Compare Grok 4.7 vs RF-DETR live
Run the same image across every model that supports a task and compare their outputs side-by-side.
These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.
Grok 4.7 vs RF-DETR Comparison Table
Evals updated October 26, 2026Pricing updated November 6, 2026
| Property | Grok 4.7 | RF-DETR |
|---|---|---|
| Organization | SpaceXAI | Roboflow |
| Category | closed | open |
| Modality | — | vision |
| Release Date | Sep 2026 | Mar 2025 |
| Context Window | 500K | — |
| Parameters | Unknown | 30.5M-126.9M |
| License | Proprietary | Apache 2.0 |
| Commercial use | commercial use per vendor terms | free for commercial use (XL and 2XL: PML 1.0, included on Roboflow cloud) |
| COCO Benchmark object detection, mAP 50:95 on COCO val2017 | ||
| Best size | Not benchmarked | 59.92XL · 126.9M params |
| Smallest size | Not benchmarked | 48.4Nano · 30.5M params |
| mAP range | Not benchmarked | 48.4–59.9across 6 sizes |
| Model Sizes COCO mAP 50:95, tested resolution and params per size | ||
| Nano | Not available | 48.4384×384 · 30.5M params |
| Small | Not available | 53.0512×512 · 32.1M params |
| Medium | Not available | 54.8576×576 · 33.7M params |
| Large | Not available | 56.3704×704 · 33.9M params |
| XL | Not available | 58.5700×700 · 126.4M params |
| 2XL | Not available | 59.9880×880 · 126.9M params |
| COCO val2017 mAP 50:95 and parameter counts from leaderboard.roboflow.com. Size names don't line up across model families, so compare sizes with similar parameter counts. | ||
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | No published price |
| Output $/1M | $6.00 | No published price |
| Vision Tasks | ||
| Object Detection | Demo | Demo (COCO) |
| Captioning | Demo | Not listed |
| Chart Question Answering | Supported | Not listed |
| Classification | Demo | Not listed |
| Document Question Answering | Supported | Not listed |
| Image Tagging | Supported | Not listed |
| Multi-Label Classification | Supported | Not listed |
| OCR | Demo | Not listed |
| Vision Language | Supported | Not listed |
| Visual Question Answering | Demo | Not listed |
| Model Features | ||
| Real-Time Vision | Not listed | Supported |
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 63.5% | Not evaluated |
| Avg cost / sample | $0.020 | – |
| Avg speed / sample | 33.31s | – |
| By task | ||
| Object Detection (low) | 40.4% ±0.6, Mean of 3 runs, range 39.8 to 41.0 | – |
| Object Detection (high) | 41.2% ±1.6, Mean of 3 runs, range 39.6 to 42.8 | – |
| Counting (low) | 61.7% ±1.3, Mean of 3 runs, range 60.8 to 63.5 | – |
| Counting (high) | 60.8% ±1.3, Mean of 3 runs, range 59.5 to 62.2 | – |
| Identification (low) | 87.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 | – |
| Identification (high) | 80.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 | – |
| OCR | 63.6% | – |
| by category |
| |
| Reasoning (low) | 64.2% ±2.3, Mean of 3 runs, range 62.3 to 66.9 | – |
| Reasoning (high) | 66.9% ±1.3, Mean of 3 runs, range 65.6 to 68.2 | – |
Grok 4.7 vs RF-DETR: Overview
Grok 4.7 is a proprietary multimodal reasoning model from SpaceXAI that accepts images alongside text and returns text-only output. On visual inputs it supports image captioning, visual question answering, OCR, document and chart question answering, and image classification and tagging, with all results expressed as generated text rather than bounding boxes or masks. Its 500,000 token context window leaves room for several images, long documents, or extended conversations about visual content in a single request.
The model exposes a configurable reasoning effort setting with low, medium, high, and xhigh levels (high by default), letting callers trade latency for the amount of deliberation spent on a prompt, including multi-step questions about an image. Built on a larger base model than Grok 4.6 with extended reinforcement learning on harder tasks, it works longer on difficult problems and checks its own work more carefully at the same serving speed. SpaceXAI's launch materials focus on coding and agentic knowledge work and report no image benchmark results.
RF-DETR is a real-time transformer-based object detection model developed by Roboflow, with code and weights first released in March 2025. Its largest size, 2XL, reaches 59.9 mAP 50:95 on Microsoft COCO val2017, the highest of the 60 detectors on leaderboard.roboflow.com. It is built on a DINOv2 vision transformer backbone, with weight-sharing neural architecture search used to identify accuracy-latency trade-offs. The full family spans six sizes from Nano (30.5M parameters, 384×384 input) to 2XL (126.9M parameters, 880×880 input), with the accompanying research paper accepted to ICLR 2026.
RF-DETR is designed for strong domain adaptability, achieving state-of-the-art performance on RF100-VL, a benchmark measuring generalization to real-world object detection tasks across diverse domains. It is deployable through Roboflow Inference and supports fine-tuning on custom datasets, making it well suited for domain-specific applications with limited training data.
