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Gemini 4 Carbon vs Muse Wonder

Gemini 4 Carbon and Muse Wonder score 89.4% vs 78.8% averaged across five Vision Evals tasks (#1 and #12 of 68 models), so Gemini 4 Carbon leads overall.

  • Gemini 4 Carbon wins all 5 tasks, with its widest lead on object detection (84.5% vs 65.1%).
  • Gemini 4 Carbon is about 1/6 of the cost: $0.0061 vs $0.034 overall average cost per sample.
  • Gemini 4 Carbon is faster: 8.7s vs 11.7s per sample.

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GoogleGemini 4 Carbon
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MetaMuse Wonder
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Gemini 4 Carbon vs Muse Wonder on Vision Evals

Score by taskGemini 4 CarbonMuse Wonder
Object Detection
84.5
65.1
Counting
90.1
76.6
OCR
87.2
74.3
Reasoning
91.4
85.2
Identification
93.8
92.7

Roboflow Vision Evals. Full breakdown below.

Gemini 4 Carbon vs Muse Wonder Comparison Table

Evals updated November 10, 2026Pricing updated November 10, 2026

PropertyGemini 4 CarbonMuse Wonder
OrganizationGoogleMeta
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateNov 2026Oct 2026
Context Window1.0M1.0M
ParametersUnknownundisclosed
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1MNo published price$4.00
Output $/1MNo published price$20.00
Vision Tasks
Promptable Concept SegmentationDemoNot listed
Both support 10 more tasks
Model Features
Both support 3 more features
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
89.4%
78.8%
Avg cost / sample$0.0061$0.034
Avg speed / sample8.71s11.68s
By task
Object Detection (low)
84.5%
±0.3, Mean of 3 runs, range 84.2 to 84.8
$0.011
65.1%
±0.8, Mean of 3 runs, range 64.5 to 66.0
$0.044
Object Detection (high)
85.9%
±0.3, Mean of 3 runs, range 85.6 to 86.2
$0.102
66.0%
±0.7, Mean of 3 runs, range 65.2 to 66.5
$0.061
Counting (low)
90.1%
±1.4, Mean of 3 runs, range 89.2 to 91.9
$0.0024
76.6%
±1.3, Mean of 3 runs, range 75.7 to 78.4
$0.024
Counting (high)
86.9%
±2.7, Mean of 3 runs, range 83.8 to 89.2
$0.075
79.7%
±1.4, Mean of 3 runs, range 78.4 to 81.1
$0.043
Identification (low)
93.8%
±0.0, Mean of 3 runs, range 93.8 to 93.8
$0.0024
92.7%
±1.6, Mean of 3 runs, range 90.6 to 93.8
$0.014
Identification (high)
96.9%
±0.0, Mean of 3 runs, range 96.9 to 96.9
$0.019
90.6%
±0.0, Mean of 3 runs, range 90.6 to 90.6
$0.023
OCR (low)
87.2%
$0.0053
74.3%
$0.034
by category
Single value
80.4%
Transcription
96.6%
Structured JSON
93.0%
Text localization
84.2%
Single value
67.4%
Transcription
93.2%
Structured JSON
90.5%
Text localization
40.1%
OCR (high)
89.7%
$0.102
76.0%
$0.059
by category
Single value
85.2%
Transcription
99.1%
Structured JSON
93.9%
Text localization
85.2%
Single value
70.9%
Transcription
88.3%
Structured JSON
90.3%
Text localization
45.9%
Reasoning (low)
91.4%
±1.7, Mean of 3 runs, range 90.1 to 93.4
$0.0025
85.2%
±0.3, Mean of 3 runs, range 84.8 to 85.4
$0.024
Reasoning (high)
92.3%
±0.3, Mean of 3 runs, range 92.0 to 92.7
$0.057
83.4%
±0.7, Mean of 3 runs, range 82.8 to 84.1
$0.047

Gemini 4 Carbon vs Muse Wonder: Overview

Gemini 4 Carbon

Gemini 4 Carbon is a multimodal model from Google. It builds on Gemini 4 Argon and supports reasoning over text and images. Google describes improvements in coding and long-running tasks. Model weights are not publicly released.

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.

Frequently Asked Questions

On Roboflow's Vision Evals, Gemini 4 Carbon performed better. It scores higher on all five vision tasks and averages 89.4% (#1 of 68) against 78.8% (#12 of 68) for Muse Wonder. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.

Yes. On the Vision Evals Object Detection benchmark at low effort, Gemini 4 Carbon leads with 84.5% against 65.1%. This is the widest gap between the two models across the benchmark's tasks.

Gemini 4 Carbon is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0061 per sample against $0.034. Actual costs depend on your image sizes, prompts, and output length.

Gemini 4 Carbon is faster. Across Roboflow's Vision Evals it averaged 8.7s per inference against 11.7s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.

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