pi 0.7 vs Qwen3.8 Flash
At a Glance
| Compare |  pi 0.7Physical Intelligence |  Qwen3.8 FlashQwen |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #4 of 44$0.021 per LiveBench case |
| Pricing and Limits | ||
| Context windowMaximum documented tokens | Not reported | 1,000K |
| Model facts checked | Aug 29, 2026View model evidence → | Aug 29, 2026View model evidence → |
Available Benchmarks
Side-by-Side Facts
| Field | pi 0.7 | Qwen3.8-Flash |
|---|---|---|
| Developer | Physical Intelligence | Qwen |
| Family | pi | Qwen3 8 Flash |
| Model | pi 0.7 | Qwen3.8-Flash |
| Version | 0.7 | Qwen3.8-Flash |
| Lifecycle | active | active |
| Released | 2026-04-16 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Robot state | Text, Image, Video |
| Output modalities | Robot action | Text |
| Context window | Unknown | 1,000K |
| Total parameters | Unknown | Unknown |
| Active parameters | Unknown | Unknown |
| License | Unknown | Unknown |
| Open weights | No | No |
| API available | Unknown | Yes |
| Self-hostable | Unknown | No |
| Provider access | Unknown | Alibaba Cloud Model Studio (Standard), Deepinfra (Standard), Openrouter (Standard), Together Ai (Standard) |
| Capabilities | cross-embodiment, dexterous-manipulation, language-steering, visual-subgoals | agents, chat, computer-use, reasoning, structured_outputs, tools, vision |
| 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
Qwen3.8 Flash Capabilities
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|---|---|---|---|
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Primary Evidence
Sources and Freshness
Questions
pi 0.7 vs Qwen3.8 Flash FAQs
Is pi 0.7 or Qwen3.8 Flash better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both pi 0.7 and Qwen3.8 Flash, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, pi 0.7 or Qwen3.8 Flash?+
Only Qwen3.8 Flash has a directly sourced input price: $0.113 per million tokens. Only Qwen3.8 Flash has a directly sourced output price: $0.382 per million tokens.
Which has a larger context window, pi 0.7 or Qwen3.8 Flash?+
Neither model has a larger sourced context window in this comparison. pi 0.7 is — and Qwen3.8 Flash is 1,000K.
Which performs better in benchmarks, pi 0.7 or Qwen3.8 Flash?+
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 Qwen3.8 Flash be self-hosted?+
Neither model is the only model in this pair currently marked as self-hostable. pi 0.7 is not marked open weight; Qwen3.8 Flash is not marked open weight.
Can pi 0.7 and Qwen3.8 Flash understand images?+
pi 0.7 is documented with image input; Qwen3.8 Flash is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, pi 0.7 or Qwen3.8 Flash?+
Neither has a larger sourced maximum output. pi 0.7 is — and Qwen3.8 Flash is 131K.
Do pi 0.7 and Qwen3.8 Flash support reasoning and tool use?+
pi 0.7: image input. Qwen3.8 Flash: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, pi 0.7 or Qwen3.8 Flash?+
pi 0.7 has 0 sourced provider routes; Qwen3.8 Flash has 4, so Qwen3.8 Flash has broader tracked availability.
Which offers better value, pi 0.7 or Qwen3.8 Flash?+
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.