Phi-4 Multimodal Instruct vs GR00T N1.7 3B
At a Glance
| Compare |  Phi-4 Multimodal InstructMicrosoft |  GR00T N1.7 3BNVIDIA |
|---|---|---|
| Pricing and Limits | ||
| Context windowMaximum documented tokens | 131K | Not reported |
| Model facts checked | Aug 28, 2026View model evidence → | Aug 29, 2026View model evidence → |
Available Benchmarks
Side-by-Side Facts
| Field | Phi-4-multimodal-instruct | GR00T N1.7 3B |
|---|---|---|
| Developer | Microsoft | NVIDIA |
| Family | Phi 4 Multimodal Instruct | Isaac GR00T |
| Model | Phi-4-multimodal-instruct | GR00T N1.7 3B |
| Version | Phi-4-multimodal-instruct | N1.7 |
| Lifecycle | active | active |
| Released | 2025-02-26 | 2026-07-07 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Audio | Text, Image, Robot state |
| Output modalities | Text | Robot action |
| Context window | 131K | Unknown |
| Total parameters | 5.6B | 3B |
| Active parameters | Unknown | Unknown |
| License | mit | apache-2.0 |
| Open weights | Yes | Yes |
| API available | Unknown | No |
| Self-hostable | Yes | Yes |
| Provider access | Unknown | Unknown |
| Capabilities | chat, generation | cross-embodiment, dexterous-manipulation, whole-body-control, fine-tuning |
| Robotics model type | Unknown | Vision-language-action model |
| Action representation | Unknown | Predictive chunks of relative joint motions |
| Control architecture | Unknown | Vision-language backbone with action expert |
| Inference location | Unknown | On device |
| Native control rate (Hz) | Unknown | Unknown |
| Supported embodiments | Unknown | Unitree G1, AgiBot Genie-1, Fourier GR-1, bimanual manipulation platforms |
| Training data | Unknown | Mixture of real teleoperation, synthetic robot data, and internet-scale video described by NVIDIA. |
Phi-4 Multimodal Instruct Capabilities
GR00T N1.7 3B Capabilities
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Primary Evidence
Sources and Freshness
Questions
Phi-4 Multimodal Instruct vs GR00T N1.7 3B FAQs
Is Phi-4 Multimodal Instruct or GR00T N1.7 3B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Phi-4 Multimodal Instruct and GR00T N1.7 3B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Phi-4 Multimodal Instruct or GR00T N1.7 3B?+
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, Phi-4 Multimodal Instruct or GR00T N1.7 3B?+
Neither model has a larger sourced context window in this comparison. Phi-4 Multimodal Instruct is 131K and GR00T N1.7 3B is —.
Which performs better in benchmarks, Phi-4 Multimodal Instruct or GR00T N1.7 3B?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Phi-4 Multimodal Instruct or GR00T N1.7 3B be self-hosted?+
Both models have the same recorded self-hosting status: supported. Phi-4 Multimodal Instruct is open weight; GR00T N1.7 3B is open weight.
Can Phi-4 Multimodal Instruct and GR00T N1.7 3B understand images?+
Phi-4 Multimodal Instruct is documented with image input; GR00T N1.7 3B is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Phi-4 Multimodal Instruct or GR00T N1.7 3B?+
Neither has a larger sourced maximum output. Phi-4 Multimodal Instruct is — and GR00T N1.7 3B is —.
Do Phi-4 Multimodal Instruct and GR00T N1.7 3B support reasoning and tool use?+
Phi-4 Multimodal Instruct: image input. GR00T N1.7 3B: image input. Feature support does not establish relative quality.
Which is available from more inference providers, Phi-4 Multimodal Instruct or GR00T N1.7 3B?+
Phi-4 Multimodal Instruct has 0 sourced provider routes; GR00T N1.7 3B has 0, a tie.
Which offers better value, Phi-4 Multimodal Instruct or GR00T N1.7 3B?+
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.