Roboflow

GPT-5.4 Mini vs Muse Spark 1.3

Compare GPT-5.4 Mini and Muse Spark 1.3 side-by-side. See how these vision models stack up in Open Prompt, Object Detection, Classification, Image Captioning, and OCR.

Compare GPT-5.4 Mini vs Muse Spark 1.3 live

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

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OpenAIGPT-5.4 Mini
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MetaMuse Spark 1.3
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Models in this comparison

GPT-5.4 Mini vs Muse Spark 1.3 on Vision Evals

Muse Spark 1.3 scores higher on all six Vision Evals tasks.

The widest gap is Object Detection, where Muse Spark 1.3 leads 58.6% to 16.1%.

Overall, GPT-5.4 Mini averages 64.7% (#38 of 52) against 79.8% (#10 of 52) for Muse Spark 1.3.

GPT-5.4 Mini is both cheaper ($0.0030 vs $0.0075 per sample) and faster (5.3s vs 23.1s per sample).

GPT-5.4 MiniMuse Spark 1.3

GPT-5.4 Mini vs Muse Spark 1.3 Comparison Table

Evals updated September 3, 2026Pricing updated September 3, 2026

PropertyGPT-5.4 MiniMuse Spark 1.3
OrganizationOpenAIMeta
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateMar 2026Sep 2026
Context Window400K1.0M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.750$1.25
Output $/1M$4.50$4.25
Vision Tasks
CaptioningDemoDemo
Chart Question Answering
ClassificationDemoDemo
Document Question Answering
Image Tagging
Multi-Label Classification
Object DetectionDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort
Overall
64.7%
79.8%
Avg cost / sample$0.0030$0.0075
Avg speed / sample5.35s23.14s
By task
Object Detection (low)
16.1%
$0.0044
58.6%
±0.7, Mean of 3 runs, range 58.0 to 59.4
$0.011
Object Detection (high)
56.6%
±2.4, Mean of 3 runs, range 54.5 to 59.3
$0.017
Counting (low)
60.8%
$0.0019
74.3%
±2.0, Mean of 3 runs, range 73.0 to 77.0
$0.0049
Counting (high)
75.7%
±3.4, Mean of 3 runs, range 73.0 to 79.7
$0.0094
Identification (low)
84.4%
$0.0013
92.7%
±1.6, Mean of 3 runs, range 90.6 to 93.8
$0.0036
Identification (high)
86.5%
±1.6, Mean of 3 runs, range 84.4 to 87.5
$0.0063
OCR (low)
88.1%
$0.0042
91.3%
±0.5, Mean of 3 runs, range 90.7 to 91.6
$0.0083
OCR (high)
86.9%
±4.1, Mean of 3 runs, range 82.2 to 90.4
$0.015
Data Extraction (low)
83.5%
$0.0014
88.7%
±1.5, Mean of 3 runs, range 86.6 to 89.7
$0.0031
Data Extraction (high)
87.6%
±0.0, Mean of 3 runs, range 87.6 to 87.6
$0.0044
Reasoning (low)
55.6%
$0.0023
73.3%
±1.0, Mean of 3 runs, range 72.2 to 74.2
$0.0064
Reasoning (high)
62.9%
$0.0081
73.1%
±1.0, Mean of 3 runs, range 72.2 to 74.2
$0.012

GPT-5.4 Mini vs Muse Spark 1.3: Overview

GPT-5.4 Mini

GPT-5.4 mini is a fast, cost-efficient model developed by OpenAI and released on March 17, 2026, optimized for high-throughput workloads and subagent orchestration. It supports text and image inputs within a 400,000-token context window, making it ideal for processing extensive visual datasets and large codebases in a single request. Designed for low-latency production environments, the model integrates with key API features including function calling, web search, and tool-based computer use, allowing it to assist in automated workflows that require navigating digital interfaces.

Compared to the previous GPT-5 mini, this version runs more than twice as fast while approaching the performance levels of the flagship GPT-5.4 on reasoning and coding benchmarks. While the larger GPT-5.4 introduces native, state-of-the-art computer-use capabilities, GPT-5.4 mini provides a scalable alternative for interpreting screenshots and reasoning over dense UI layouts. For vision tasks on Playground, it excels at extracting structured information from visual documents and assisting in agentic tasks that involve real-time interpretation of software interfaces alongside text.

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 six vision tasks and averages 79.8% (#10 of 52) against 64.7% (#38 of 52) for GPT-5.4 Mini. 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 16.1%. This is the widest gap between the two models across the benchmark's tasks.

GPT-5.4 Mini is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0030 per sample against $0.0075. GPT-5.4 Mini is priced at $0.75 per 1M input tokens and $4.50 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.

GPT-5.4 Mini is faster. Across Roboflow's Vision Evals it averaged 5.3s per inference against 23.1s. 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 open prompts and object detection in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.