GR00T N1.7 3B vs Qwen3 Coder Flash
At a Glance
| Compare | GR00T N1.7 3BNVIDIA | |
|---|---|---|
| Pricing and Limits | ||
| Context windowMaximum documented tokens | Not reported | 1,000K |
| Model facts checked | Aug 29, 2026View model evidence → | Sep 3, 2026View model evidence → |
Available Benchmarks
Side-by-Side Facts
| Field | GR00T N1.7 3B | Qwen3 Coder Flash |
|---|---|---|
| Developer | NVIDIA | Qwen |
| Family | Isaac GR00T | Qwen3 Coder |
| Model | GR00T N1.7 3B | Qwen3 Coder Flash |
| Version | N1.7 | Qwen3 Coder Flash |
| Lifecycle | active | active |
| Released | 2026-07-07 | 2025-07-28 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Robot state | Text |
| Output modalities | Robot action | Text |
| Context window | Unknown | 1,000K |
| Total parameters | 3B | Unknown |
| Active parameters | Unknown | Unknown |
| License | apache-2.0 | Unknown |
| Open weights | Yes | No |
| API available | No | Yes |
| Self-hostable | Yes | No |
| Provider access | Unknown | Alibaba Cloud Model Studio (Standard), Openrouter (Standard) |
| Capabilities | cross-embodiment, dexterous-manipulation, whole-body-control, fine-tuning | agents, chat, generation, reasoning, structured_outputs, tools |
| Robotics model type | Vision-language-action model | Unknown |
| Action representation | Predictive chunks of relative joint motions | Unknown |
| Control architecture | Vision-language backbone with action expert | Unknown |
| Inference location | On device | Unknown |
| Native control rate (Hz) | Unknown | Unknown |
| Supported embodiments | Unitree G1, AgiBot Genie-1, Fourier GR-1, bimanual manipulation platforms | Unknown |
| Training data | Mixture of real teleoperation, synthetic robot data, and internet-scale video described by NVIDIA. | Unknown |
GR00T N1.7 3B Capabilities
Qwen3 Coder Flash Capabilities
Primary Evidence
Sources and Freshness
Questions
GR00T N1.7 3B vs Qwen3 Coder Flash FAQs
Is GR00T N1.7 3B or Qwen3 Coder Flash better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both GR00T N1.7 3B and Qwen3 Coder Flash, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, GR00T N1.7 3B or Qwen3 Coder Flash?+
Only Qwen3 Coder Flash has a directly sourced input price: $0.195 per million tokens. Only Qwen3 Coder Flash has a directly sourced output price: $0.975 per million tokens.
Which has a larger context window, GR00T N1.7 3B or Qwen3 Coder Flash?+
Neither model has a larger sourced context window in this comparison. GR00T N1.7 3B is — and Qwen3 Coder Flash is 1,000K.
Which performs better in benchmarks, GR00T N1.7 3B or Qwen3 Coder Flash?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can GR00T N1.7 3B or Qwen3 Coder Flash be self-hosted?+
GR00T N1.7 3B is the only model in this pair currently marked as self-hostable. GR00T N1.7 3B is open weight; Qwen3 Coder Flash is not marked open weight.
Can GR00T N1.7 3B and Qwen3 Coder Flash understand images?+
GR00T N1.7 3B is documented with image input; Qwen3 Coder Flash is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, GR00T N1.7 3B or Qwen3 Coder Flash?+
Neither has a larger sourced maximum output. GR00T N1.7 3B is — and Qwen3 Coder Flash is —.
Do GR00T N1.7 3B and Qwen3 Coder Flash support reasoning and tool use?+
GR00T N1.7 3B: image input. Qwen3 Coder Flash: reasoning and tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, GR00T N1.7 3B or Qwen3 Coder Flash?+
GR00T N1.7 3B has 0 sourced provider routes; Qwen3 Coder Flash has 2, so Qwen3 Coder Flash has broader tracked availability.
Which offers better value, GR00T N1.7 3B or Qwen3 Coder 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.