Helix 02 vs Stable Diffusion 3.5 Large
At a Glance
| Compare | Helix 02Figure | Stable Diffusion 3.5 LargeStability AI |
|---|---|---|
| Pricing and Limits | ||
| Context windowMaximum documented tokens | Not reported | 0K |
| Model facts checked | Aug 29, 2026View model evidence → | Aug 28, 2026View model evidence → |
Available Benchmarks
Side-by-Side Facts
| Field | Helix 02 | stable-diffusion-3.5-large |
|---|---|---|
| Developer | Figure | Stability AI |
| Family | Helix | Stable Diffusion 3 5 Large |
| Model | Helix 02 | stable-diffusion-3.5-large |
| Version | 02 | stable-diffusion-3.5-large |
| Lifecycle | active | active |
| Released | 2026-01-05 | 2024-10-22 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Robot state | Text |
| Output modalities | Robot action | Image |
| Context window | Unknown | 0K |
| Total parameters | Unknown | 8.1B |
| Active parameters | Unknown | Unknown |
| License | Unknown | other |
| Open weights | No | Yes |
| API available | No | Yes |
| Self-hostable | No | Yes |
| Provider access | Unknown | Hugging Face (Standard), Stability AI (Pay as you go) |
| Capabilities | dexterous-manipulation, long-horizon-control, tactile-control, whole-body-control | generation |
| 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
Stable Diffusion 3.5 Large Capabilities
Primary Evidence
Sources and Freshness
Questions
Helix 02 vs Stable Diffusion 3.5 Large FAQs
Is Helix 02 or Stable Diffusion 3.5 Large better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Helix 02 and Stable Diffusion 3.5 Large, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Helix 02 or Stable Diffusion 3.5 Large?+
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 Stable Diffusion 3.5 Large?+
Neither model has a larger sourced context window in this comparison. Helix 02 is — and Stable Diffusion 3.5 Large is 0K.
Which performs better in benchmarks, Helix 02 or Stable Diffusion 3.5 Large?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Helix 02 or Stable Diffusion 3.5 Large be self-hosted?+
Stable Diffusion 3.5 Large is the only model in this pair currently marked as self-hostable. Helix 02 is not marked open weight; Stable Diffusion 3.5 Large is open weight.
Can Helix 02 and Stable Diffusion 3.5 Large understand images?+
Helix 02 is documented with image input; Stable Diffusion 3.5 Large is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Helix 02 or Stable Diffusion 3.5 Large?+
Neither has a larger sourced maximum output. Helix 02 is — and Stable Diffusion 3.5 Large is —.
Do Helix 02 and Stable Diffusion 3.5 Large support reasoning and tool use?+
Helix 02: image input. Stable Diffusion 3.5 Large: none of these features are definitively sourced. Feature support does not establish relative quality.
Which is available from more inference providers, Helix 02 or Stable Diffusion 3.5 Large?+
Helix 02 has 0 sourced provider routes; Stable Diffusion 3.5 Large has 2, so Stable Diffusion 3.5 Large has broader tracked availability.
Which offers better value, Helix 02 or Stable Diffusion 3.5 Large?+
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.