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 3 of the five Vision Evals tasks.
The widest gap is Identification, where Gemini 3.1 Pro leads 100.0% to 91.7%.
Overall, Gemini 3.1 Pro averages 76.8% (#13 of 63) against 74.4% (#15 of 63) for Muse Spark 1.1.
Muse Spark 1.1 is both cheaper ($0.0075 vs $0.010 per sample) and faster (7.2s vs 8.4s per sample).
Gemini 3.1 Pro vs Muse Spark 1.1 Comparison Table
Evals updated October 13, 2026Pricing updated October 18, 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 | Unknown | Unknown |
| License | Proprietary | Proprietary |
| Commercial use | commercial use per vendor terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $1.25 |
| Output $/1M | $12.00 | $4.25 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | Supported | Supported |
| Classification | Demo | Demo |
| Document Question Answering | Supported | Supported |
| Image Tagging | Supported | Supported |
| Multi-Label Classification | Supported | Supported |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | Supported | Supported |
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | Supported | Supported |
| LLMs with Vision Capabilities | Supported | Supported |
| Multimodal Vision | Supported | Supported |
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 76.8% | 74.4% |
| Avg cost / sample | $0.010 | $0.0075 |
| Avg speed / sample | 8.44s | 7.15s |
| By task | ||
| Object Detection (low) | 67.4% | 60.6% ±1.9, Mean of 3 runs, range 58.4 to 62.1 |
| Object Detection (high) | – | 60.0% ±0.5, Mean of 3 runs, range 59.6 to 60.7 |
| Counting (low) | 71.6% | 76.6% ±1.3, Mean of 3 runs, range 75.7 to 78.4 |
| Counting (high) | – | 76.1% ±0.7, Mean of 3 runs, range 75.7 to 77.0 |
| Identification (low) | 100.0% | 91.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| Identification (high) | – | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| OCR (low) | 72.6% | 68.8% |
| by category |
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| OCR (high) | 73.1% | 67.5% |
| by category |
|
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| Reasoning (low) | 72.2% | 74.2% ±1.7, Mean of 3 runs, range 72.2 to 75.5 |
| Reasoning (high) | 74.8% | 76.6% ±1.3, Mean of 3 runs, range 75.5 to 78.2 |
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.