Muse Ember vs Qwen3.6 Plus
Muse Ember: 76.6% average across five Vision Evals tasks (#16 of 67 models). We have not run Qwen3.6 Plus through Roboflow's Vision Evals, so there is no head-to-head score.
Compare Muse Ember vs Qwen3.6 Plus 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.
Muse Ember vs Qwen3.6 Plus Comparison Table
Evals updated November 10, 2026Pricing updated November 10, 2026
| Property | Muse Ember | Qwen3.6 Plus |
|---|---|---|
| Organization | Meta | Qwen |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Nov 2026 | Apr 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | Unknown | Unknown |
| License | Proprietary | Proprietary |
| Commercial use | commercial use per vendor terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $0.100 | $0.325 |
| Output $/1M | $0.500 | $1.95 |
| Vision Tasks | ||
| Captioning | Supported | Demo |
| OCR | Supported | Demo |
| Visual Question Answering | Supported | Demo |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 76.6% | Not evaluated |
| Avg cost / sample | $0.0009 | – |
| Avg speed / sample | 6.08s | – |
| By task | ||
| Object Detection (low) | 63.8% ±0.5, Mean of 3 runs, range 63.3 to 64.3 | – |
| Object Detection (high) | 64.0% ±0.5, Mean of 3 runs, range 63.5 to 64.5 | – |
| Counting (low) | 79.7% ±1.4, Mean of 3 runs, range 78.4 to 81.1 | – |
| Counting (high) | 79.3% ±0.7, Mean of 3 runs, range 78.4 to 79.7 | – |
| Identification (low) | 91.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 | – |
| Identification (high) | 90.6% ±3.1, Mean of 3 runs, range 87.5 to 93.8 | – |
| OCR (low) | 69.5% | – |
| by category |
| |
| OCR (high) | 70.5% | – |
| by category |
| |
| Reasoning (low) | 78.2% ±1.3, Mean of 3 runs, range 76.8 to 79.5 | – |
| Reasoning (high) | 81.9% ±1.7, Mean of 3 runs, range 80.1 to 83.4 | – |
Muse Ember vs Qwen3.6 Plus: Overview
Muse Ember is a proprietary multimodal reasoning model from Meta, released on November 9, 2026. It accepts text, images, video, audio, and PDF documents and returns text. The Meta Model API serves it as muse-ember-1.0, with a 1,048,576-token context window and up to 524,288 output tokens. Reasoning effort is adjustable from minimal to max.
Meta positions Ember for repeated tasks within larger agent workflows: tool calls, routing, coding subtasks, and processing collections of images and documents. It can caption or sort photos and extract fields from records while a larger Muse model handles planning. Meta recommends Spark or Wonder for harder single-shot coding and knowledge-heavy problems. Ember is available as a hosted model; its weights are not publicly released.
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.