Muse Spark 1.2 vs Qwen3.7 Plus
Compare Muse Spark 1.2 and Qwen3.7 Plus side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, Classification, Object Detection, and OCR.
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Models in this comparison
Muse Spark 1.2 vs Qwen3.7 Plus on Vision Evals
Muse Spark 1.2 scores higher on all six Vision Evals tasks.
The widest gap is Reasoning, where Muse Spark 1.2 leads 74.8% to 39.7%.
Overall, Muse Spark 1.2 averages 80.4% (#6 of 31) against 67.4% (#18 of 31) for Qwen3.7 Plus.
Qwen3.7 Plus is both cheaper ($0.0008 vs $0.0071 per sample) and faster (7.0s vs 7.8s per sample).
Muse Spark 1.2 vs Qwen3.7 Plus Comparison Table
Evals updated August 20, 2026Pricing updated August 24, 2026
| Property | Muse Spark 1.2 | Qwen3.7 Plus |
|---|---|---|
| Organization | Meta | Qwen |
| Category | closed | closed |
| Modality | multimodal | — |
| Release Date | Aug 2026 | — |
| Context Window | 1.0M | — |
| Parameters | ||
| License | Proprietary | |
| 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 | ||
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Vision Language | ||
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 80.4% | 67.4% |
| Avg cost / sample | $0.0071 | $0.0008 |
| Avg speed / sample | 7.78s | 7.01s |
| By task | ||
| Object Detection | 60.2% $0.0094 | 60.1% $0.0013 |
| Counting | 74.3% $0.0049 | 50.0% $0.0004 |
| Identification | 90.6% $0.0038 | 84.4% $0.0003 |
| OCR | 93.8% $0.0079 | 86.5% $0.0009 |
| Data Extraction | 88.7% $0.0033 | 83.5% $0.0004 |
| Reasoning (low) | 74.8% $0.0074 | 39.7% $0.0003 |
| Reasoning (high) | 76.2% $0.012 | 68.2% $0.0043 |
Muse Spark 1.2 vs Qwen3.7 Plus: Overview
Muse Spark 1.2 is a proprietary multimodal reasoning model from Meta Superintelligence Labs, released as a coding-focused update to Muse Spark 1.1. It accepts text, images, video, audio, and PDF documents and returns text, with a context window of roughly one million tokens that allows whole repositories, long documents, and extended agent trajectories to be held in a single request. The model thinks before answering, and the amount of reasoning effort it spends is configurable per request. Alongside its visual and document understanding, it supports structured output and parallel function calling, and it is designed to operate either as a planning agent that delegates work or as a subagent executing tasks in parallel.
Training for version 1.2 scaled up compute on coding tasks and widened the diversity of training environments, concentrating on long-horizon work such as whole-repository generation, large end-to-end projects, and automated research. Part of the training data was self-generated, with Muse Spark 1.1 producing coding environments and instruction-following templates and grading candidate solutions against them. The model was co-trained with the Muse Code terminal agent, incorporating rejection-sampled harness trajectories and that toolset. Meta reports 82.9 percent on Terminal-Bench 2.1, an improvement of 6.7 points over Muse Spark 1.1. Multimodal use cases documented for the family include visual-to-code generation and detailed image and video captioning.
Frequently Asked Questions
On Roboflow's Vision Evals, Muse Spark 1.2 performed better. It scores higher on all six vision tasks and averages 80.4% (#6 of 31) against 67.4% (#18 of 31) 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 1.2 leads with 74.8% 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 $0.0071. Muse Spark 1.2 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 7.0s per inference against 7.8s. 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 image captioning and open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.