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Gemma 4 26B A4B vs Muse Spark 1.3

Compare Gemma 4 26B A4B and Muse Spark 1.3 side-by-side. See how these vision models stack up in Image Captioning, OCR, Open Prompt, Object Detection, and Classification.

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GoogleGemma 4 26B A4B
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MetaMuse Spark 1.3
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Models in this comparison

Gemma 4 26B A4B vs Muse Spark 1.3 on Vision Evals

Muse Spark 1.3 scores higher on all 4 Vision Evals tasks.

The widest gap is Counting, where Muse Spark 1.3 leads 74.3% to 43.2%.

Overall, Gemma 4 26B A4B averages 54.1% (#50 of 63) against 73.9% (#16 of 63) for Muse Spark 1.3.

Gemma 4 26B A4B is cheaper ($0.0019 vs $0.0084 per sample), while Muse Spark 1.3 is faster (25.5s vs 27.8s per sample).

Gemma 4 26B A4BMuse Spark 1.3

Gemma 4 26B A4B vs Muse Spark 1.3 Comparison Table

Evals updated October 13, 2026Pricing updated October 18, 2026

PropertyGemma 4 26B A4BMuse Spark 1.3
OrganizationGoogleMeta
Categoryopenclosed
Modalitymultimodalmultimodal
Release DateApr 2026Sep 2026
Context Window256K1.0M
Parameters25.2BUnknown
LicenseApache 2.0Proprietary
Commercial usefree for commercial usecommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$0.068$1.25
Output $/1M$0.225$4.25
Vision Tasks
CaptioningDemoDemo
Chart Question AnsweringSupportedSupported
ClassificationDemoDemo
Document Question AnsweringSupportedSupported
Image TaggingSupportedSupported
Multi-Label ClassificationSupportedSupported
Object DetectionDemoDemo
OCRDemoDemo
Vision LanguageSupportedSupported
Visual Question AnsweringDemoDemo
Model Features
Foundation VisionSupportedSupported
LLMs with Vision CapabilitiesSupportedSupported
Multimodal VisionSupportedSupported
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
54.1%
4/5 tasks
73.9%
Quantizationsself-hosted
BF1651.6%FP854.1%AWQ-INT452.0%hardware →
Avg cost / sample$0.0019$0.0084
Avg speed / sample27.84s25.50s
By task
Object Detection (low)
44.2%
±0.7, Mean of 3 runs, range 43.5 to 44.8
$0
58.6%
±0.7, Mean of 3 runs, range 58.0 to 59.4
$0.011
Object Detection (high)–
56.6%
±2.4, Mean of 3 runs, range 54.5 to 59.3
$0.017
Counting (low)
43.2%
±2.0, Mean of 3 runs, range 41.9 to 46.0
$0
74.3%
±2.0, Mean of 3 runs, range 73.0 to 77.0
$0.0049
Counting (high)–
75.7%
±3.4, Mean of 3 runs, range 73.0 to 79.7
$0.0094
Identification (low)
81.3%
±3.1, Mean of 3 runs, range 78.1 to 84.4
$0
92.7%
±1.6, Mean of 3 runs, range 90.6 to 93.8
$0.0036
Identification (high)–
86.5%
±1.6, Mean of 3 runs, range 84.4 to 87.5
$0.0063
OCR (low)–
70.4%
$0.0086
by category
Single value
62.6%
Transcription
91.8%
Structured JSON
88.0%
Text localization
33.3%
OCR (high)–
69.8%
$0.015
by category
Single value
62.2%
Transcription
86.8%
Structured JSON
87.2%
Text localization
35.2%
Reasoning (low)
47.7%
±2.0, Mean of 3 runs, range 45.0 to 49.0
$0
73.3%
±1.0, Mean of 3 runs, range 72.2 to 74.2
$0.0064
Reasoning (high)–
73.1%
±1.0, Mean of 3 runs, range 72.2 to 74.2
$0.012

Gemma 4 26B A4B vs Muse Spark 1.3: 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 Spark 1.3

Muse Spark 1.3 is a proprietary multimodal reasoning model from Meta Superintelligence Labs and the fourth Muse Spark release in five months, arriving on September 2, 2026. It takes text, images, video, and document files as input and returns text, and it operates over a context window of 1,048,576 tokens. Meta trains the model for long-horizon agentic work, so it carries accumulated context and prior tool results forward across many turns, reconciles messy or conflicting inputs, and asks for clarification when a task is underspecified. Visual inputs such as screenshots and video clips feed a reasoning loop that runs against a real execution environment rather than a scripted sequence of steps.

The model exposes graded reasoning effort settings. An xhigh configuration is generally available at launch, while a max reasoning configuration aimed at harder reasoning and agentic problems arrives after further safety testing. Artificial Analysis measures Muse Spark 1.3 (max) at 62 on its Intelligence Index and the xhigh configuration at 61, with agentic tool-use evaluations driving most of the gain over Muse Spark 1.2; max reaches 52% on Tau3-Bench Banking by spending more turns and reasoning tokens than xhigh. Prior Muse Spark versions emit bounding box coordinates, transcriptions, and structured field extractions from images on Roboflow Vision Evals.

Frequently Asked Questions

On Roboflow's Vision Evals, Muse Spark 1.3 performed better. It scores higher on all 4 vision tasks and averages 73.9% (#16 of 63) against 54.1% (#50 of 63) 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 Spark 1.3 leads with 74.3% against 43.2%. This is the widest gap between the two models across the benchmark's tasks.

Gemma 4 26B A4B is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0019 per sample against $0.0084. Actual costs depend on your image sizes, prompts, and output length.

Muse Spark 1.3 is faster. Across Roboflow's Vision Evals it averaged 25.5s per inference against 27.8s. 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.