Roboflow

Claude Opus 5.5 vs Muse Ember

Claude Opus 5.5 and Muse Ember score 82.0% vs 76.6% averaged across five Vision Evals tasks (#5 and #16 of 67 models), so Claude Opus 5.5 leads overall.

  • Claude Opus 5.5 wins all 5 tasks, with its widest lead on object detection (74.5% vs 63.8%).
  • Muse Ember is under a tenth of the cost: $0.0009 vs $0.015 overall average cost per sample.
  • Muse Ember is faster: 6.1s vs 7.6s per sample.

Compare Claude Opus 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 Opus 5.5 vs Muse Ember on Vision Evals

Score by taskClaude Opus 5.5Muse Ember
Object Detection
74.5
63.8
OCR
78.3
69.5
Reasoning
83.0
78.2
Identification
93.8
91.7
Counting
80.6
79.7

Roboflow Vision Evals. Full breakdown below.

Claude Opus 5.5 vs Muse Ember Comparison Table

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

PropertyClaude Opus 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$4.00$0.100
Output $/1M$20.00$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
82.0%
76.6%
Avg cost / sample$0.015$0.0009
Avg speed / sample7.63s6.08s
By task
Object Detection (low)
74.5%
±0.5, Mean of 3 runs, range 74.0 to 75.0
$0.021
63.8%
±0.5, Mean of 3 runs, range 63.3 to 64.3
$0.0011
Object Detection (high)
76.0%
±1.1, Mean of 3 runs, range 74.7 to 77.0
$0.030
64.0%
±0.5, Mean of 3 runs, range 63.5 to 64.5
$0.0017
Counting (low)
80.6%
±2.0, Mean of 3 runs, range 78.4 to 82.4
$0.0081
79.7%
±1.4, Mean of 3 runs, range 78.4 to 81.1
$0.0006
Counting (high)
82.0%
±2.0, Mean of 3 runs, range 79.7 to 83.8
$0.0098
79.3%
±0.7, Mean of 3 runs, range 78.4 to 79.7
$0.0011
Identification (low)
93.8%
±0.0, Mean of 3 runs, range 93.8 to 93.8
$0.0058
91.7%
±1.6, Mean of 3 runs, range 90.6 to 93.8
$0.0004
Identification (high)
95.8%
±1.6, Mean of 3 runs, range 93.8 to 96.9
$0.0067
90.6%
±3.1, Mean of 3 runs, range 87.5 to 93.8
$0.0007
OCR (low)
78.3%
$0.016
69.5%
$0.0010
by category
Single value
67.8%
Transcription
96.7%
Structured JSON
91.5%
Text localization
61.0%
Single value
61.7%
Transcription
95.5%
Structured JSON
85.1%
Text localization
35.3%
OCR (high)
79.9%
$0.023
70.5%
$0.0017
by category
Single value
69.6%
Transcription
96.1%
Structured JSON
94.1%
Text localization
61.1%
Single value
63.0%
Transcription
90.2%
Structured JSON
87.2%
Text localization
35.9%
Reasoning (low)
83.0%
±1.0, Mean of 3 runs, range 82.1 to 84.1
$0.0090
78.2%
±1.3, Mean of 3 runs, range 76.8 to 79.5
$0.0006
Reasoning (high)
85.9%
±2.6, Mean of 3 runs, range 82.8 to 88.1
$0.011
81.9%
±1.7, Mean of 3 runs, range 80.1 to 83.4
$0.0013

Claude Opus 5.5 vs Muse Ember: Overview

Claude Opus 5.5

Claude Opus 5.5 is a proprietary multimodal reasoning model from Anthropic and the first entry in the Claude 5.5 family. It accepts interleaved text and image input and returns text, with a one million token context window and up to 128,000 output tokens per response. Adaptive thinking is always enabled on this model and cannot be disabled; thinking depth is instead governed by an effort parameter with five levels, where medium is the default, a change from the high default used by Claude Opus 5 and earlier Opus models. Anthropic reports a knowledge cutoff of June 2026.

On the visual side, Anthropic characterizes Opus 5.5 as its strongest Opus release for vision and computer use, describing improved reading of dense documents, charts, screenshots, and diagrams for document extraction and visual analysis tasks. The accompanying system card states that Opus 5.5 scored higher than Opus 5 on every evaluation in its capability summary, with the largest gains concentrated in agentic coding, visual reasoning, computer use, and long-horizon knowledge work. The model ships with safety classifiers covering biology and cybersecurity that can route blocked requests to earlier Claude models.

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, Claude Opus 5.5 performed better. It scores higher on all five vision tasks and averages 82.0% (#5 of 67) against 76.6% (#16 of 67) for Muse Ember. 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 Opus 5.5 leads with 74.5% 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 $0.015. Claude Opus 5.5 is priced at $4.00 per 1M input tokens and $20.00 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 7.6s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.