Gemini 3.7 Flash vs Muse Spark Open
Gemini 3.7 Flash and Muse Spark Open score 80.1% vs 69.4% averaged across five Vision Evals tasks (#7 and #25 of 65 models), so Gemini 3.7 Flash leads overall.
- Gemini 3.7 Flash wins all 5 tasks, with its widest lead on reasoning (80.8% vs 63.8%).
- Gemini 3.7 Flash is about half the cost: $0.0033 vs $0.0059 overall average cost per sample.
- Muse Spark Open is faster: 7.4s vs 11.0s per sample.
Roboflow Vision Evals. Full breakdown below.
Compare Gemini 3.7 Flash 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 3.7 Flash vs Muse Spark Open on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Gemini 3.7 Flash vs Muse Spark Open Comparison Table
Evals updated October 26, 2026Pricing updated October 27, 2026
| Property | Gemini 3.7 Flash | Muse Spark Open |
|---|---|---|
| Organization | Meta | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Aug 2026 | Oct 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | Undisclosed | 816B total / 42B active |
| License | Proprietary | Custom |
| Commercial use | commercial use per vendor terms | See license terms |
| Pricing per 1M tokens | ||
| Input $/1M | $0.750 | $1.25 |
| Output $/1M | $3.75 | $4.25 |
| Vision Tasks | ||
| Captioning | Demo | Supported |
| Classification | Demo | Supported |
| Object Detection | Demo | Supported |
| OCR | Demo | Supported |
| Visual Question Answering | Demo | Supported |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 80.1% | 69.4% |
| Avg cost / sample | $0.0033 | $0.0059 |
| Avg speed / sample | 11.04s | 7.37s |
| By task | ||
| Object Detection (low) | 70.5% ±1.1, Mean of 3 runs, range 69.4 to 71.5 | 57.8% ±0.6, Mean of 3 runs, range 57.0 to 58.2 |
| Object Detection (high) | 74.3% ±0.8, Mean of 3 runs, range 73.3 to 75.0 | 60.9% ±0.8, Mean of 3 runs, range 60.3 to 61.9 |
| Counting (low) | 78.4% ±1.4, Mean of 3 runs, range 77.0 to 79.7 | 75.7% ±2.7, Mean of 3 runs, range 73.0 to 78.4 |
| Counting (high) | 79.3% ±2.0, Mean of 3 runs, range 77.0 to 81.1 | 75.2% ±4.1, Mean of 3 runs, range 71.6 to 79.7 |
| Identification (low) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 82.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 |
| Identification (high) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 80.2% ±3.1, Mean of 3 runs, range 78.1 to 84.4 |
| OCR (low) | 73.9% | 67.6% |
| by category |
|
|
| OCR (high) | 78.8% | 68.6% |
| by category |
|
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| Reasoning (low) | 80.8% ±2.0, Mean of 3 runs, range 78.8 to 82.8 | 63.8% ±1.0, Mean of 3 runs, range 62.9 to 64.9 |
| Reasoning (high) | 81.9% ±1.3, Mean of 3 runs, range 80.1 to 82.8 | 65.1% ±3.0, Mean of 3 runs, range 62.3 to 68.2 |
Gemini 3.7 Flash vs Muse Spark Open: Overview
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 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.
Frequently Asked Questions
On Roboflow's Vision Evals, Gemini 3.7 Flash performed better. It scores higher on all five vision tasks and averages 80.1% (#7 of 65) against 69.4% (#25 of 65) for Muse Spark Open. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Reasoning benchmark at low effort, Gemini 3.7 Flash leads with 80.8% against 63.8%. 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 $0.0033 per sample against $0.0059. Gemini 3.7 Flash is priced at $0.75 per 1M input tokens and $3.75 per 1M output; Muse Spark Open 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.
Muse Spark Open is faster. Across Roboflow's Vision Evals it averaged 7.4s per inference against 11.0s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.