Gemini 3.1 Pro vs Muse Spark Open
Gemini 3.1 Pro and Muse Spark Open score 76.8% vs 69.4% averaged across five Vision Evals tasks (#14 and #25 of 65 models), so Gemini 3.1 Pro leads overall.
- Gemini 3.1 Pro wins 4 of the 5 tasks, with its widest lead on object detection (67.4% vs 57.8%).
- Muse Spark Open is about half the cost: $0.0059 vs $0.010 overall average cost per sample.
- Muse Spark Open is faster: 7.4s vs 8.4s per sample.
Roboflow Vision Evals. Full breakdown below.
Compare Gemini 3.1 Pro 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 3.1 Pro vs Muse Spark Open on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Gemini 3.1 Pro vs Muse Spark Open Comparison Table
Evals updated October 26, 2026Pricing updated October 27, 2026
| Property | Gemini 3.1 Pro | Muse Spark Open |
|---|---|---|
| Organization | Meta | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Feb 2026 | Oct 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | Unknown | 816B total / 42B active |
| License | Proprietary | Custom |
| Commercial use | commercial use per vendor terms | See license terms |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $1.25 |
| Output $/1M | $12.00 | $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 | 76.8% | 69.4% |
| Avg cost / sample | $0.010 | $0.0059 |
| Avg speed / sample | 8.44s | 7.37s |
| By task | ||
| Object Detection (low) | 67.4% | 57.8% ±0.6, Mean of 3 runs, range 57.0 to 58.2 |
| Object Detection (high) | – | 60.9% ±0.8, Mean of 3 runs, range 60.3 to 61.9 |
| Counting (low) | 71.6% | 75.7% ±2.7, Mean of 3 runs, range 73.0 to 78.4 |
| Counting (high) | – | 75.2% ±4.1, Mean of 3 runs, range 71.6 to 79.7 |
| Identification (low) | 100.0% | 82.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 |
| Identification (high) | – | 80.2% ±3.1, Mean of 3 runs, range 78.1 to 84.4 |
| OCR (low) | 72.6% | 67.6% |
| by category |
|
|
| OCR (high) | 73.1% | 68.6% |
| by category |
|
|
| Reasoning (low) | 72.2% | 63.8% ±1.0, Mean of 3 runs, range 62.9 to 64.9 |
| Reasoning (high) | 74.8% | 65.1% ±3.0, Mean of 3 runs, range 62.3 to 68.2 |
Gemini 3.1 Pro vs Muse Spark Open: Overview
Gemini 3.1 Pro is a proprietary multimodal model from Google’s Gemini 3 series, released in early 2026 and designed for advanced reasoning across large multimodal datasets. It accepts text, images, audio, video, and documents, supporting up to a 1-million-token input context with up to 64k output tokens. Compared with Gemini 3 Pro, it improves long-context synthesis and multi-step reasoning, enabling more reliable analysis of large documents, datasets, and software codebases.
The model also advances visual understanding and grounding, allowing it to interpret UI screenshots, diagrams, and real-world scenes while referencing specific regions within images or video. These capabilities make Gemini 3.1 Pro well suited for multimodal workflows involving document processing, interface analysis, robotics research, and complex visual reasoning.
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 3.1 Pro performed better. It scores higher on 4 of the five vision tasks and averages 76.8% (#14 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 Identification benchmark at low effort, Gemini 3.1 Pro leads with 100.0% against 82.3%. This is the widest gap between the two models across the benchmark's tasks.
Muse Spark Open is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0059 per sample against $0.010. Gemini 3.1 Pro is priced at $2.00 per 1M input tokens and $12.00 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 8.4s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.