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Gemini 2.5 Flash-Lite vs Muse Spark 1.3

Compare Gemini 2.5 Flash-Lite and Muse Spark 1.3 side-by-side. See how these vision models stack up in Image Captioning, Object Detection, OCR, Open Prompt, and Classification.

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GoogleGemini 2.5 Flash-Lite
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MetaMuse Spark 1.3
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Models in this comparison

Gemini 2.5 Flash-Lite vs Muse Spark 1.3 Comparison Table

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

PropertyGemini 2.5 Flash-LiteMuse Spark 1.3
OrganizationGoogleMeta
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateJul 2025Sep 2026
Context Window1.0M1.0M
ParametersUnknownUnknown
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$0.100$1.25
Output $/1M$0.400$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
OverallNot evaluated
73.9%
Avg cost / sample–$0.0084
Avg speed / sample–25.50s
By task
Object Detection (low)–
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)–
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)–
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)–
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

Gemini 2.5 Flash-Lite vs Muse Spark 1.3: Overview

Gemini 2.5 Flash-Lite

Gemini 2.5 Flash-Lite, released for general availability on July 22, 2025, is the most cost-efficient model in the Gemini 2.5 family, designed for high-volume and latency-sensitive tasks. It is multimodal, supporting text, images, video, audio, and PDFs as inputs, with text as its primary output. The model handles up to 1 million input tokens and generates outputs up to 64K tokens, making it suitable for large-scale document or media processing at low cost. It is built on a Sparse Mixture-of-Experts architecture with native multimodal support, though exact parameter counts are undisclosed.

Flash-Lite offers the lowest usage cost among Gemini 2.5 models. It introduces developer controls for “thinking mode,” allowing fine-tuning of reasoning depth vs. efficiency. It also integrates native tools such as code execution, search grounding, and URL context. While strong on translation, classification, coding, and general multimodal reasoning, it lacks support for image or audio generation in its stable release and is less capable than Gemini 2.5 Flash or Pro on complex reasoning-heavy workflows.

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

Gemini 2.5 Flash-Lite has not yet been evaluated on Roboflow's current Vision Evals, so this comparison shows specs, licensing, and pricing rather than benchmark scores.

Yes. The comparison demo on this page runs both models on the same image side by side for image captioning and object detection in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.