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GPT-6 Luna vs Muse Spark Open

GPT-6 Luna and Muse Spark Open score 69.7% vs 69.4% averaged across five Vision Evals tasks (#22 and #25 of 65 models), so they are effectively tied.

  • GPT-6 Luna wins 3 of the 5 tasks, but the widest gap is OCR, where Muse Spark Open leads 67.6% vs 59.8%.
  • GPT-6 Luna is under a tenth of the cost: $0.0004 vs $0.0059 overall average cost per sample.
  • Muse Spark Open is faster: 7.4s vs 10.2s per sample.

Compare GPT-6 Luna 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.

GPT-6 Luna vs Muse Spark Open on Vision Evals

Score by taskGPT-6 LunaMuse Spark Open
OCR
59.8
67.6
Object Detection
65.5
57.8
Identification
87.5
82.3
Counting
71.6
75.7
Reasoning
64.2
63.8

Roboflow Vision Evals. Full breakdown below.

GPT-6 Luna vs Muse Spark Open Comparison Table

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

PropertyGPT-6 LunaMuse Spark Open
OrganizationOpenAIMeta
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateSep 2026Oct 2026
Context Window1.1M1.0M
ParametersUnknown816B total / 42B active
LicenseProprietaryCustom
Commercial usecommercial use per vendor termsSee license terms
Pricing per 1M tokens
Input $/1M$0.100$1.25
Output $/1M$0.500$4.25
Vision Tasks
CaptioningDemoSupported
ClassificationDemoSupported
Object DetectionDemoSupported
OCRDemoSupported
Visual Question AnsweringDemoSupported
Promptable Concept SegmentationDemoNot listed
Both support 5 more tasks
Model Features
Both support 3 more features
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
69.7%
69.4%
Avg cost / sample$0.0004$0.0059
Avg speed / sample10.21s7.37s
By task
Object Detection (low)
65.5%
±0.4, Mean of 3 runs, range 65.2 to 66.0
$0.0006
57.8%
±0.6, Mean of 3 runs, range 57.0 to 58.2
$0.0073
Object Detection (high)
68.0%
±0.7, Mean of 3 runs, range 67.3 to 68.6
$0.0014
60.9%
±0.8, Mean of 3 runs, range 60.3 to 61.9
$0.013
Counting (low)
71.6%
±0.0, Mean of 3 runs, range 71.6 to 71.6
$0.0002
75.7%
±2.7, Mean of 3 runs, range 73.0 to 78.4
$0.0036
Counting (high)
72.1%
±0.7, Mean of 3 runs, range 71.6 to 73.0
$0.0004
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
$0.0002
82.3%
±1.6, Mean of 3 runs, range 81.3 to 84.4
$0.0027
Identification (high)
87.5%
±3.1, Mean of 3 runs, range 84.4 to 90.6
$0.0002
80.2%
±3.1, Mean of 3 runs, range 78.1 to 84.4
$0.0056
OCR (low)
59.8%
$0.0004
67.6%
$0.0061
by category
Single value
41.7%
Transcription
80.0%
Structured JSON
78.2%
Text localization
47.4%
Single value
60.0%
Transcription
89.0%
Structured JSON
85.5%
Text localization
29.5%
OCR (high)
62.5%
$0.0013
68.6%
$0.014
by category
Single value
44.4%
Transcription
84.0%
Structured JSON
81.0%
Text localization
50.0%
Single value
58.3%
Transcription
91.4%
Structured JSON
88.2%
Text localization
32.2%
Reasoning (low)
64.2%
±3.0, Mean of 3 runs, range 60.3 to 66.2
$0.0003
63.8%
±1.0, Mean of 3 runs, range 62.9 to 64.9
$0.0049
Reasoning (high)
71.1%
±2.3, Mean of 3 runs, range 68.2 to 72.8
$0.0004
65.1%
±3.0, Mean of 3 runs, range 62.3 to 68.2
$0.012

GPT-6 Luna vs Muse Spark Open: Overview

GPT-6 Luna

GPT-6 Luna is the fast, cost-efficient tier of OpenAI's GPT-6 model family, sitting below GPT-6 Sol and the larger GPT-6 Astra model that opened the generation. It is a proprietary multimodal transformer that accepts text and image input and returns text, and it exposes an adjustable reasoning effort setting so the same model can run in a low-latency mode or spend additional inference compute on harder problems. OpenAI positions it for high-volume and latency-sensitive workloads such as conversational assistants, classification, and lightweight agentic pipelines, while noting that at higher reasoning effort it handles software engineering and computer-use tasks that previously required a Sol-tier model.

The model supports a context window of roughly 1,050,000 input tokens with a maximum output of 128,000 tokens, which allows long documents, extended agent traces, and large code repositories to be processed in a single request. OpenAI describes the GPT-6 generation as improving factual reliability and adopting a more concise communication style relative to the GPT-5.6 series, and attributes the efficiency of the Sol and Luna tiers to gains in caching and inference rather than to reduced capability. Architecture details, parameter counts, and training data are not published.

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, GPT-6 Luna performed better. It scores higher on 3 of the five vision tasks and averages 69.7% (#22 of 65) against 69.4% (#25 of 65) for Muse Spark Open. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.

No. On the Vision Evals OCR benchmark at low effort, Muse Spark Open leads with 67.6% against 59.8%. This is the widest gap between the two models across the benchmark's tasks.

GPT-6 Luna is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0004 per sample against $0.0059. GPT-6 Luna is priced at $0.10 per 1M input tokens and $0.50 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 10.2s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.