pi 0.7 vs GLM OCR
At a Glance
| Compare |  pi 0.7Physical Intelligence |  GLM OCRZ.ai |
|---|---|---|
| Pricing and Limits | ||
| Context windowMaximum documented tokens | Not reported | 131K |
| Model facts checked | Aug 29, 2026View model evidence → | Aug 28, 2026View model evidence → |
Available Benchmarks
Side-by-Side Facts
| Field | pi 0.7 | GLM-OCR |
|---|---|---|
| Developer | Physical Intelligence | Z.ai |
| Family | pi | Glm OCR |
| Model | pi 0.7 | GLM-OCR |
| Version | 0.7 | GLM-OCR |
| Lifecycle | active | active |
| Released | 2026-04-16 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Robot state | Text, Image |
| Output modalities | Robot action | Text |
| Context window | Unknown | 131K |
| Total parameters | Unknown | 1.3B |
| Active parameters | Unknown | Unknown |
| License | Unknown | mit |
| Open weights | No | Yes |
| API available | Unknown | Yes |
| Self-hostable | Unknown | Yes |
| Provider access | Unknown | Together Ai (Standard) |
| Capabilities | cross-embodiment, dexterous-manipulation, language-steering, visual-subgoals | chat, generation, tools |
| Robotics model type | Vision-language-action model | Unknown |
| Action representation | Continuous robot actions conditioned by multimodal prompts | Unknown |
| Control architecture | High-level policy, world model, and action expert | Unknown |
| Inference location | Unknown | Unknown |
| Native control rate (Hz) | Unknown | Unknown |
| Supported embodiments | mobile manipulators, bimanual UR5e, multiple fixed manipulators | Unknown |
| Training data | Robot demonstrations, autonomous data, egocentric human data, and multimodal web data described by the publisher. | Unknown |
pi 0.7 Capabilities
GLM OCR Capabilities
Related Comparisons
| A | Pair | B | Context |
|---|---|---|---|
 GPT-6 AstraOpenAI | vs | pi 0.7Physical Intelligence | cross-developer peers |
Claude Fable 5.1Anthropic | vs | pi 0.7Physical Intelligence | cross-developer peers |
 Gemini 3.1 ProGoogle DeepMind | vs | pi 0.7Physical Intelligence | cross-developer peers |
 DeepSeek-V4.1-FlashDeepSeek | vs | pi 0.7Physical Intelligence | cross-developer peers |
pi 0.7Physical Intelligence | vs | Grok 4.6xAI | cross-developer peers |
pi 0.7Physical Intelligence | vs |  Qwen3.8-MaxQwen | cross-developer peers |
 Kimi-K3Moonshot AI | vs | pi 0.7Physical Intelligence | cross-developer peers |
 MiniMax-M3MiniMax | vs | pi 0.7Physical Intelligence | cross-developer peers |
pi 0.7Physical Intelligence | vs | GLM-5.3Z.ai | cross-developer peers |
pi 0.7Physical Intelligence | vs |  Hy4 previewTencent | cross-developer peers |
Seed 2.1 ProByteDance Seed | vs | pi 0.7Physical Intelligence | cross-developer peers |
 Mistral Large 3Mistral AI | vs | pi 0.7Physical Intelligence | cross-developer peers |
Primary Evidence
Sources and Freshness
Questions
pi 0.7 vs GLM OCR FAQs
Is pi 0.7 or GLM OCR better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both pi 0.7 and GLM OCR, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, pi 0.7 or GLM OCR?+
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, pi 0.7 or GLM OCR?+
Neither model has a larger sourced context window in this comparison. pi 0.7 is — and GLM OCR is 131K.
Which performs better in benchmarks, pi 0.7 or GLM OCR?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can pi 0.7 or GLM OCR be self-hosted?+
GLM OCR is the only model in this pair currently marked as self-hostable. pi 0.7 is not marked open weight; GLM OCR is open weight.
Can pi 0.7 and GLM OCR understand images?+
pi 0.7 is documented with image input; GLM OCR is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, pi 0.7 or GLM OCR?+
Neither has a larger sourced maximum output. pi 0.7 is — and GLM OCR is —.
Do pi 0.7 and GLM OCR support reasoning and tool use?+
pi 0.7: image input. GLM OCR: tool calling and image input. Feature support does not establish relative quality.
Which is available from more inference providers, pi 0.7 or GLM OCR?+
pi 0.7 has 0 sourced provider routes; GLM OCR has 1, so GLM OCR has broader tracked availability.
Which offers better value, pi 0.7 or GLM OCR?+
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.