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Muse Spark 1.2 vs Qwen3.6 Plus

Compare Muse Spark 1.2 and Qwen3.6 Plus side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, and OCR.

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MetaMuse Spark 1.2
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QwenQwen3.6 Plus
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Models in this comparison

Muse Spark 1.2 vs Qwen3.6 Plus Comparison Table

Evals updated August 6, 2026Pricing updated August 7, 2026

PropertyMuse Spark 1.2Qwen3.6 Plus
OrganizationMetaQwen
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateAug 2026Apr 2026
Context Window1.0M1.0M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$1.25$0.325
Output $/1M$4.25$1.95
Vision Tasks
CaptioningDemoDemo
Chart Question Answering
ClassificationDemo
Document Question Answering
Image Tagging
Multi-Label Classification
Object DetectionDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort
Overall
80.4%
Not evaluated
Avg cost / sample$0.0071
Avg speed / sample7.78s
By task
Object Detection
60.1%
$0.0094
Counting
74.3%
$0.0049
Identification
90.6%
$0.0038
OCR
93.8%
$0.0079
Data Extraction
88.7%
$0.0033
Reasoning (low)
74.8%
$0.0074
Reasoning (high)
76.2%
$0.012

Muse Spark 1.2 vs Qwen3.6 Plus: Overview

Muse Spark 1.2

Muse Spark 1.2 is a proprietary multimodal reasoning model from Meta Superintelligence Labs, released as a coding-focused update to Muse Spark 1.1. It accepts text, images, video, audio, and PDF documents and returns text, with a context window of roughly one million tokens that allows whole repositories, long documents, and extended agent trajectories to be held in a single request. The model thinks before answering, and the amount of reasoning effort it spends is configurable per request. Alongside its visual and document understanding, it supports structured output and parallel function calling, and it is designed to operate either as a planning agent that delegates work or as a subagent executing tasks in parallel.

Training for version 1.2 scaled up compute on coding tasks and widened the diversity of training environments, concentrating on long-horizon work such as whole-repository generation, large end-to-end projects, and automated research. Part of the training data was self-generated, with Muse Spark 1.1 producing coding environments and instruction-following templates and grading candidate solutions against them. The model was co-trained with the Muse Code terminal agent, incorporating rejection-sampled harness trajectories and that toolset. Meta reports 82.9 percent on Terminal-Bench 2.1, an improvement of 6.7 points over Muse Spark 1.1. Multimodal use cases documented for the family include visual-to-code generation and detailed image and video captioning.

Qwen3.6 Plus

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.

Frequently Asked Questions

Qwen3.6 Plus has not yet been evaluated on Roboflow's current Vision Evals, so this comparison shows specs, licensing, and pricing rather than benchmark scores.

Yes. The comparison demo on this page runs both models on the same image side by side for image captioning and open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.