Gemini 3.1 Flash Lite vs pi 0.7
At a Glance
| Compare | Gemini 3.1 Flash LiteGoogle DeepMind | pi 0.7Physical Intelligence |
|---|---|---|
| Pricing and Limits | ||
| Context windowMaximum documented tokens | 1,049K | Not reported |
| Model facts checked | Aug 29, 2026View model evidence → | Aug 29, 2026View model evidence → |
Available Benchmarks
Side-by-Side Facts
| Field | Gemini 3.1 Flash-Lite | pi 0.7 |
|---|---|---|
| Developer | Google DeepMind | Physical Intelligence |
| Family | Gemini 3 | pi |
| Model | Gemini 3.1 Flash-Lite | pi 0.7 |
| Version | Gemini 3.1 Flash-Lite | 0.7 |
| Lifecycle | active | active |
| Released | Unknown | 2026-04-16 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Video, Audio, Document | Text, Image, Robot state |
| Output modalities | Text | Robot action |
| Context window | 1,049K | Unknown |
| Total parameters | Unknown | Unknown |
| Active parameters | Unknown | Unknown |
| License | Unknown | Unknown |
| Open weights | No | No |
| API available | Yes | Unknown |
| Self-hostable | No | Unknown |
| Provider access | Google AI (Standard), Google Gemini (Standard) | Unknown |
| Capabilities | chat, generation, reasoning, tools | cross-embodiment, dexterous-manipulation, language-steering, visual-subgoals |
| Robotics model type | Unknown | Vision-language-action model |
| Action representation | Unknown | Continuous robot actions conditioned by multimodal prompts |
| Control architecture | Unknown | High-level policy, world model, and action expert |
| Inference location | Unknown | Unknown |
| Native control rate (Hz) | Unknown | Unknown |
| Supported embodiments | Unknown | mobile manipulators, bimanual UR5e, multiple fixed manipulators |
| Training data | Unknown | Robot demonstrations, autonomous data, egocentric human data, and multimodal web data described by the publisher. |
Gemini 3.1 Flash Lite Capabilities
pi 0.7 Capabilities
Primary Evidence
Sources and Freshness
Questions
Gemini 3.1 Flash Lite vs pi 0.7 FAQs
Is Gemini 3.1 Flash Lite or pi 0.7 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 3.1 Flash Lite and pi 0.7, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Gemini 3.1 Flash Lite or pi 0.7?+
Only Gemini 3.1 Flash Lite has a directly sourced input price: $0.25 per million tokens. Only Gemini 3.1 Flash Lite has a directly sourced output price: $1.50 per million tokens.
Which has a larger context window, Gemini 3.1 Flash Lite or pi 0.7?+
Neither model has a larger sourced context window in this comparison. Gemini 3.1 Flash Lite is 1,049K and pi 0.7 is —.
Which performs better in benchmarks, Gemini 3.1 Flash Lite or pi 0.7?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Gemini 3.1 Flash Lite or pi 0.7 be self-hosted?+
Neither model is the only model in this pair currently marked as self-hostable. Gemini 3.1 Flash Lite is not marked open weight; pi 0.7 is not marked open weight.
Can Gemini 3.1 Flash Lite and pi 0.7 understand images?+
Gemini 3.1 Flash Lite is documented with image input; pi 0.7 is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Gemini 3.1 Flash Lite or pi 0.7?+
Neither has a larger sourced maximum output. Gemini 3.1 Flash Lite is 66K and pi 0.7 is —.
Do Gemini 3.1 Flash Lite and pi 0.7 support reasoning and tool use?+
Gemini 3.1 Flash Lite: reasoning, tool calling, and image input. pi 0.7: image input. Feature support does not establish relative quality.
Which is available from more inference providers, Gemini 3.1 Flash Lite or pi 0.7?+
Gemini 3.1 Flash Lite has 2 sourced provider routes; pi 0.7 has 0, so Gemini 3.1 Flash Lite has broader tracked availability.
Which offers better value, Gemini 3.1 Flash Lite or pi 0.7?+
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.