Muse Ember vs Qwen2.5 VL 7B Instruct
Muse Ember: 76.6% average across five Vision Evals tasks (#17 of 68 models). We have not run Qwen2.5 VL 7B Instruct through Roboflow's Vision Evals, so there is no head-to-head score.
Compare Muse Ember vs Qwen2.5 VL 7B Instruct 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 Qwen2.5 VL 7B Instruct Comparison Table
Evals updated November 10, 2026Pricing updated November 11, 2026
| Property | Muse Ember | Qwen2.5 VL 7B Instruct |
|---|---|---|
| Organization | Meta | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Nov 2026 | Jan 2025 |
| Context Window | 1.0M | 33K |
| Parameters | Unknown | 7B |
| License | Proprietary | Apache 2.0 |
| Commercial use | commercial use per vendor terms | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $0.100 | No published price |
| Output $/1M | $0.500 | No published price |
| Vision Tasks | ||
| 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 Qwen2.5 VL 7B Instruct: 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.
Qwen2.5-VL-7B-Instruct is a 7-billion parameter vision-language model from Alibaba’s QwenLM team, released on January 26, 2025 under the Apache 2.0 license. It is the instruction-tuned variant of the 7B scale in the Qwen2.5-VL family, designed to process multimodal inputs such as text, images, charts, documents, and video. The model enables structured outputs—including JSON for structured content and bounding boxes for visual localization. Weights are publicly available on Hugging Face and GitHub, making it suitable for both research and applied multimodal use.