Gemini 3.1 Pro vs Muse Spark 1.1
Compare Gemini 3.1 Pro and Muse Spark 1.1 side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, Classification, OCR, and Object Detection.
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Models in this comparison
Gemini 3.1 Pro vs Muse Spark 1.1 on Vision Evals
Gemini 3.1 Pro scores higher on 4 of the six Vision Evals tasks.
The widest gap is Identification, where Gemini 3.1 Pro leads 100.0% to 87.5%.
Overall, Gemini 3.1 Pro averages 83.1% (#3 of 25) against 79.2% (#6 of 25) for Muse Spark 1.1.
Muse Spark 1.1 is cheaper ($0.0069 vs $0.0093 per sample), while Gemini 3.1 Pro is faster (7.8s vs 11.4s per sample).
Gemini 3.1 Pro vs Muse Spark 1.1 Comparison Table
Evals updated August 6, 2026Pricing updated August 11, 2026
| Property | Gemini 3.1 Pro | Muse Spark 1.1 |
|---|---|---|
| Organization | Meta | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Feb 2026 | Jul 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $1.25 |
| Output $/1M | $12.00 | $4.25 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | Demo |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 83.1% | 79.2% |
| Avg cost / sample | $0.0093 | $0.0069 |
| Avg speed / sample | 7.81s | 11.40s |
| By task | ||
| Object Detection | 67.4% $0.010 | 58.2% $0.010 |
| Counting | 71.6% $0.0071 | 75.7% $0.0043 |
| Identification | 100.0% $0.0070 | 87.5% $0.0032 |
| OCR | 92.6% $0.0066 | 92.5% $0.0063 |
| Data Extraction | 94.8% $0.0063 | 86.6% $0.0031 |
| Reasoning (low) | 72.2% $0.012 | 74.8% $0.0065 |
| Reasoning (high) | 74.8% $0.021 | 76.2% $0.013 |
Gemini 3.1 Pro vs Muse Spark 1.1: 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 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 3.1 Pro performed better. It scores higher on 4 of the six vision tasks and averages 83.1% (#3 of 25) against 79.2% (#6 of 25) 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 Identification benchmark, Gemini 3.1 Pro leads with 100.0% against 87.5%. This is the widest gap between the two models across the benchmark's tasks.
Muse Spark 1.1 is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0069 per sample against $0.0093. Gemini 3.1 Pro is priced at $2.00 per 1M input tokens and $12.00 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 3.1 Pro is faster. Across Roboflow's Vision Evals it averaged 7.8s 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.