Muse Ember vs Qwen3.5 35B A3B
Muse Ember averages 76.6% across five Vision Evals tasks and Qwen3.5 35B A3B 62.5% across four (#16 and #36 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 54.1%).
- Muse Ember is about half the cost: $0.0009 vs $0.0016 overall average cost per sample.
- Muse Ember is faster: 6.1s vs 31.9s per sample.
Roboflow Vision Evals. Full breakdown below.
Compare Muse Ember vs Qwen3.5 35B A3B 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 35B A3B on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Muse Ember vs Qwen3.5 35B A3B Comparison Table
Evals updated November 10, 2026Pricing updated November 10, 2026
| Property | Muse Ember | Qwen3.5 35B A3B |
|---|---|---|
| Organization | Meta | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Nov 2026 | Feb 2026 |
| Context Window | 1.0M | 262K |
| Parameters | Unknown | 35B |
| License | Proprietary | Apache 2.0 |
| Commercial use | commercial use per vendor terms | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $0.100 | $0.140 |
| Output $/1M | $0.500 | $1.00 |
| 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% | 62.5%* |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0009 | $0.0016 |
| Avg speed / sample | 6.08s | 31.88s |
| By task | ||
| Object Detection (low) | 63.8% ±0.5, Mean of 3 runs, range 63.3 to 64.3 | 52.9% ±3.2, Mean of 3 runs, range 49.5 to 55.9 |
| 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 | 62.6% ±2.0, Mean of 3 runs, range 60.8 to 64.9 |
| 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 | 80.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 |
| 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 | 54.1% ±0.3, Mean of 3 runs, range 53.6 to 54.3 |
| Reasoning (high) | 81.9% ±1.7, Mean of 3 runs, range 80.1 to 83.4 | – |
Muse Ember vs Qwen3.5 35B A3B: 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.
The Qwen3.5-35B-A3B is a native vision-language model developed by Alibaba Cloud’s Qwen team, released on February 24, 2026, as a high-efficiency entry in the Qwen 3.5 family. It utilizes a sophisticated hybrid architecture that integrates Gated Delta Networks with a sparse Mixture-of-Experts (MoE) system. While the model houses 35 billion total parameters, its routing mechanism activates only 8 routed experts and 1 shared expert per token, totaling approximately 3 billion active parameters. This design achieves cross-generational parity with the previous flagship Qwen3-235B dense model, delivering comparable reasoning and multimodal intelligence with significantly reduced inference latency and compute requirements. Available under the Apache 2.0 license, it is released in both base and instruction-tuned variants for seamless integration with open-source stacks like vLLM and Hugging Face Transformers.
Designed for the emerging era of agentic AI, the model utilizes a unified multimodal foundation built through early-fusion training. This approach allows it to outperform the prior Qwen3-VL series in spatial grounding, document analysis, and UI/GUI interaction. It features a native context window of 262,144 tokens, which is extensible up to 1,010,000 tokensvia RoPE scaling, and provides global support for 201 languages and dialects. This combination of a compact active parameter count and frontier-level visual comprehension makes it a versatile tool for developers requiring a balance of high-throughput speed and sophisticated visual reasoning for long-context workflows.
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 62.5% (#36 of 67) for Qwen3.5 35B A3B. 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 54.1%. 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 $0.0016. 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 31.9s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.