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Gemini 3.8 Flash vs Muse Spark 1.3

Compare Gemini 3.8 Flash and Muse Spark 1.3 side-by-side. See how these vision models stack up in Object Detection, Image Captioning, OCR, Classification, and Open Prompt.

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GoogleGemini 3.8 Flash
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MetaMuse Spark 1.3
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Models in this comparison

Gemini 3.8 Flash vs Muse Spark 1.3 on Vision Evals

Gemini 3.8 Flash scores higher on all five Vision Evals tasks.

The widest gap is Object Detection, where Gemini 3.8 Flash leads 68.1% to 58.6%.

Overall, Gemini 3.8 Flash averages 79.8% (#7 of 63) against 73.9% (#16 of 63) for Muse Spark 1.3.

Gemini 3.8 Flash is both cheaper ($0.0045 vs $0.0084 per sample) and faster (12.8s vs 25.5s per sample).

Gemini 3.8 FlashMuse Spark 1.3

Gemini 3.8 Flash vs Muse Spark 1.3 Comparison Table

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

PropertyGemini 3.8 FlashMuse Spark 1.3
OrganizationGoogleMeta
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateSep 2026Sep 2026
Context Window1.0M1.0M
ParametersUnknownUnknown
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$0.825$1.25
Output $/1M$4.13$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
79.8%
73.9%
Avg cost / sample$0.0045$0.0084
Avg speed / sample12.76s25.50s
By task
Object Detection (low)
68.1%
±0.8, Mean of 3 runs, range 67.3 to 69.0
$0.0045
58.6%
±0.7, Mean of 3 runs, range 58.0 to 59.4
$0.011
Object Detection (high)
74.8%
±1.1, Mean of 3 runs, range 73.4 to 75.6
$0.023
56.6%
±2.4, Mean of 3 runs, range 54.5 to 59.3
$0.017
Counting (low)
78.8%
±2.7, Mean of 3 runs, range 75.7 to 81.1
$0.0040
74.3%
±2.0, Mean of 3 runs, range 73.0 to 77.0
$0.0049
Counting (high)
79.3%
±1.4, Mean of 3 runs, range 78.4 to 81.1
$0.027
75.7%
±3.4, Mean of 3 runs, range 73.0 to 79.7
$0.0094
Identification (low)
97.9%
±1.6, Mean of 3 runs, range 96.9 to 100.0
$0.0016
92.7%
±1.6, Mean of 3 runs, range 90.6 to 93.8
$0.0036
Identification (high)
96.9%
±0.0, Mean of 3 runs, range 96.9 to 96.9
$0.0037
86.5%
±1.6, Mean of 3 runs, range 84.4 to 87.5
$0.0063
OCR (low)
72.8%
$0.0049
70.4%
$0.0086
by category
Single value
73.5%
Transcription
91.3%
Structured JSON
86.1%
Text localization
26.4%
Single value
62.6%
Transcription
91.8%
Structured JSON
88.0%
Text localization
33.3%
OCR (high)
78.7%
$0.039
69.8%
$0.015
by category
Single value
70.9%
Transcription
94.1%
Structured JSON
90.9%
Text localization
58.6%
Single value
62.2%
Transcription
86.8%
Structured JSON
87.2%
Text localization
35.2%
Reasoning (low)
81.2%
±0.3, Mean of 3 runs, range 80.8 to 81.5
$0.0038
73.3%
±1.0, Mean of 3 runs, range 72.2 to 74.2
$0.0064
Reasoning (high)
84.5%
±1.0, Mean of 3 runs, range 83.4 to 85.4
$0.023
73.1%
±1.0, Mean of 3 runs, range 72.2 to 74.2
$0.012

Gemini 3.8 Flash vs Muse Spark 1.3: Overview

Gemini 3.8 Flash

Gemini 3.8 Flash is a natively multimodal reasoning model in Google's Gemini 3 series, positioned as the speed and cost oriented Flash tier while targeting long-horizon software engineering, autonomous agents, and enterprise workflows. It accepts text, images, video, audio, and PDF documents in a single request and returns text, with an input limit of 1,048,576 tokens and an output limit of 65,536 tokens. Thinking is configurable at low, medium, and high levels, and the model supports function calling, code execution, structured outputs, context caching, search and Maps grounding, file search, and computer use in preview. Image generation, audio generation, and the Live API are not supported.

On vision oriented evaluations the model reports 86.2% on CharXiv Reasoning for chart and figure synthesis and 87.8% on LVBench for long video understanding in agentic mode, alongside 90.8% on Terminal-Bench 2.1 and 61.6% on SWE-Bench Pro for coding. Following Gemini API conventions, it can localize objects by emitting bounding boxes as [ymin, xmin, ymax, xmax] integers normalized to a 0 to 1000 range, which supports prompt driven detection and grounding in addition to captioning, document parsing, and visual question answering. The knowledge cutoff is March 2026, though coverage in some domains reflects the January 2025 cutoff shared across the Gemini 3 family.

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, Gemini 3.8 Flash performed better. It scores higher on all five vision tasks and averages 79.8% (#7 of 63) against 73.9% (#16 of 63) for Muse Spark 1.3. 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, Gemini 3.8 Flash leads with 68.1% against 58.6%. This is the widest gap between the two models across the benchmark's tasks.

Gemini 3.8 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0045 per sample against $0.0084. Gemini 3.8 Flash is priced at $0.83 per 1M input tokens and $4.13 per 1M output; Muse Spark 1.3 is priced at $1.25 per 1M input tokens and $4.25 per 1M output. Actual costs depend on your image sizes, prompts, and output length.

Gemini 3.8 Flash is faster. Across Roboflow's Vision Evals it averaged 12.8s per inference against 25.5s. 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 object detection and image captioning in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.