Roboflow

Gemini 2.5 Flash-Lite vs Muse Spark Open

Muse Spark Open: 69.4% average across five Vision Evals tasks (#25 of 65 models). We have not run Gemini 2.5 Flash-Lite through Roboflow's Vision Evals, so there is no head-to-head score.

Compare Gemini 2.5 Flash-Lite vs Muse Spark Open 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.

Gemini 2.5 Flash-Lite vs Muse Spark Open Comparison Table

Evals updated October 26, 2026Pricing updated October 27, 2026

PropertyGemini 2.5 Flash-LiteMuse Spark Open
OrganizationGoogleMeta
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateJul 2025Oct 2026
Context Window1.0M1.0M
ParametersUnknown816B total / 42B active
LicenseProprietaryCustom
Commercial usecommercial use per vendor termsSee license terms
Pricing per 1M tokens
Input $/1M$0.100$1.25
Output $/1M$0.400$4.25
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
OverallNot evaluated
69.4%
Avg cost / sample–$0.0059
Avg speed / sample–7.37s
By task
Object Detection (low)–
57.8%
±0.6, Mean of 3 runs, range 57.0 to 58.2
$0.0073
Object Detection (high)–
60.9%
±0.8, Mean of 3 runs, range 60.3 to 61.9
$0.013
Counting (low)–
75.7%
±2.7, Mean of 3 runs, range 73.0 to 78.4
$0.0036
Counting (high)–
75.2%
±4.1, Mean of 3 runs, range 71.6 to 79.7
$0.0073
Identification (low)–
82.3%
±1.6, Mean of 3 runs, range 81.3 to 84.4
$0.0027
Identification (high)–
80.2%
±3.1, Mean of 3 runs, range 78.1 to 84.4
$0.0056
OCR (low)–
67.6%
$0.0061
by category
Single value
60.0%
Transcription
89.0%
Structured JSON
85.5%
Text localization
29.5%
OCR (high)–
68.6%
$0.014
by category
Single value
58.3%
Transcription
91.4%
Structured JSON
88.2%
Text localization
32.2%
Reasoning (low)–
63.8%
±1.0, Mean of 3 runs, range 62.9 to 64.9
$0.0049
Reasoning (high)–
65.1%
±3.0, Mean of 3 runs, range 62.3 to 68.2
$0.012

Gemini 2.5 Flash-Lite vs Muse Spark Open: 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 Open

Muse Spark Open is an open-weight multimodal model released by Meta on October 15, 2026. Its sparse mixture-of-experts architecture has 816 billion total parameters and activates 42 billion per token. It accepts text, images, audio and video, produces text, and supports a context window of 1,048,576 tokens. Meta provides BF16 and NVFP4 checkpoints for self-hosting, alongside access through hosted inference partners.

The model is designed for coding, tool use and multi-step reasoning over long tasks. Its visual inputs support workflows involving screenshots, documents and other image content. Vision Evals measures its text understanding, counting, identification, reasoning and object detection at Low and High effort. The published weights use the custom Muse Model License, with commercial use subject to its terms and the accompanying usage policy.