Grok 4.7 vs Muse Spark 1.3
Compare Grok 4.7 and Muse Spark 1.3 side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, Classification, OCR, and Object Detection.
Compare Grok 4.7 vs Muse Spark 1.3 live
Run the same image across every model that supports a task and compare their outputs side-by-side.
Detect and compare bounding boxes across models on the same image.
Upload an image
Drag and drop an image here, or click to browse
Models in this comparison
Grok 4.7 vs Muse Spark 1.3 on Vision Evals
Muse Spark 1.3 scores higher on all five Vision Evals tasks.
The widest gap is Object Detection, where Muse Spark 1.3 leads 58.6% to 40.4%.
Overall, Grok 4.7 averages 63.5% (#31 of 63) against 73.9% (#16 of 63) for Muse Spark 1.3.
Muse Spark 1.3 is both cheaper ($0.0084 vs $0.020 per sample) and faster (25.5s vs 33.3s per sample).
Grok 4.7 vs Muse Spark 1.3 Comparison Table
Evals updated October 13, 2026Pricing updated October 16, 2026
| Property | Grok 4.7 | Muse Spark 1.3 |
|---|---|---|
| Organization | SpaceXAI | Meta |
| Category | closed | closed |
| Modality | — | multimodal |
| Release Date | Sep 2026 | Sep 2026 |
| Context Window | 500K | 1.0M |
| Parameters | Unknown | Unknown |
| License | Proprietary | Proprietary |
| Commercial use | commercial use per vendor terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $1.25 |
| Output $/1M | $6.00 | $4.25 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | Supported | Supported |
| Classification | Demo | Demo |
| Document Question Answering | Supported | Supported |
| Image Tagging | Supported | Supported |
| Multi-Label Classification | Supported | Supported |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | Supported | Supported |
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | Not listed | Supported |
| LLMs with Vision Capabilities | Not listed | Supported |
| Multimodal Vision | Not listed | Supported |
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 63.5% | 73.9% |
| Avg cost / sample | $0.020 | $0.0084 |
| Avg speed / sample | 33.31s | 25.50s |
| By task | ||
| Object Detection (low) | 40.4% ±0.6, Mean of 3 runs, range 39.8 to 41.0 | 58.6% ±0.7, Mean of 3 runs, range 58.0 to 59.4 |
| Object Detection (high) | 41.2% ±1.6, Mean of 3 runs, range 39.6 to 42.8 | 56.6% ±2.4, Mean of 3 runs, range 54.5 to 59.3 |
| Counting (low) | 61.7% ±1.3, Mean of 3 runs, range 60.8 to 63.5 | 74.3% ±2.0, Mean of 3 runs, range 73.0 to 77.0 |
| Counting (high) | 60.8% ±1.3, Mean of 3 runs, range 59.5 to 62.2 | 75.7% ±3.4, Mean of 3 runs, range 73.0 to 79.7 |
| Identification (low) | 87.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| Identification (high) | 80.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 | 86.5% ±1.6, Mean of 3 runs, range 84.4 to 87.5 |
| OCR (low) | 63.6% | 70.4% |
| by category |
|
|
| OCR (high) | – | 69.8% |
| by category |
| |
| Reasoning (low) | 64.2% ±2.3, Mean of 3 runs, range 62.3 to 66.9 | 73.3% ±1.0, Mean of 3 runs, range 72.2 to 74.2 |
| Reasoning (high) | 66.9% ±1.3, Mean of 3 runs, range 65.6 to 68.2 | 73.1% ±1.0, Mean of 3 runs, range 72.2 to 74.2 |
Grok 4.7 vs Muse Spark 1.3: 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.
Muse Spark 1.3 is a proprietary multimodal reasoning model from Meta Superintelligence Labs and the fourth Muse Spark release in five months, arriving on September 2, 2026. It takes text, images, video, and document files as input and returns text, and it operates over a context window of 1,048,576 tokens. Meta trains the model for long-horizon agentic work, so it carries accumulated context and prior tool results forward across many turns, reconciles messy or conflicting inputs, and asks for clarification when a task is underspecified. Visual inputs such as screenshots and video clips feed a reasoning loop that runs against a real execution environment rather than a scripted sequence of steps.
The model exposes graded reasoning effort settings. An xhigh configuration is generally available at launch, while a max reasoning configuration aimed at harder reasoning and agentic problems arrives after further safety testing. Artificial Analysis measures Muse Spark 1.3 (max) at 62 on its Intelligence Index and the xhigh configuration at 61, with agentic tool-use evaluations driving most of the gain over Muse Spark 1.2; max reaches 52% on Tau3-Bench Banking by spending more turns and reasoning tokens than xhigh. Prior Muse Spark versions emit bounding box coordinates, transcriptions, and structured field extractions from images on Roboflow Vision Evals.
Frequently Asked Questions
On Roboflow's Vision Evals, Muse Spark 1.3 performed better. It scores higher on all five vision tasks and averages 73.9% (#16 of 63) against 63.5% (#31 of 63) for Grok 4.7. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Object Detection benchmark at low effort, Muse Spark 1.3 leads with 58.6% against 40.4%. This is the widest gap between the two models across the benchmark's tasks.
Muse Spark 1.3 is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0084 per sample against $0.020. Grok 4.7 is priced at $2.00 per 1M input tokens and $6.00 per 1M output; Muse Spark 1.3 is priced at $1.25 per 1M input tokens and $4.25 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
Muse Spark 1.3 is faster. Across Roboflow's Vision Evals it averaged 25.5s per inference against 33.3s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.
Yes. The comparison demo on this page runs both models on the same image side by side for image captioning and open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.