Muse Spark 1.3 vs Qwen3.7 Plus
Compare Muse Spark 1.3 and Qwen3.7 Plus side-by-side. See how these vision models stack up in Object Detection, Open Prompt, OCR, Classification, and Image Captioning.
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Models in this comparison
Muse Spark 1.3 vs Qwen3.7 Plus on Vision Evals
Muse Spark 1.3 scores higher on 4 of the five Vision Evals tasks.
The widest gap is Reasoning, where Muse Spark 1.3 leads 73.3% to 39.7%.
Overall, Muse Spark 1.3 averages 73.9% (#16 of 63) against 58.9% (#37 of 63) for Qwen3.7 Plus.
Qwen3.7 Plus is both cheaper ($0.0008 vs $0.0084 per sample) and faster (7.8s vs 25.5s per sample).
Muse Spark 1.3 vs Qwen3.7 Plus Comparison Table
Evals updated October 13, 2026Pricing updated October 17, 2026
| Property | Muse Spark 1.3 | Qwen3.7 Plus |
|---|---|---|
| Organization | Meta | Qwen |
| Category | closed | closed |
| Modality | multimodal | — |
| Release Date | Sep 2026 | Jun 2026 |
| Context Window | 1.0M | — |
| Parameters | Unknown | Unknown |
| License | Proprietary | Unknown |
| Commercial use | commercial use per vendor terms | See license terms |
| Pricing per 1M tokens | ||
| Input $/1M | $1.25 | $0.320 |
| Output $/1M | $4.25 | $1.28 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Classification | Demo | Demo |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Visual Question Answering | Demo | Demo |
| Chart Question Answering | Supported | Not listed |
| Document Question Answering | Supported | Not listed |
| Image Tagging | Supported | Not listed |
| Multi-Label Classification | Supported | Not listed |
| Vision Language | Supported | Not listed |
| Model Features | ||
| Foundation Vision | Supported | Not listed |
| LLMs with Vision Capabilities | Supported | Not listed |
| Multimodal Vision | Supported | Not listed |
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 73.9% | 58.9% |
| Avg cost / sample | $0.0084 | $0.0008 |
| Avg speed / sample | 25.50s | 7.77s |
| By task | ||
| Object Detection (low) | 58.6% ±0.7, Mean of 3 runs, range 58.0 to 59.4 | 60.1% |
| Object Detection (high) | 56.6% ±2.4, Mean of 3 runs, range 54.5 to 59.3 | – |
| Counting (low) | 74.3% ±2.0, Mean of 3 runs, range 73.0 to 77.0 | 50.0% |
| Counting (high) | 75.7% ±3.4, Mean of 3 runs, range 73.0 to 79.7 | – |
| Identification (low) | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 | 84.4% |
| Identification (high) | 86.5% ±1.6, Mean of 3 runs, range 84.4 to 87.5 | – |
| OCR (low) | 70.4% | 60.3% |
| by category |
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| OCR (high) | 69.8% | 65.5% |
| by category |
|
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| Reasoning (low) | 73.3% ±1.0, Mean of 3 runs, range 72.2 to 74.2 | 39.7% |
| Reasoning (high) | 73.1% ±1.0, Mean of 3 runs, range 72.2 to 74.2 | 68.2% |
Muse Spark 1.3 vs Qwen3.7 Plus: Overview
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 4 of the five vision tasks and averages 73.9% (#16 of 63) against 58.9% (#37 of 63) for Qwen3.7 Plus. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Reasoning benchmark at low effort, Muse Spark 1.3 leads with 73.3% against 39.7%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3.7 Plus is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0008 per sample against $0.0084. Muse Spark 1.3 is priced at $1.25 per 1M input tokens and $4.25 per 1M output; Qwen3.7 Plus is priced at $0.32 per 1M input tokens and $1.28 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
Qwen3.7 Plus is faster. Across Roboflow's Vision Evals it averaged 7.8s per inference against 25.5s. 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 object detection and open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.