Granite Embedding 30m English vs pi 0.7
At a Glance
| Compare | pi 0.7Physical Intelligence | |
|---|---|---|
| Pricing and Limits | ||
| Context windowMaximum documented tokens | 1K | Not reported |
| Model facts checked | Aug 28, 2026View model evidence → | Aug 29, 2026View model evidence → |
Available Benchmarks
Side-by-Side Facts
| Field | granite-embedding-30m-english | pi 0.7 |
|---|---|---|
| Developer | IBM | Physical Intelligence |
| Family | Granite Embedding 30m English | pi |
| Model | granite-embedding-30m-english | pi 0.7 |
| Version | granite-embedding-30m-english | 0.7 |
| Lifecycle | active | active |
| Released | 2025-08-29 | 2026-04-16 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text, Image, Robot state |
| Output modalities | Embedding | Robot action |
| Context window | 1K | Unknown |
| Total parameters | 30.3M | Unknown |
| Active parameters | Unknown | Unknown |
| License | apache-2.0 | Unknown |
| Open weights | Yes | No |
| API available | Yes | Unknown |
| Self-hostable | Yes | Unknown |
| Provider access | Hugging Face (Standard) | Unknown |
| Capabilities | embeddings | 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. |
Granite Embedding 30m English Capabilities
pi 0.7 Capabilities
Primary Evidence
Sources and Freshness
Questions
Granite Embedding 30m English vs pi 0.7 FAQs
Is Granite Embedding 30m English or pi 0.7 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Granite Embedding 30m English and pi 0.7, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Granite Embedding 30m English or pi 0.7?+
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, Granite Embedding 30m English or pi 0.7?+
Neither model has a larger sourced context window in this comparison. Granite Embedding 30m English is 1K and pi 0.7 is —.
Which performs better in benchmarks, Granite Embedding 30m English 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 Granite Embedding 30m English or pi 0.7 be self-hosted?+
Granite Embedding 30m English is the only model in this pair currently marked as self-hostable. Granite Embedding 30m English is open weight; pi 0.7 is not marked open weight.
Can Granite Embedding 30m English and pi 0.7 understand images?+
Granite Embedding 30m English is not 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, Granite Embedding 30m English or pi 0.7?+
Neither has a larger sourced maximum output. Granite Embedding 30m English is — and pi 0.7 is —.
Do Granite Embedding 30m English and pi 0.7 support reasoning and tool use?+
Granite Embedding 30m English: none of these features are definitively sourced. pi 0.7: image input. Feature support does not establish relative quality.
Which is available from more inference providers, Granite Embedding 30m English or pi 0.7?+
Granite Embedding 30m English has 1 sourced provider route; pi 0.7 has 0, so Granite Embedding 30m English has broader tracked availability.
Which offers better value, Granite Embedding 30m English 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.