Kimi K2.7 Code vs GR00T N1.7 3B
At a Glance
| Compare | Kimi K2.7 CodeMoonshot AI | GR00T N1.7 3BNVIDIA |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| CostLower is better · Published-token output estimate | #9 of 44$0.042 per LiveBench case | UnrankedNot in the 44-model eligible cohort |
| Pricing and Limits | ||
| Context windowMaximum documented tokens | 262K | Not reported |
| Model facts checked | Sep 3, 2026View model evidence → | Aug 29, 2026View model evidence → |
Available Benchmarks
Side-by-Side Facts
| Field | Kimi K2.7 Code | GR00T N1.7 3B |
|---|---|---|
| Developer | Moonshot AI | NVIDIA |
| Family | Kimi K2 7 | Isaac GR00T |
| Model | Kimi K2.7 Code | GR00T N1.7 3B |
| Version | Kimi K2.7 Code | N1.7 |
| Lifecycle | active | active |
| Released | 2026-06-11 | 2026-07-07 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Video | Text, Image, Robot state |
| Output modalities | Text | Robot action |
| Context window | 262K | Unknown |
| Total parameters | 1T | 3B |
| Active parameters | 32B | Unknown |
| License | modified-mit | apache-2.0 |
| Open weights | Yes | Yes |
| API available | Yes | No |
| Self-hostable | Yes | Yes |
| Provider access | Deepinfra (Standard), Fireworks Ai (Standard), Openrouter (Standard), Together Ai (Standard) | Unknown |
| Capabilities | agents, chat, coding, reasoning, tools, vision | 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. |
Kimi K2.7 Code Capabilities
GR00T N1.7 3B Capabilities
Primary Evidence
Sources and Freshness
Questions
Kimi K2.7 Code vs GR00T N1.7 3B FAQs
Is Kimi K2.7 Code or GR00T N1.7 3B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Kimi K2.7 Code and GR00T N1.7 3B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Kimi K2.7 Code or GR00T N1.7 3B?+
Only Kimi K2.7 Code has a directly sourced input price: $0.68 per million tokens. Only Kimi K2.7 Code has a directly sourced output price: $3.21 per million tokens.
Which has a larger context window, Kimi K2.7 Code or GR00T N1.7 3B?+
Neither model has a larger sourced context window in this comparison. Kimi K2.7 Code is 262K and GR00T N1.7 3B is —.
Which performs better in benchmarks, Kimi K2.7 Code 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 Kimi K2.7 Code or GR00T N1.7 3B be self-hosted?+
Both models have the same recorded self-hosting status: supported. Kimi K2.7 Code is open weight; GR00T N1.7 3B is open weight.
Can Kimi K2.7 Code and GR00T N1.7 3B understand images?+
Kimi K2.7 Code 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, Kimi K2.7 Code or GR00T N1.7 3B?+
Neither has a larger sourced maximum output. Kimi K2.7 Code is — and GR00T N1.7 3B is —.
Do Kimi K2.7 Code and GR00T N1.7 3B support reasoning and tool use?+
Kimi K2.7 Code: reasoning, tool calling, and image input. GR00T N1.7 3B: image input. Feature support does not establish relative quality.
Which is available from more inference providers, Kimi K2.7 Code or GR00T N1.7 3B?+
Kimi K2.7 Code has 4 sourced provider routes; GR00T N1.7 3B has 0, so Kimi K2.7 Code has broader tracked availability.
Which offers better value, Kimi K2.7 Code 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.