Roboflow

Muse Wonder vs Qwen3.7 Plus

Compare Muse Wonder and Qwen3.7 Plus side-by-side.

Compare Muse Wonder vs Qwen3.7 Plus 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.

Models in this comparison

Muse Wonder vs Qwen3.7 Plus on Vision Evals

Muse Wonder scores higher on all five Vision Evals tasks.

The widest gap is Reasoning, where Muse Wonder leads 85.2% to 39.7%.

Overall, Muse Wonder averages 78.8% (#8 of 62) against 58.9% (#36 of 62) for Qwen3.7 Plus.

Qwen3.7 Plus is both cheaper ($0.0008 vs $0.034 per sample) and faster (7.8s vs 11.7s per sample).

Muse WonderQwen3.7 Plus

Muse Wonder vs Qwen3.7 Plus Comparison Table

Evals updated October 13, 2026Pricing updated October 13, 2026

PropertyMuse WonderQwen3.7 Plus
OrganizationMetaQwen
Categoryclosedclosed
Modalitymultimodal—
Release DateOct 2026Jun 2026
Context Window1.0M—
ParametersundisclosedUnknown
LicenseProprietaryUnknown
Commercial usecommercial use per vendor termsSee license terms
Pricing per 1M tokens
Input $/1M$4.00$0.320
Output $/1M$20.00$1.28
Vision Tasks
CaptioningSupportedDemo
ClassificationSupportedDemo
Object DetectionSupportedDemo
OCRSupportedDemo
Visual Question AnsweringSupportedDemo
Chart Question AnsweringSupportedNot listed
Document Question AnsweringSupportedNot listed
Image TaggingSupportedNot listed
Multi-Label ClassificationSupportedNot listed
Vision LanguageSupportedNot listed
Model Features
Foundation VisionSupportedNot listed
LLMs with Vision CapabilitiesSupportedNot listed
Multimodal VisionSupportedNot listed
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
78.8%
58.9%
Avg cost / sample$0.034$0.0008
Avg speed / sample11.68s7.77s
By task
Object Detection (low)
65.1%
±0.8, Mean of 3 runs, range 64.5 to 66.0
$0.044
60.1%
$0.0013
Object Detection (high)
66.0%
±0.7, Mean of 3 runs, range 65.2 to 66.5
$0.061
–
Counting (low)
76.6%
±1.3, Mean of 3 runs, range 75.7 to 78.4
$0.024
50.0%
$0.0004
Counting (high)
79.7%
±1.4, Mean of 3 runs, range 78.4 to 81.1
$0.043
–
Identification (low)
92.7%
±1.6, Mean of 3 runs, range 90.6 to 93.8
$0.014
84.4%
$0.0003
Identification (high)
90.6%
±0.0, Mean of 3 runs, range 90.6 to 90.6
$0.023
–
OCR (low)
74.3%
$0.034
60.3%
$0.0009
by category
Single value
67.4%
Transcription
93.2%
Structured JSON
90.5%
Text localization
40.1%
Single value
53.5%
Transcription
86.7%
Structured JSON
75.8%
Text localization
23.1%
OCR (high)
76.0%
$0.059
65.5%
$0.0042
by category
Single value
70.9%
Transcription
88.3%
Structured JSON
90.3%
Text localization
45.9%
Single value
58.3%
Transcription
89.7%
Structured JSON
81.3%
Text localization
30.4%
Reasoning (low)
85.2%
±0.3, Mean of 3 runs, range 84.8 to 85.4
$0.024
39.7%
$0.0003
Reasoning (high)
83.4%
±0.7, Mean of 3 runs, range 82.8 to 84.1
$0.047
68.2%
$0.0043

Muse Wonder vs Qwen3.7 Plus: Overview

Muse Wonder

Muse Wonder is a proprietary multimodal reasoning model from Meta, released on October 12, 2026 as the first step past the Muse Spark line on Meta's scaling ladder. It launched together with Muse Wonder Full, a larger configuration that is available only through Muse Code subscription plans, while Muse Wonder itself is served through the Meta Model API under the identifier muse-wonder-1.0. The model accepts text, images, video, audio, and PDF documents as input and returns text, with a context window of 1,048,576 tokens and graded reasoning effort settings. Meta trained both models at its Prometheus data center on larger base models than Muse Spark 1.3, and attributes their gains on unfamiliar tasks to post-training that transfers more effectively from a stronger base.

Meta positions Muse Wonder for long-running personal and professional agents: it plans multi-step work, delegates to sub-agents, calls tools in parallel, recovers from errors, and operates browsers and desktop or mobile applications. Two training objectives shape this behaviour. Active Memory carries goals, constraints, and earlier decisions across conversations and retrieves the details relevant to the current step, and Active Focus keeps the model trying alternative approaches when a task gets difficult while asking for clarification before important actions. Visual inputs such as screenshots, charts, and video feed the same reasoning loop, and the model emits bounding box coordinates for grounding queries. Meta's model card lists visual and spatial reasoning, especially 3D tasks, as a known limitation and recommends validating performance for applications that depend on precise spatial understanding.

Qwen3.7 Plus
No description available

Frequently Asked Questions

On Roboflow's Vision Evals, Muse Wonder performed better. It scores higher on all five vision tasks and averages 78.8% (#8 of 62) against 58.9% (#36 of 62) for Qwen3.7 Plus. 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 Wonder leads with 85.2% against 39.7%. This is the widest gap between the two models across the benchmark's tasks.

Qwen3.7 Plus is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0008 per sample against $0.034. Muse Wonder is priced at $4.00 per 1M input tokens and $20.00 per 1M output; Qwen3.7 Plus is priced at $0.32 per 1M input tokens and $1.28 per 1M output. Actual costs depend on your image sizes, prompts, and output length.

Qwen3.7 Plus is faster. Across Roboflow's Vision Evals it averaged 7.8s per inference against 11.7s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.