Roboflow

Gemma 4 26B A4B vs Muse Ember

Muse Ember averages 76.6% across five Vision Evals tasks and Gemma 4 26B A4B 54.1% across four (#16 and #54 of 67 models), so Muse Ember leads overall.

  • Muse Ember wins all 4 tasks both ran, with its widest lead on counting (79.7% vs 43.2%).
  • Muse Ember is about half the cost: $0.0009 vs $0.0019 overall average cost per sample.
  • Muse Ember is faster: 6.1s vs 27.8s per sample.

Compare Gemma 4 26B A4B 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.

Gemma 4 26B A4B vs Muse Ember on Vision Evals

Score by taskGemma 4 26B A4BMuse Ember
Counting
43.2
79.7
Reasoning
47.7
78.2
Object Detection
44.2
63.8
Identification
81.3
91.7

Roboflow Vision Evals. Full breakdown below.

Gemma 4 26B A4B vs Muse Ember Comparison Table

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

PropertyGemma 4 26B A4BMuse Ember
OrganizationGoogleMeta
Categoryopenclosed
Modalitymultimodalmultimodal
Release DateApr 2026Nov 2026
Context Window256K1.0M
Parameters25.2BUnknown
LicenseApache 2.0Proprietary
Commercial usefree for commercial usecommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$0.080$0.100
Output $/1M$0.100$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
54.1%*
76.6%
Quantizationsself-hosted
BF1651.6%FP854.1%AWQ-INT452.0%hardware →
Avg cost / sample$0.0019$0.0009
Avg speed / sample27.84s6.08s
By task
Object Detection (low)
44.2%
±0.7, Mean of 3 runs, range 43.5 to 44.8
$0
63.8%
±0.5, Mean of 3 runs, range 63.3 to 64.3
$0.0011
Object Detection (high)–
64.0%
±0.5, Mean of 3 runs, range 63.5 to 64.5
$0.0017
Counting (low)
43.2%
±2.0, Mean of 3 runs, range 41.9 to 46.0
$0
79.7%
±1.4, Mean of 3 runs, range 78.4 to 81.1
$0.0006
Counting (high)–
79.3%
±0.7, Mean of 3 runs, range 78.4 to 79.7
$0.0011
Identification (low)
81.3%
±3.1, Mean of 3 runs, range 78.1 to 84.4
$0
91.7%
±1.6, Mean of 3 runs, range 90.6 to 93.8
$0.0004
Identification (high)–
90.6%
±3.1, Mean of 3 runs, range 87.5 to 93.8
$0.0007
OCR (low)–
69.5%
$0.0010
by category
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)
47.7%
±2.0, Mean of 3 runs, range 45.0 to 49.0
$0
78.2%
±1.3, Mean of 3 runs, range 76.8 to 79.5
$0.0006
Reasoning (high)–
81.9%
±1.7, Mean of 3 runs, range 80.1 to 83.4
$0.0013

Gemma 4 26B A4B vs Muse Ember: Overview

Gemma 4 26B A4B

Gemma 4 26B A4B is the Mixture-of-Experts variant in Google's Gemma 4 family, with 25.2B total parameters but only 3.8B active per token. Built from the same Gemini 3 research as the 31B dense sibling and released as open weights under the Apache 2.0 license, it supports a 256K token context window with text and image input and configurable thinking mode. The "A4B" in the name refers to its approximately 4B active parameters. The MoE design makes it significantly faster at inference than the dense 31B, running nearly as fast as a 4B-parameter model while delivering roughly 97% of the dense model's quality.

For vision tasks, the 26B A4B shares the same multimodal capabilities as the 31B image understanding with variable aspect ratios and resolutions, and structured bounding box output for UI element detection. The tradeoff versus the 31B dense model is a small quality reduction in exchange for much faster inference and lower hardware requirements, fitting in 18GB of VRAM at 4-bit quantization. It ranked #6 among open models on the Arena AI text leaderboard at launch.

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 all 4 vision tasks and averages 76.6% (#16 of 67) against 54.1% (#54 of 67) for Gemma 4 26B A4B. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.

No. On the Vision Evals Counting benchmark at low effort, Muse Ember leads with 79.7% against 43.2%. 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 $0.0019. 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 27.8s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.