Muse Spark 1.3 vs Qwen3.5 122B A10B
Compare Muse Spark 1.3 and Qwen3.5 122B A10B side-by-side. See how these vision models stack up in Open Prompt, OCR, and Image Captioning.
Compare Muse Spark 1.3 vs Qwen3.5 122B A10B live
Run the same image across every model that supports a task and compare their outputs side-by-side.
Extract and compare text from images across multiple models.
Upload an image
Drag and drop an image here, or click to browse
Models in this comparison
Muse Spark 1.3 vs Qwen3.5 122B A10B Comparison Table
Evals updated September 5, 2026Pricing updated September 21, 2026
| Property | Muse Spark 1.3 | Qwen3.5 122B A10B |
|---|---|---|
| Organization | Meta | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Feb 2026 |
| Context Window | 1.0M | 256K |
| Parameters | 122B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $1.25 | $0.260 |
| Output $/1M | $4.25 | $2.08 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 79.8% | Not evaluated |
| Avg cost / sample | $0.0075 | – |
| Avg speed / sample | 23.14s | – |
| By task | ||
| Object Detection (low) | 58.6% ±0.7, Mean of 3 runs, range 58.0 to 59.4 | – |
| Object Detection (high) | 56.6% ±2.4, Mean of 3 runs, range 54.5 to 59.3 | – |
| Counting (low) | 74.3% ±2.0, Mean of 3 runs, range 73.0 to 77.0 | – |
| Counting (high) | 75.7% ±3.4, Mean of 3 runs, range 73.0 to 79.7 | – |
| Identification (low) | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 | – |
| Identification (high) | 86.5% ±1.6, Mean of 3 runs, range 84.4 to 87.5 | – |
| OCR (low) | 91.3% ±0.5, Mean of 3 runs, range 90.7 to 91.6 | – |
| OCR (high) | 86.9% ±4.1, Mean of 3 runs, range 82.2 to 90.4 | – |
| Data Extraction (low) | 88.7% ±1.5, Mean of 3 runs, range 86.6 to 89.7 | – |
| Data Extraction (high) | 87.6% ±0.0, Mean of 3 runs, range 87.6 to 87.6 | – |
| Reasoning (low) | 73.3% ±1.0, Mean of 3 runs, range 72.2 to 74.2 | – |
| Reasoning (high) | 73.1% ±1.0, Mean of 3 runs, range 72.2 to 74.2 | – |
Muse Spark 1.3 vs Qwen3.5 122B A10B: Overview
Muse Spark 1.3 is a proprietary multimodal reasoning model from Meta Superintelligence Labs and the fourth Muse Spark release in five months, arriving on September 2, 2026. It takes text, images, video, and document files as input and returns text, and it operates over a context window of 1,048,576 tokens. Meta trains the model for long-horizon agentic work, so it carries accumulated context and prior tool results forward across many turns, reconciles messy or conflicting inputs, and asks for clarification when a task is underspecified. Visual inputs such as screenshots and video clips feed a reasoning loop that runs against a real execution environment rather than a scripted sequence of steps.
The model exposes graded reasoning effort settings. An xhigh configuration is generally available at launch, while a max reasoning configuration aimed at harder reasoning and agentic problems arrives after further safety testing. Artificial Analysis measures Muse Spark 1.3 (max) at 62 on its Intelligence Index and the xhigh configuration at 61, with agentic tool-use evaluations driving most of the gain over Muse Spark 1.2; max reaches 52% on Tau3-Bench Banking by spending more turns and reasoning tokens than xhigh. Prior Muse Spark versions emit bounding box coordinates, transcriptions, and structured field extractions from images on Roboflow Vision Evals.
Qwen3.5-122B-A10B is a high-capacity multimodal Mixture-of-Experts (MoE) model developed by Alibaba’s Qwen team as part of the Qwen3.5 model family. The architecture contains 122 billion total parameters while activating roughly 10 billion per token through sparse expert routing, allowing the model to balance large-scale reasoning ability with relatively efficient inference compared to dense models of similar size.
The model is designed to process both text and visual inputs within a unified multimodal framework, enabling tasks that require reasoning across images, documents, charts, and natural language. This makes it suitable for applications such as document understanding, diagram interpretation, and complex visual question answering.
Qwen3.5-122B-A10B supports a native context window of approximately 256,000 tokens, which can be extended further through techniques such as YaRN scaling to support very long-context workloads. Released under the Apache 2.0 license, it builds on earlier Qwen multimodal systems and provides developers with an open-weight model capable of handling demanding multimodal reasoning and analysis tasks.