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.
Roboflow Vision Evals. Full breakdown below.
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
Roboflow Vision Evals. Full breakdown below.
GPT-6 Luna vs Muse Spark Open Comparison Table
Evals updated October 26, 2026Pricing updated October 27, 2026
| Property | GPT-6 Luna | Muse Spark Open |
|---|---|---|
| Organization | OpenAI | Meta |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Oct 2026 |
| Context Window | 1.1M | 1.0M |
| Parameters | Unknown | 816B total / 42B active |
| License | Proprietary | Custom |
| Commercial use | commercial use per vendor terms | See license terms |
| Pricing per 1M tokens | ||
| Input $/1M | $0.100 | $1.25 |
| Output $/1M | $0.500 | $4.25 |
| Vision Tasks | ||
| Captioning | Demo | Supported |
| Classification | Demo | Supported |
| Object Detection | Demo | Supported |
| OCR | Demo | Supported |
| Visual Question Answering | Demo | Supported |
| Promptable Concept Segmentation | Demo | Not listed |
| 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 / sample | 10.21s | 7.37s |
| By task | ||
| Object Detection (low) | 65.5% ±0.4, Mean of 3 runs, range 65.2 to 66.0 | 57.8% ±0.6, Mean of 3 runs, range 57.0 to 58.2 |
| Object Detection (high) | 68.0% ±0.7, Mean of 3 runs, range 67.3 to 68.6 | 60.9% ±0.8, Mean of 3 runs, range 60.3 to 61.9 |
| Counting (low) | 71.6% ±0.0, Mean of 3 runs, range 71.6 to 71.6 | 75.7% ±2.7, Mean of 3 runs, range 73.0 to 78.4 |
| Counting (high) | 72.1% ±0.7, Mean of 3 runs, range 71.6 to 73.0 | 75.2% ±4.1, Mean of 3 runs, range 71.6 to 79.7 |
| Identification (low) | 87.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 | 82.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 |
| Identification (high) | 87.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 | 80.2% ±3.1, Mean of 3 runs, range 78.1 to 84.4 |
| OCR (low) | 59.8% | 67.6% |
| by category |
|
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| OCR (high) | 62.5% | 68.6% |
| by category |
|
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| Reasoning (low) | 64.2% ±3.0, Mean of 3 runs, range 60.3 to 66.2 | 63.8% ±1.0, Mean of 3 runs, range 62.9 to 64.9 |
| Reasoning (high) | 71.1% ±2.3, Mean of 3 runs, range 68.2 to 72.8 | 65.1% ±3.0, Mean of 3 runs, range 62.3 to 68.2 |
GPT-6 Luna vs Muse Spark Open: Overview
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 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.