Roboflow

Gemini 2.5 Flash-Lite vs Muse Ember

Muse Ember: 76.6% average across five Vision Evals tasks (#16 of 67 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 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.

Gemini 2.5 Flash-Lite vs Muse Ember Comparison Table

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

PropertyGemini 2.5 Flash-LiteMuse Ember
OrganizationGoogleMeta
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateJul 2025Nov 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$0.100
Output $/1M$0.400$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
OverallNot evaluated
76.6%
Avg cost / sample–$0.0009
Avg speed / sample–6.08s
By task
Object Detection (low)–
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)–
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)–
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)–
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

Gemini 2.5 Flash-Lite vs Muse Ember: 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 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.