Gemini 3.1 Pro vs Muse Spark 1.2
Gemini 3.1 Pro and Muse Spark 1.2 score 76.8% vs 73.8% averaged across five Vision Evals tasks (#14 and #18 of 65 models), so Gemini 3.1 Pro leads overall.
- Gemini 3.1 Pro wins 3 of the 5 tasks, with its widest lead on object detection (67.4% vs 59.0%).
- Muse Spark 1.2 is cheaper: $0.0083 vs $0.010 overall average cost per sample.
- Gemini 3.1 Pro is faster: 8.4s vs 11.5s per sample.
Roboflow Vision Evals. Full breakdown below.
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Gemini 3.1 Pro vs Muse Spark 1.2 on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Gemini 3.1 Pro vs Muse Spark 1.2 Comparison Table
Evals updated October 26, 2026Pricing updated November 4, 2026
| Property | Gemini 3.1 Pro | Muse Spark 1.2 |
|---|---|---|
| Organization | Meta | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Feb 2026 | Aug 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 | ||
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 76.8% | 73.8% |
| Avg cost / sample | $0.010 | $0.0083 |
| Avg speed / sample | 8.44s | 11.51s |
| By task | ||
| Object Detection (low) | 67.4% | 59.0% ±1.0, Mean of 3 runs, range 58.1 to 60.2 |
| Object Detection (high) | – | 60.5% ±0.3, Mean of 3 runs, range 60.2 to 60.7 |
| Counting (low) | 71.6% | 76.6% ±2.7, Mean of 3 runs, range 74.3 to 79.7 |
| Counting (high) | – | 75.2% ±2.0, Mean of 3 runs, range 73.0 to 77.0 |
| Identification (low) | 100.0% | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| Identification (high) | – | 87.5% ±0.0, Mean of 3 runs, range 87.5 to 87.5 |
| OCR (low) | 72.6% | 68.7% |
| by category |
|
|
| OCR (high) | 73.1% | 68.6% |
| by category |
|
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| Reasoning (low) | 72.2% | 75.1% ±0.3, Mean of 3 runs, range 74.8 to 75.5 |
| Reasoning (high) | 74.8% | 75.7% ±0.3, Mean of 3 runs, range 75.5 to 76.2 |
Gemini 3.1 Pro vs Muse Spark 1.2: 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 1.2 is a proprietary multimodal reasoning model from Meta Superintelligence Labs, released as a coding-focused update to Muse Spark 1.1. It accepts text, images, video, audio, and PDF documents and returns text, with a context window of roughly one million tokens that allows whole repositories, long documents, and extended agent trajectories to be held in a single request. The model thinks before answering, and the amount of reasoning effort it spends is configurable per request. Alongside its visual and document understanding, it supports structured output and parallel function calling, and it is designed to operate either as a planning agent that delegates work or as a subagent executing tasks in parallel.
Training for version 1.2 scaled up compute on coding tasks and widened the diversity of training environments, concentrating on long-horizon work such as whole-repository generation, large end-to-end projects, and automated research. Part of the training data was self-generated, with Muse Spark 1.1 producing coding environments and instruction-following templates and grading candidate solutions against them. The model was co-trained with the Muse Code terminal agent, incorporating rejection-sampled harness trajectories and that toolset. Multimodal use cases documented for the family include visual-to-code generation and detailed image and video captioning.