OCR 4.1 vs pi 0.7
At a Glance
| Compare |  OCR 4.1Mistral AI |  pi 0.7Physical Intelligence |
|---|---|---|
| Pricing and Limits | ||
| Context windowMaximum documented tokens | Not reported | Not reported |
| Model facts checked | Aug 29, 2026View model evidence → | Aug 29, 2026View model evidence → |
Available Benchmarks
Side-by-Side Facts
| Field | OCR 4.1 | pi 0.7 |
|---|---|---|
| Developer | Mistral AI | Physical Intelligence |
| Family | Mistral OCR | pi |
| Model | OCR 4.1 | pi 0.7 |
| Version | OCR 4.1 | 0.7 |
| Lifecycle | active | active |
| Released | 2026-07-16 | 2026-04-16 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Image, Document | Text, Image, Robot state |
| Output modalities | Text | Robot action |
| Context window | Unknown | 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 | Mistral AI (Standard) | Unknown |
| Capabilities | bounding-box-extraction, document-ai, ocr, structured-annotations | 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. |
OCR 4.1 Capabilities
pi 0.7 Capabilities
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Primary Evidence
Sources and Freshness
Questions
OCR 4.1 vs pi 0.7 FAQs
Is OCR 4.1 or pi 0.7 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both OCR 4.1 and pi 0.7, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, OCR 4.1 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, OCR 4.1 or pi 0.7?+
Neither model has a larger sourced context window in this comparison. OCR 4.1 is — and pi 0.7 is —.
Which performs better in benchmarks, OCR 4.1 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 OCR 4.1 or pi 0.7 be self-hosted?+
Neither model is the only model in this pair currently marked as self-hostable. OCR 4.1 is not marked open weight; pi 0.7 is not marked open weight.
Can OCR 4.1 and pi 0.7 understand images?+
OCR 4.1 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, OCR 4.1 or pi 0.7?+
Neither has a larger sourced maximum output. OCR 4.1 is — and pi 0.7 is —.
Do OCR 4.1 and pi 0.7 support reasoning and tool use?+
OCR 4.1: image input. pi 0.7: image input. Feature support does not establish relative quality.
Which is available from more inference providers, OCR 4.1 or pi 0.7?+
OCR 4.1 has 1 sourced provider route; pi 0.7 has 0, so OCR 4.1 has broader tracked availability.
Which offers better value, OCR 4.1 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.