GR00T N1.7 3B vs Jev
At a Glance
| Compare |  GR00T N1.7 3BNVIDIA |  JevTypeSafe |
|---|---|---|
| Pricing and Limits | ||
| Context windowMaximum documented tokens | Not reported | 64K |
| Model facts checked | Aug 29, 2026View model evidence → | Sep 17, 2026View model evidence → |
Available Benchmarks
Side-by-Side Facts
| Field | GR00T N1.7 3B | Jev |
|---|---|---|
| Developer | NVIDIA | TypeSafe |
| Family | Isaac GR00T | Jev |
| Model | GR00T N1.7 3B | Jev |
| Version | N1.7 | Jev |
| Lifecycle | active | active |
| Released | 2026-07-07 | 2026-09-15 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Robot state | Text, Model-specific input |
| Output modalities | Robot action | Model-specific input |
| Context window | Unknown | 64K |
| 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 | TypeSafe (Standard) |
| Capabilities | cross-embodiment, dexterous-manipulation, whole-body-control, fine-tuning | calibrated-confidence, parallel-evaluation, structured_outputs, typed-decisions |
| Maximum Choice cardinality | Unknown | 255 options |
| Default request rate limit | Unknown | 1200 requests per minute |
| State plus longest question limit | Unknown | 32000 tokens |
| Combined state and questions limit | Unknown | 64000 tokens |
| Default token rate limit | Unknown | 250000 tokens per second |
| 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
Jev Capabilities
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Primary Evidence
Sources and Freshness
Questions
GR00T N1.7 3B vs Jev FAQs
Is GR00T N1.7 3B or Jev better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both GR00T N1.7 3B and Jev, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, GR00T N1.7 3B or Jev?+
Only Jev has a directly sourced input price: $0.042 per million tokens. Only Jev has a directly sourced output price: $0.000 per million tokens.
Which has a larger context window, GR00T N1.7 3B or Jev?+
Neither model has a larger sourced context window in this comparison. GR00T N1.7 3B is — and Jev is 64K.
Which performs better in benchmarks, GR00T N1.7 3B or Jev?+
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 Jev 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; Jev is not marked open weight.
Can GR00T N1.7 3B and Jev understand images?+
GR00T N1.7 3B is documented with image input; Jev is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, GR00T N1.7 3B or Jev?+
Neither has a larger sourced maximum output. GR00T N1.7 3B is — and Jev is —.
Do GR00T N1.7 3B and Jev support reasoning and tool use?+
GR00T N1.7 3B: image input. Jev: none of these features are definitively sourced. Feature support does not establish relative quality.
Which is available from more inference providers, GR00T N1.7 3B or Jev?+
GR00T N1.7 3B has 0 sourced provider routes; Jev has 1, so Jev has broader tracked availability.
Which offers better value, GR00T N1.7 3B or Jev?+
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.