Helix 02 vs Gemini 2.5 Flash Lite
At a Glance
| Compare | Helix 02Figure | Gemini 2.5 Flash LiteGoogle DeepMind |
|---|---|---|
| Pricing and Limits | ||
| Context windowMaximum documented tokens | Not reported | 1,049K |
| Model facts checked | Aug 29, 2026View model evidence → | Aug 29, 2026View model evidence → |
Available Benchmarks
Side-by-Side Facts
| Field | Helix 02 | Gemini 2.5 Flash-Lite |
|---|---|---|
| Developer | Figure | Google DeepMind |
| Family | Helix | Gemini 2 5 |
| Model | Helix 02 | Gemini 2.5 Flash-Lite |
| Version | 02 | Gemini 2.5 Flash-Lite |
| Lifecycle | active | active |
| Released | 2026-01-05 | Unknown |
| Knowledge cutoff | Unknown | 2025-01-01 |
| Input modalities | Text, Image, Robot state | Text, Image, Video, Audio, Document |
| Output modalities | Robot action | Text |
| Context window | Unknown | 1,049K |
| Total parameters | Unknown | Unknown |
| Active parameters | Unknown | Unknown |
| License | Unknown | Unknown |
| Open weights | No | No |
| API available | No | Yes |
| Self-hostable | No | No |
| Provider access | Unknown | Google AI (Standard), Google Gemini (Standard) |
| Capabilities | dexterous-manipulation, long-horizon-control, tactile-control, whole-body-control | chat, generation, reasoning, tools |
| 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
Gemini 2.5 Flash Lite Capabilities
Primary Evidence
Sources and Freshness
Questions
Helix 02 vs Gemini 2.5 Flash Lite FAQs
Is Helix 02 or Gemini 2.5 Flash Lite better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Helix 02 and Gemini 2.5 Flash Lite, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Helix 02 or Gemini 2.5 Flash Lite?+
Only Gemini 2.5 Flash Lite has a directly sourced input price: $0.10 per million tokens. Only Gemini 2.5 Flash Lite has a directly sourced output price: $0.40 per million tokens.
Which has a larger context window, Helix 02 or Gemini 2.5 Flash Lite?+
Neither model has a larger sourced context window in this comparison. Helix 02 is — and Gemini 2.5 Flash Lite is 1,049K.
Which performs better in benchmarks, Helix 02 or Gemini 2.5 Flash Lite?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Helix 02 or Gemini 2.5 Flash Lite be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. Helix 02 is not marked open weight; Gemini 2.5 Flash Lite is not marked open weight.
Can Helix 02 and Gemini 2.5 Flash Lite understand images?+
Helix 02 is documented with image input; Gemini 2.5 Flash Lite is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Helix 02 or Gemini 2.5 Flash Lite?+
Neither has a larger sourced maximum output. Helix 02 is — and Gemini 2.5 Flash Lite is 66K.
Do Helix 02 and Gemini 2.5 Flash Lite support reasoning and tool use?+
Helix 02: image input. Gemini 2.5 Flash Lite: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Helix 02 or Gemini 2.5 Flash Lite?+
Helix 02 has 0 sourced provider routes; Gemini 2.5 Flash Lite has 2, so Gemini 2.5 Flash Lite has broader tracked availability.
Which offers better value, Helix 02 or Gemini 2.5 Flash Lite?+
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.