Roboflow

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.

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Run the same image across every model that supports a task and compare their outputs side-by-side.

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Open Object Detection in the full playground
GrokGrok 4.7
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MetaMuse Spark 1.3
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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 4.7Muse Spark 1.3

Grok 4.7 vs Muse Spark 1.3 Comparison Table

Evals updated October 13, 2026Pricing updated October 16, 2026

PropertyGrok 4.7Muse Spark 1.3
OrganizationSpaceXAIMeta
Categoryclosedclosed
Modality—multimodal
Release DateSep 2026Sep 2026
Context Window500K1.0M
ParametersUnknownUnknown
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$2.00$1.25
Output $/1M$6.00$4.25
Vision Tasks
CaptioningDemoDemo
Chart Question AnsweringSupportedSupported
ClassificationDemoDemo
Document Question AnsweringSupportedSupported
Image TaggingSupportedSupported
Multi-Label ClassificationSupportedSupported
Object DetectionDemoDemo
OCRDemoDemo
Vision LanguageSupportedSupported
Visual Question AnsweringDemoDemo
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%
73.9%
Avg cost / sample$0.020$0.0084
Avg speed / sample33.31s25.50s
By task
Object Detection (low)
40.4%
±0.6, Mean of 3 runs, range 39.8 to 41.0
$0.021
58.6%
±0.7, Mean of 3 runs, range 58.0 to 59.4
$0.011
Object Detection (high)
41.2%
±1.6, Mean of 3 runs, range 39.6 to 42.8
$0.028
56.6%
±2.4, Mean of 3 runs, range 54.5 to 59.3
$0.017
Counting (low)
61.7%
±1.3, Mean of 3 runs, range 60.8 to 63.5
$0.011
74.3%
±2.0, Mean of 3 runs, range 73.0 to 77.0
$0.0049
Counting (high)
60.8%
±1.3, Mean of 3 runs, range 59.5 to 62.2
$0.017
75.7%
±3.4, Mean of 3 runs, range 73.0 to 79.7
$0.0094
Identification (low)
87.5%
±3.1, Mean of 3 runs, range 84.4 to 90.6
$0.0063
92.7%
±1.6, Mean of 3 runs, range 90.6 to 93.8
$0.0036
Identification (high)
80.2%
±1.6, Mean of 3 runs, range 78.1 to 81.3
$0.0093
86.5%
±1.6, Mean of 3 runs, range 84.4 to 87.5
$0.0063
OCR (low)
63.6%
$0.023
70.4%
$0.0086
by category
Single value
54.4%
Transcription
89.5%
Structured JSON
86.4%
Text localization
14.8%
Single value
62.6%
Transcription
91.8%
Structured JSON
88.0%
Text localization
33.3%
OCR (high)–
69.8%
$0.015
by category
Single value
62.2%
Transcription
86.8%
Structured JSON
87.2%
Text localization
35.2%
Reasoning (low)
64.2%
±2.3, Mean of 3 runs, range 62.3 to 66.9
$0.015
73.3%
±1.0, Mean of 3 runs, range 72.2 to 74.2
$0.0064
Reasoning (high)
66.9%
±1.3, Mean of 3 runs, range 65.6 to 68.2
$0.024
73.1%
±1.0, Mean of 3 runs, range 72.2 to 74.2
$0.012

Grok 4.7 vs Muse Spark 1.3: 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 1.3

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.