Roboflow

Gemini 3.7 Flash vs Muse Spark 1.1

Compare Gemini 3.7 Flash and Muse Spark 1.1 side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, Classification, and OCR.

Compare Gemini 3.7 Flash vs Muse Spark 1.1 live

Run the same image across every model that supports a task and compare their outputs side-by-side.

Compare image classification labels and confidence scores side-by-side.

Open Classification in the full playground
GoogleGemini 3.7 Flash
Run to compare this model.
MetaMuse Spark 1.1
Run to compare this model.

Models in this comparison

Gemini 3.7 Flash vs Muse Spark 1.1 on Vision Evals

Gemini 3.7 Flash scores higher on 5 of the six Vision Evals tasks.

The widest gap is Object Detection, where Gemini 3.7 Flash leads 69.4% to 58.4%.

Overall, Gemini 3.7 Flash averages 84.6% (#2 of 30) against 79.3% (#7 of 30) for Muse Spark 1.1.

Gemini 3.7 Flash is both cheaper ($0.0016 vs $0.0069 per sample) and faster (10.0s vs 11.4s per sample).

Gemini 3.7 FlashMuse Spark 1.1

Gemini 3.7 Flash vs Muse Spark 1.1 Comparison Table

Evals updated August 14, 2026Pricing updated August 15, 2026

PropertyGemini 3.7 FlashMuse Spark 1.1
OrganizationGoogleMeta
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateAug 2026Jul 2026
Context Window1.0M1.0M
ParametersUndisclosed
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.375$1.25
Output $/1M$1.88$4.25
Vision Tasks
CaptioningDemoDemo
Chart Question Answering
ClassificationDemoDemo
Document Question Answering
Image Tagging
Multi-Label Classification
Object DetectionDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort
Overall
84.6%
79.3%
Avg cost / sample$0.0016$0.0069
Avg speed / sample9.97s11.40s
By task
Object Detection
69.4%
$0.0024
58.4%
$0.010
Counting
77.0%
$0.0013
75.7%
$0.0043
Identification
96.9%
$0.0007
87.5%
$0.0032
OCR
86.9%
$0.0014
92.5%
$0.0063
Data Extraction
94.8%
$0.0007
86.6%
$0.0031
Reasoning (low)
82.8%
$0.0011
74.8%
$0.0065
Reasoning (high)
82.1%
$0.0026
76.2%
$0.013

Gemini 3.7 Flash vs Muse Spark 1.1: Overview

Gemini 3.7 Flash

Gemini 3.7 Flash is a proprietary multimodal model from Google, positioned in the Flash branch of the Gemini 3 series that trades some of the capacity of the larger Pro models for lower latency and lower cost per token. It accepts interleaved text and image input alongside other modalities handled by the Gemini family and returns text, and it continues the series pattern of exposing a configurable thinking budget so that reasoning effort can be scaled up for harder problems or reduced for high throughput extraction, routing and classification work. The model is announced roughly three weeks after Gemini 3.6 Flash, part of an unusually fast iteration cadence within the Flash line.

Google reports gains concentrated in agentic coding and front end generation, citing a WebDev Arena Elo of 1588 for this release compared with 1538 for the preceding Flash model, and describes it as producing more functional layouts and more feature complete applications in fewer prompts. Weights are not published and the architecture, parameter count and training corpus are undisclosed, consistent with prior Gemini releases. Visual capability follows the Flash lineage, covering image and document understanding, chart and diagram interpretation, text recognition in images, and general visual question answering.

Muse Spark 1.1

Muse Spark 1.1 is a natively multimodal reasoning model from Meta Superintelligence Labs, released on July 9, 2026, as a significant upgrade to the original Muse Spark. The model accepts text, image, video, PDF, and audio as input and produces text output. It operates with a 1-million-token context window (1,048,576 tokens per the Meta Model API documentation) and is designed specifically for agentic tasks that require planning, tool use, computer use, and multi-agent orchestration. The model runs in a "Thinking" mode, where adjustable reasoning effort is applied before generating a response. It can function both as a main agent gathering context, forming plans, and delegating to parallel subagents and as a subagent that adheres to assigned tasks and escalates when needed. It is trained to decide autonomously when to write automation scripts versus interact directly with a user interface.

Muse Spark 1.1 supports a range of multimodal capabilities including visual perception, image and video captioning, visual-to-code generation, and document analysis. The model was evaluated under Meta's Advanced AI Scaling Framework across frontier risk categories including chemical and biological threats, cybersecurity, and loss-of-control scenarios. Parameter count, architecture details, and training data composition are not publicly disclosed. The model is proprietary and closed-weight, accessible to consumers through the Meta AI app and to developers via the Meta Model API, which launched in public preview alongside this release.

Frequently Asked Questions

On Roboflow's Vision Evals, Gemini 3.7 Flash performed better. It scores higher on 5 of the six vision tasks and averages 84.6% (#2 of 30) against 79.3% (#7 of 30) for Muse Spark 1.1. 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, Gemini 3.7 Flash leads with 69.4% against 58.4%. This is the widest gap between the two models across the benchmark's tasks.

Gemini 3.7 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0016 per sample against $0.0069. Gemini 3.7 Flash is priced at $0.38 per 1M input tokens and $1.88 per 1M output; Muse Spark 1.1 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 3.7 Flash is faster. Across Roboflow's Vision Evals it averaged 10.0s per inference against 11.4s. 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 open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.