Muse Ember vs Qwen3.5 9b
Muse Ember averages 76.6% across five Vision Evals tasks and Qwen3.5 9b 56.0% across four (#16 and #46 of 67 models), so Muse Ember leads overall.
- Muse Ember wins all 4 tasks both ran, with its widest lead on reasoning (78.2% vs 45.9%).
- Muse Ember is about half the cost: $0.0009 vs $0.0021 overall average cost per sample.
- Muse Ember is faster: 6.1s vs 41.4s per sample.
Roboflow Vision Evals. Full breakdown below.
Compare Muse Ember vs Qwen3.5 9b 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 Qwen3.5 9b on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Muse Ember vs Qwen3.5 9b Comparison Table
Evals updated November 10, 2026Pricing updated November 10, 2026
| Property | Muse Ember | Qwen3.5 9b |
|---|---|---|
| Organization | Meta | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Nov 2026 | Mar 2026 |
| Context Window | 1.0M | 262K |
| Parameters | Unknown | 9B |
| License | Proprietary | Apache 2.0 |
| Commercial use | commercial use per vendor terms | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $0.100 | $0.170 |
| Output $/1M | $0.500 | $0.250 |
| 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% | 56.0%* |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0009 | $0.0021 |
| Avg speed / sample | 6.08s | 41.36s |
| By task | ||
| Object Detection (low) | 63.8% ±0.5, Mean of 3 runs, range 63.3 to 64.3 | 38.1% ±5.7, Mean of 3 runs, range 33.5 to 44.9 |
| 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 | 56.8% ±1.4, Mean of 3 runs, range 55.4 to 58.1 |
| 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 | 83.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 |
| 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 | 45.9% ±1.7, Mean of 3 runs, range 44.4 to 47.7 |
| Reasoning (high) | 81.9% ±1.7, Mean of 3 runs, range 80.1 to 83.4 | – |
Muse Ember vs Qwen3.5 9b: 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.
Qwen3.5-9B is a 9-billion-parameter multimodal foundation model developed by Alibaba Cloud's Qwen team, released on March 2, 2026 as part of the Qwen3.5 model family.
The model supports vision-language inputs through an early-fusion multimodal architecture built on a dense hybrid foundation of Gated Delta Networks and Gated Attention. It can also operate in a text-only mode by skipping the vision encoder during inference. It provides a 262,144-token context window (extensible to ~1M tokens via YaRN) and is released under the Apache License 2.0. Within the current AI landscape, Qwen3.5-9B offers a strong balance of capability and efficiency, making it well-suited for multimodal assistants, document analysis, long-context reasoning, and developer-deployed agentic systems.
Frequently Asked Questions
On Roboflow's Vision Evals, Muse Ember performed better. It scores higher on all 4 vision tasks and averages 76.6% (#16 of 67) against 56.0% (#46 of 67) for Qwen3.5 9b. 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 Ember leads with 78.2% against 45.9%. This is the widest gap between the two models across the benchmark's tasks.
Muse Ember is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0009 per sample against $0.0021. Actual costs depend on your image sizes, prompts, and output length.
Muse Ember is faster. Across Roboflow's Vision Evals it averaged 6.1s per inference against 41.4s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.