsai 27B?+

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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