Helix 02 vs Granite Embedding 30m English
At a Glance
| Compare | Helix 02Figure | |
|---|---|---|
| Pricing and Limits | ||
| Context windowMaximum documented tokens | Not reported | 1K |
| Model facts checked | Aug 29, 2026View model evidence → | Aug 28, 2026View model evidence → |
Available Benchmarks
Side-by-Side Facts
| Field | Helix 02 | granite-embedding-30m-english |
|---|---|---|
| Developer | Figure | IBM |
| Family | Helix | Granite Embedding 30m English |
| Model | Helix 02 | granite-embedding-30m-english |
| Version | 02 | granite-embedding-30m-english |
| Lifecycle | active | active |
| Released | 2026-01-05 | 2025-08-29 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Robot state | Text |
| Output modalities | Robot action | Embedding |
| Context window | Unknown | 1K |
| Total parameters | Unknown | 30.3M |
| Active parameters | Unknown | Unknown |
| License | Unknown | apache-2.0 |
| Open weights | No | Yes |
| API available | No | Yes |
| Self-hostable | No | Yes |
| Provider access | Unknown | Hugging Face (Standard) |
| Capabilities | dexterous-manipulation, long-horizon-control, tactile-control, whole-body-control | embeddings |
| Robotics model type | Vision-language-action model | Unknown |
| Action representation | Full-body joint targets | Unknown |
| Control architecture | Semantic reasoning, visuomotor policy, and kHz whole-body controller | Unknown |
| Inference location | On device | Unknown |
| Native control rate (Hz) | Unknown | Unknown |
| Supported embodiments | Figure 03 | Unknown |
| Training data | Figure 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
Granite Embedding 30m English Capabilities
Primary Evidence
Sources and Freshness
Questions
Helix 02 vs Granite Embedding 30m English FAQs
Is Helix 02 or Granite Embedding 30m English better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Helix 02 and Granite Embedding 30m English, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Helix 02 or Granite Embedding 30m English?+
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 Granite Embedding 30m English?+
Neither model has a larger sourced context window in this comparison. Helix 02 is — and Granite Embedding 30m English is 1K.
Which performs better in benchmarks, Helix 02 or Granite Embedding 30m English?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Helix 02 or Granite Embedding 30m English be self-hosted?+
Granite Embedding 30m English is the only model in this pair currently marked as self-hostable. Helix 02 is not marked open weight; Granite Embedding 30m English is open weight.
Can Helix 02 and Granite Embedding 30m English understand images?+
Helix 02 is documented with image input; Granite Embedding 30m English is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Helix 02 or Granite Embedding 30m English?+
Neither has a larger sourced maximum output. Helix 02 is — and Granite Embedding 30m English is —.
Do Helix 02 and Granite Embedding 30m English support reasoning and tool use?+
Helix 02: image input. Granite Embedding 30m English: none of these features are definitively sourced. Feature support does not establish relative quality.
Which is available from more inference providers, Helix 02 or Granite Embedding 30m English?+
Helix 02 has 0 sourced provider routes; Granite Embedding 30m English has 1, so Granite Embedding 30m English has broader tracked availability.
Which offers better value, Helix 02 or Granite Embedding 30m English?+
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.