Roboflow

GPT-6.1 Sol vs Muse Ember

GPT-6.1 Sol and Muse Ember score 81.7% vs 76.6% averaged across five Vision Evals tasks (#7 and #16 of 67 models), so GPT-6.1 Sol leads overall.

  • GPT-6.1 Sol wins 3 of the 5 tasks, with its widest lead on object detection (80.8% vs 63.8%).
  • Muse Ember is about 1/7 of the cost: $0.0009 vs $0.0068 overall average cost per sample.
  • Muse Ember is faster: 6.1s vs 12.8s per sample.

Compare GPT-6.1 Sol 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.

GPT-6.1 Sol vs Muse Ember on Vision Evals

Score by taskGPT-6.1 SolMuse Ember
Object Detection
80.8
63.8
OCR
75.8
69.5
Reasoning
83.7
78.2
Identification
89.6
91.7
Counting
78.8
79.7

Roboflow Vision Evals. Full breakdown below.

GPT-6.1 Sol vs Muse Ember Comparison Table

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

PropertyGPT-6.1 SolMuse Ember
OrganizationOpenAIMeta
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateSep 2026Nov 2026
Context Window1.1M1.0M
ParametersundisclosedUnknown
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
Promptable Concept SegmentationDemoNot listed
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
81.7%
76.6%
Avg cost / sample$0.0068$0.0009
Avg speed / sample12.82s6.08s
By task
Object Detection (low)
80.8%
±0.1, Mean of 3 runs, range 80.7 to 80.9
$0.010
63.8%
±0.5, Mean of 3 runs, range 63.3 to 64.3
$0.0011
Object Detection (high)
81.6%
±0.4, Mean of 3 runs, range 81.1 to 82.0
$0.022
64.0%
±0.5, Mean of 3 runs, range 63.5 to 64.5
$0.0017
Counting (low)
78.8%
±3.4, Mean of 3 runs, range 75.7 to 82.4
$0.0037
79.7%
±1.4, Mean of 3 runs, range 78.4 to 81.1
$0.0006
Counting (high)
80.2%
±3.4, Mean of 3 runs, range 77.0 to 83.8
$0.0057
79.3%
±0.7, Mean of 3 runs, range 78.4 to 79.7
$0.0011
Identification (low)
89.6%
±1.6, Mean of 3 runs, range 87.5 to 90.6
$0.0025
91.7%
±1.6, Mean of 3 runs, range 90.6 to 93.8
$0.0004
Identification (high)
88.5%
±1.6, Mean of 3 runs, range 87.5 to 90.6
$0.0030
90.6%
±3.1, Mean of 3 runs, range 87.5 to 93.8
$0.0007
OCR (low)
75.8%
$0.0069
69.5%
$0.0010
by category
Single value
58.3%
Transcription
95.7%
Structured JSON
89.5%
Text localization
74.4%
Single value
61.7%
Transcription
95.5%
Structured JSON
85.1%
Text localization
35.3%
OCR (high)
75.8%
$0.016
70.5%
$0.0017
by category
Single value
56.5%
Transcription
96.0%
Structured JSON
90.4%
Text localization
76.7%
Single value
63.0%
Transcription
90.2%
Structured JSON
87.2%
Text localization
35.9%
Reasoning (low)
83.7%
±1.3, Mean of 3 runs, range 82.1 to 84.8
$0.0033
78.2%
±1.3, Mean of 3 runs, range 76.8 to 79.5
$0.0006
Reasoning (high)
88.7%
±2.0, Mean of 3 runs, range 87.4 to 91.4
$0.0042
81.9%
±1.7, Mean of 3 runs, range 80.1 to 83.4
$0.0013

GPT-6.1 Sol vs Muse Ember: Overview

GPT-6.1 Sol

GPT-6.1 Sol is a reasoning model in OpenAI's GPT-6 series that accepts text and image input and returns text. It is an upgrade to GPT-6 Sol positioned to approach the intelligence of the larger GPT-6 Astra model on agentic coding, computer use, and professional knowledge work. The model exposes an adjustable reasoning effort control, ranging from low settings for simple turns to maximum settings for harder tasks, and can be driven with tool use enabled or disabled. It operates over a context window of roughly one million tokens and emits up to 128,000 output tokens in a single response, which supports long-running agent loops over large codebases and multi-document collections. Audio and video inputs are not supported.

OpenAI reports that GPT-6.1 Sol performs on par with or better than GPT-6 Sol across its image input safety evaluations, and that the share of responses containing a factual error at low reasoning effort falls from 11.4 percent to 7.7 percent.

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, GPT-6.1 Sol performed better. It scores higher on 3 of the five vision tasks and averages 81.7% (#7 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, GPT-6.1 Sol leads with 80.8% 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 $0.0068. GPT-6.1 Sol 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 12.8s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.