Muse Spark Open vs Qwen3.6 27B
Muse Spark Open averages 69.4% across five Vision Evals tasks and Qwen3.6 27B 67.1% across four (#25 and #26 of 65 models), so Muse Spark Open leads overall.
- Muse Spark Open wins 2 of the 4 tasks both ran, with its widest lead on counting (75.7% vs 67.1%).
- Qwen3.6 27B is about a third of the cost: $0.0021 vs $0.0059 overall average cost per sample.
- Muse Spark Open is faster: 7.4s vs 42.1s per sample.
Roboflow Vision Evals. Full breakdown below.
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Muse Spark Open vs Qwen3.6 27B on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Muse Spark Open vs Qwen3.6 27B Comparison Table
Evals updated October 26, 2026Pricing updated October 31, 2026
| Property | Muse Spark Open | Qwen3.6 27B |
|---|---|---|
| Organization | Meta | Qwen |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Oct 2026 | Apr 2026 |
| Context Window | 1.0M | 262K |
| Parameters | 816B total / 42B active | 27B |
| License | Custom | Apache 2.0 |
| Commercial use | See license terms | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $1.25 | $0.450 |
| Output $/1M | $4.25 | $2.70 |
| Vision Tasks | ||
| Classification | Demo | Supported |
| Object Detection | Demo | Not listed |
| Video Classification | Not listed | Supported |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 69.4% | 67.1%* |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0059 | $0.0021 |
| Avg speed / sample | 7.37s | 42.09s |
| By task | ||
| Object Detection (low) | 57.8% ±0.6, Mean of 3 runs, range 57.0 to 58.2 | 59.7% ±0.9, Mean of 3 runs, range 59.0 to 60.8 |
| Object Detection (high) | 60.9% ±0.8, Mean of 3 runs, range 60.3 to 61.9 | – |
| Counting (low) | 75.7% ±2.7, Mean of 3 runs, range 73.0 to 78.4 | 67.1% ±4.7, Mean of 3 runs, range 62.2 to 71.6 |
| Counting (high) | 75.2% ±4.1, Mean of 3 runs, range 71.6 to 79.7 | – |
| Identification (low) | 82.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 | 82.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 |
| Identification (high) | 80.2% ±3.1, Mean of 3 runs, range 78.1 to 84.4 | – |
| OCR (low) | 67.6% | – |
| by category |
| |
| OCR (high) | 68.6% | – |
| by category |
| |
| Reasoning (low) | 63.8% ±1.0, Mean of 3 runs, range 62.9 to 64.9 | 59.2% ±1.7, Mean of 3 runs, range 57.6 to 60.9 |
| Reasoning (high) | 65.1% ±3.0, Mean of 3 runs, range 62.3 to 68.2 | – |
Muse Spark Open vs Qwen3.6 27B: Overview
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.
Qwen3.6-27B is a dense 27-billion-parameter multimodal language model developed by Alibaba's Qwen team and released on April 22, 2026. It combines a causal language model with an integrated vision encoder, supporting text, image, and video inputs natively. The architecture employs a hybrid attention design that interleaves Gated DeltaNet linear attention blocks with standard Gated Attention layers across 64 transformer layers with a hidden dimension of 5,120. Unlike Mixture-of-Experts variants in the Qwen3.6 family, all 27 billion parameters are active on every inference pass, simplifying deployment and quantization. The model supports a native context window of 262,144 tokens, extensible to approximately 1,010,000 tokens via YaRN scaling. It is released under the Apache 2.0 license with open weights available on Hugging Face and ModelScope.
The model introduces two notable capabilities relative to prior Qwen releases: enhanced agentic coding support covering frontend workflows and repository-level reasoning, and a Thinking Preservation mechanism that retains chain-of-thought reasoning context across multi-turn conversation history to reduce redundant token generation in iterative agent sessions. It supports both a thinking mode for multi-step reasoning and a non-thinking mode for faster responses within a single model. Vision capabilities include chart understanding (CharXiv RQ: 78.4), OCR (CC-OCR: 81.2), and video understanding (VideoMME with subtitles: 87.7).