Roboflow

Grok 4.7 vs Muse Spark Open

Muse Spark Open and Grok 4.7 score 69.4% vs 63.5% averaged across five Vision Evals tasks (#25 and #33 of 65 models), so Muse Spark Open leads overall.

  • Muse Spark Open wins 3 of the 5 tasks, with its widest lead on object detection (57.8% vs 40.4%).
  • Muse Spark Open is about a third of the cost: $0.0059 vs $0.020 overall average cost per sample.
  • Muse Spark Open is faster: 7.4s vs 33.3s per sample.

Compare Grok 4.7 vs Muse Spark Open 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 Muse Spark Open on Vision Evals

Score by taskGrok 4.7Muse Spark Open
Object Detection
40.4
57.8
Counting
61.7
75.7
Identification
87.5
82.3
OCR
63.6
67.6
Reasoning
64.2
63.8

Roboflow Vision Evals. Full breakdown below.

Grok 4.7 vs Muse Spark Open Comparison Table

Evals updated October 26, 2026Pricing updated October 27, 2026

PropertyGrok 4.7Muse Spark Open
OrganizationSpaceXAIMeta
Categoryclosedopen
Modality—multimodal
Release DateSep 2026Oct 2026
Context Window500K1.0M
ParametersUnknown816B total / 42B active
LicenseProprietaryCustom
Commercial usecommercial use per vendor termsSee license terms
Pricing per 1M tokens
Input $/1M$2.00$1.25
Output $/1M$6.00$4.25
Vision Tasks
CaptioningDemoSupported
ClassificationDemoSupported
Object DetectionDemoSupported
OCRDemoSupported
Visual Question AnsweringDemoSupported
Both support 5 more tasks
Model Features
Foundation VisionNot listedSupported
LLMs with Vision CapabilitiesNot listedSupported
Multimodal VisionNot listedSupported
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
63.5%
69.4%
Avg cost / sample$0.020$0.0059
Avg speed / sample33.31s7.37s
By task
Object Detection (low)
40.4%
±0.6, Mean of 3 runs, range 39.8 to 41.0
$0.021
57.8%
±0.6, Mean of 3 runs, range 57.0 to 58.2
$0.0073
Object Detection (high)
41.2%
±1.6, Mean of 3 runs, range 39.6 to 42.8
$0.028
60.9%
±0.8, Mean of 3 runs, range 60.3 to 61.9
$0.013
Counting (low)
61.7%
±1.3, Mean of 3 runs, range 60.8 to 63.5
$0.011
75.7%
±2.7, Mean of 3 runs, range 73.0 to 78.4
$0.0036
Counting (high)
60.8%
±1.3, Mean of 3 runs, range 59.5 to 62.2
$0.017
75.2%
±4.1, Mean of 3 runs, range 71.6 to 79.7
$0.0073
Identification (low)
87.5%
±3.1, Mean of 3 runs, range 84.4 to 90.6
$0.0063
82.3%
±1.6, Mean of 3 runs, range 81.3 to 84.4
$0.0027
Identification (high)
80.2%
±1.6, Mean of 3 runs, range 78.1 to 81.3
$0.0093
80.2%
±3.1, Mean of 3 runs, range 78.1 to 84.4
$0.0056
OCR (low)
63.6%
$0.023
67.6%
$0.0061
by category
Single value
54.4%
Transcription
89.5%
Structured JSON
86.4%
Text localization
14.8%
Single value
60.0%
Transcription
89.0%
Structured JSON
85.5%
Text localization
29.5%
OCR (high)–
68.6%
$0.014
by category
Single value
58.3%
Transcription
91.4%
Structured JSON
88.2%
Text localization
32.2%
Reasoning (low)
64.2%
±2.3, Mean of 3 runs, range 62.3 to 66.9
$0.015
63.8%
±1.0, Mean of 3 runs, range 62.9 to 64.9
$0.0049
Reasoning (high)
66.9%
±1.3, Mean of 3 runs, range 65.6 to 68.2
$0.024
65.1%
±3.0, Mean of 3 runs, range 62.3 to 68.2
$0.012

Grok 4.7 vs Muse Spark Open: Overview

Grok 4.7

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 Open

Muse Spark Open is an open-weight multimodal model released by Meta on October 15, 2026. Its sparse mixture-of-experts architecture has 816 billion total parameters and activates 42 billion per token. It accepts text, images, audio and video, produces text, and supports a context window of 1,048,576 tokens. Meta provides BF16 and NVFP4 checkpoints for self-hosting, alongside access through hosted inference partners.

The model is designed for coding, tool use and multi-step reasoning over long tasks. Its visual inputs support workflows involving screenshots, documents and other image content. Vision Evals measures its text understanding, counting, identification, reasoning and object detection at Low and High effort. The published weights use the custom Muse Model License, with commercial use subject to its terms and the accompanying usage policy.

Frequently Asked Questions

On Roboflow's Vision Evals, Muse Spark Open performed better. It scores higher on 3 of the five vision tasks and averages 69.4% (#25 of 65) against 63.5% (#33 of 65) 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 Open leads with 57.8% against 40.4%. This is the widest gap between the two models across the benchmark's tasks.

Muse Spark Open is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0059 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 Open 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 Open is faster. Across Roboflow's Vision Evals it averaged 7.4s per inference against 33.3s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.