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Grok 4.5 vs Muse Spark 1.3

Compare Grok 4.5 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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GrokGrok 4.5
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MetaMuse Spark 1.3
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Models in this comparison

Grok 4.5 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 19.0%.

Overall, Grok 4.5 averages 55.3% (#46 of 63) against 73.9% (#16 of 63) for Muse Spark 1.3.

Grok 4.5 is both cheaper ($0.0078 vs $0.0084 per sample) and faster (16.4s vs 25.5s per sample).

Grok 4.5Muse Spark 1.3

Grok 4.5 vs Muse Spark 1.3 Comparison Table

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

PropertyGrok 4.5Muse Spark 1.3
OrganizationSpaceXAIMeta
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateJul 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 VisionSupportedSupported
LLMs with Vision CapabilitiesSupportedSupported
Multimodal VisionSupportedSupported
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
55.3%
73.9%
Avg cost / sample$0.0078$0.0084
Avg speed / sample16.41s25.50s
By task
Object Detection (low)
19.0%
±0.8, Mean of 3 runs, range 18.0 to 19.6
$0.011
58.6%
±0.7, Mean of 3 runs, range 58.0 to 59.4
$0.011
Object Detection (high)
17.8%
±0.3, Mean of 3 runs, range 17.5 to 18.0
$0.020
56.6%
±2.4, Mean of 3 runs, range 54.5 to 59.3
$0.017
Counting (low)
59.5%
±3.4, Mean of 3 runs, range 55.4 to 62.2
$0.0065
74.3%
±2.0, Mean of 3 runs, range 73.0 to 77.0
$0.0049
Counting (high)
57.7%
±3.4, Mean of 3 runs, range 54.0 to 60.8
$0.012
75.7%
±3.4, Mean of 3 runs, range 73.0 to 79.7
$0.0094
Identification (low)
83.3%
±1.6, Mean of 3 runs, range 81.3 to 84.4
$0.0046
92.7%
±1.6, Mean of 3 runs, range 90.6 to 93.8
$0.0036
Identification (high)
85.4%
±3.1, Mean of 3 runs, range 81.3 to 87.5
$0.0067
86.5%
±1.6, Mean of 3 runs, range 84.4 to 87.5
$0.0063
OCR (low)
57.1%
$0.0065
70.4%
$0.0086
by category
Single value
47.8%
Transcription
85.4%
Structured JSON
80.3%
Text localization
6.1%
Single value
62.6%
Transcription
91.8%
Structured JSON
88.0%
Text localization
33.3%
OCR (high)
57.5%
$0.013
69.8%
$0.015
by category
Single value
47.8%
Transcription
86.1%
Structured JSON
80.9%
Text localization
7.0%
Single value
62.2%
Transcription
86.8%
Structured JSON
87.2%
Text localization
35.2%
Reasoning (low)
57.6%
±1.7, Mean of 3 runs, range 55.6 to 58.9
$0.0082
73.3%
±1.0, Mean of 3 runs, range 72.2 to 74.2
$0.0064
Reasoning (high)
59.8%
±2.6, Mean of 3 runs, range 57.0 to 62.3
$0.019
73.1%
±1.0, Mean of 3 runs, range 72.2 to 74.2
$0.012

Grok 4.5 vs Muse Spark 1.3: Overview

Grok 4.5

Grok 4.5 is a proprietary reasoning model from SpaceXAI (xAI) that accepts interleaved text and image input and returns text, with a 500,000 token context window. xAI positions it as a model for coding, agentic software work, and knowledge tasks, and states it was trained in the company's Memphis data centers on datasets spanning science, engineering, and mathematics. Its reinforcement learning stage covers hundreds of thousands of multi step software engineering tasks scored by automated checks and model based grading, and training is reported to have run on tens of thousands of NVIDIA GB300 GPUs using an asynchronous scheme in which multi hour agentic rollouts continue while learning proceeds in parallel, targeting long horizon autonomous operation rather than single turn inference.

For vision, the model consumes JPEG and PNG images in any order relative to text prompts, covering visual question answering, description of chart and document imagery, and reading text rendered inside a scene. Reasoning effort is configurable, and the model supports function calling and structured outputs, so image inputs can be interleaved with tool calls inside agent loops. xAI has not published a technical report, architecture details, or parameter count, and reported mixture of experts sizing figures come from secondary coverage rather than official documentation.

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.