Muse Spark 1.2 vs Qwen3.6 Plus
Compare Muse Spark 1.2 and Qwen3.6 Plus side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, and OCR.
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Muse Spark 1.2 vs Qwen3.6 Plus Comparison Table
Evals updated September 5, 2026Pricing updated September 20, 2026
| Property | Muse Spark 1.2 | Qwen3.6 Plus |
|---|---|---|
| Organization | Meta | Qwen |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Aug 2026 | Apr 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $1.25 | $0.325 |
| Output $/1M | $4.25 | $1.95 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 80.5% | Not evaluated |
| Avg cost / sample | $0.0072 | – |
| Avg speed / sample | 7.81s | – |
| By task | ||
| Object Detection (low) | 59.0% ±1.0, Mean of 3 runs, range 58.1 to 60.2 | – |
| Object Detection (high) | 60.5% ±0.3, Mean of 3 runs, range 60.2 to 60.7 | – |
| Counting (low) | 76.6% ±2.7, Mean of 3 runs, range 74.3 to 79.7 | – |
| Counting (high) | 75.2% ±2.0, Mean of 3 runs, range 73.0 to 77.0 | – |
| Identification (low) | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 | – |
| Identification (high) | 87.5% ±0.0, Mean of 3 runs, range 87.5 to 87.5 | – |
| OCR (low) | 93.6% ±0.6, Mean of 3 runs, range 92.9 to 94.1 | – |
| OCR (high) | 92.9% ±0.8, Mean of 3 runs, range 91.9 to 93.6 | – |
| Data Extraction (low) | 89.0% ±1.0, Mean of 3 runs, range 87.6 to 89.7 | – |
| Data Extraction (high) | 88.3% ±1.5, Mean of 3 runs, range 86.6 to 89.7 | – |
| Reasoning (low) | 75.1% ±0.3, Mean of 3 runs, range 74.8 to 75.5 | – |
| Reasoning (high) | 75.7% ±0.3, Mean of 3 runs, range 75.5 to 76.2 | – |
Muse Spark 1.2 vs Qwen3.6 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.
Qwen3.6 Plus is a flagship model in Alibaba’s Qwen Plus series, designed for agentic workflows, coding, and multi-step reasoning. It supports a 1 million token context window and up to 65,536 output tokens, with built-in reasoning capabilities. The model is available as a hosted, proprietary API through Alibaba Cloud.
Compared to Qwen3.5, it improves reliability in multi-step execution and frontend code generation, with stronger performance on agentic coding tasks. It also supports document and image understanding, though its vision capabilities are more limited than dedicated Qwen-VL models. Qwen3.6 Plus is part of a broader Qwen ecosystem that includes both closed-source APIs and open-weight models.
Frequently Asked Questions
Qwen3.6 Plus has not yet been evaluated on Roboflow's current Vision Evals, so this comparison shows specs, licensing, and pricing rather than benchmark scores.
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.