Roboflow

Claude Sonnet 5.5 vs Muse Ember

Claude Sonnet 5.5 and Muse Ember score 77.6% vs 76.6% averaged across five Vision Evals tasks (#12 and #16 of 67 models), so they are effectively tied.

  • Muse Ember wins 3 of the 5 tasks, but the widest gap is object detection, where Claude Sonnet 5.5 leads 74.3% vs 63.8%.
  • Muse Ember is about 1/8 of the cost: $0.0009 vs $0.0073 overall average cost per sample.
  • Muse Ember is faster: 6.1s vs 8.3s per sample.

Compare Claude Sonnet 5.5 vs Muse Ember 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.

Claude Sonnet 5.5 vs Muse Ember on Vision Evals

Score by taskClaude Sonnet 5.5Muse Ember
Object Detection
74.3
63.8
OCR
66.3
69.5
Reasoning
76.4
78.2
Counting
79.3
79.7
Identification
91.7
91.7

Roboflow Vision Evals. Full breakdown below.

Claude Sonnet 5.5 vs Muse Ember Comparison Table

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

PropertyClaude Sonnet 5.5Muse Ember
OrganizationAnthropicMeta
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateSep 2026Nov 2026
Context Window1.0M1.0M
ParametersUnknownUnknown
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$2.00$0.100
Output $/1M$10.00$0.500
Vision Tasks
CaptioningDemoSupported
ClassificationDemoSupported
Object DetectionDemoSupported
OCRDemoSupported
Visual Question AnsweringDemoSupported
Both support 5 more tasks
Model Features
Both support 3 more features
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
77.6%
76.6%
Avg cost / sample$0.0073$0.0009
Avg speed / sample8.33s6.08s
By task
Object Detection (low)
74.3%
±0.9, Mean of 3 runs, range 73.5 to 75.3
$0.0098
63.8%
±0.5, Mean of 3 runs, range 63.3 to 64.3
$0.0011
Object Detection (high)
76.8%
±0.4, Mean of 3 runs, range 76.5 to 77.3
$0.014
64.0%
±0.5, Mean of 3 runs, range 63.5 to 64.5
$0.0017
Counting (low)
79.3%
±0.7, Mean of 3 runs, range 78.4 to 79.7
$0.0042
79.7%
±1.4, Mean of 3 runs, range 78.4 to 81.1
$0.0006
Counting (high)
82.9%
±1.4, Mean of 3 runs, range 81.1 to 83.8
$0.0053
79.3%
±0.7, Mean of 3 runs, range 78.4 to 79.7
$0.0011
Identification (low)
91.7%
±3.1, Mean of 3 runs, range 87.5 to 93.8
$0.0029
91.7%
±1.6, Mean of 3 runs, range 90.6 to 93.8
$0.0004
Identification (high)
90.6%
±0.0, Mean of 3 runs, range 90.6 to 90.6
$0.0033
90.6%
±3.1, Mean of 3 runs, range 87.5 to 93.8
$0.0007
OCR (low)
66.3%
$0.0075
69.5%
$0.0010
by category
Single value
48.7%
Transcription
76.3%
Structured JSON
83.8%
Text localization
60.6%
Single value
61.7%
Transcription
95.5%
Structured JSON
85.1%
Text localization
35.3%
OCR (high)–
70.5%
$0.0017
by category
Single value
63.0%
Transcription
90.2%
Structured JSON
87.2%
Text localization
35.9%
Reasoning (low)
76.4%
±0.7, Mean of 3 runs, range 75.5 to 76.8
$0.0049
78.2%
±1.3, Mean of 3 runs, range 76.8 to 79.5
$0.0006
Reasoning (high)
83.9%
±1.7, Mean of 3 runs, range 82.1 to 85.4
$0.0061
81.9%
±1.7, Mean of 3 runs, range 80.1 to 83.4
$0.0013

Claude Sonnet 5.5 vs Muse Ember: Overview

Claude Sonnet 5.5

Claude Sonnet 5.5 is a proprietary multimodal language model from Anthropic and the second release in the Claude 5.5 family, following Claude Opus 5.5. It accepts interleaved text and image input and returns text, operating with a 1M token context window and a maximum output of 128K tokens per request. The model uses adaptive thinking by default, allocating variable reasoning effort per request rather than exposing a manual extended thinking toggle, and its training data cutoff is June 2026. Anthropic positions it as a faster, lower cost complement to Opus 5.5 for well scoped everyday tasks, bug fixing, and producing documents, slides, and spreadsheets.

Anthropic describes it as the first Sonnet model able to complete Pokemon Red from screenshots alone, and it generates output more than 30% faster than Sonnet 5 while using fewer tokens for equivalent work.

Muse Ember

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.

Frequently Asked Questions

On Roboflow's Vision Evals, Muse Ember performed better. It scores higher on 3 of the five vision tasks and averages 76.6% (#16 of 67) against 77.6% (#12 of 67) for Claude Sonnet 5.5. 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, Claude Sonnet 5.5 leads with 74.3% against 63.8%. 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 $0.0073. Claude Sonnet 5.5 is priced at $2.00 per 1M input tokens and $10.00 per 1M output; Muse Ember is priced at $0.10 per 1M input tokens and $0.50 per 1M output. 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 8.3s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.