Gemini 3.1 Pro vs Muse Ember
Gemini 3.1 Pro and Muse Ember score 76.8% vs 76.6% averaged across five Vision Evals tasks (#15 and #16 of 67 models), so they are effectively tied.
- Gemini 3.1 Pro wins 3 of the 5 tasks, but the widest gap is counting, where Muse Ember leads 79.7% vs 71.6%.
- Muse Ember is under a tenth of the cost: $0.0009 vs $0.010 overall average cost per sample.
- Muse Ember is faster: 6.1s vs 8.4s per sample.
Roboflow Vision Evals. Full breakdown below.
Compare Gemini 3.1 Pro vs Muse Ember 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 Ember on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Gemini 3.1 Pro vs Muse Ember Comparison Table
Evals updated November 10, 2026Pricing updated November 10, 2026
| Property | Gemini 3.1 Pro | Muse Ember |
|---|---|---|
| Organization | Meta | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Feb 2026 | Nov 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 | $0.100 |
| Output $/1M | $12.00 | $0.500 |
| 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% | 76.6% |
| Avg cost / sample | $0.010 | $0.0009 |
| Avg speed / sample | 8.44s | 6.08s |
| By task | ||
| Object Detection (low) | 67.4% | 63.8% ±0.5, Mean of 3 runs, range 63.3 to 64.3 |
| Object Detection (high) | – | 64.0% ±0.5, Mean of 3 runs, range 63.5 to 64.5 |
| Counting (low) | 71.6% | 79.7% ±1.4, Mean of 3 runs, range 78.4 to 81.1 |
| Counting (high) | – | 79.3% ±0.7, Mean of 3 runs, range 78.4 to 79.7 |
| Identification (low) | 100.0% | 91.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| Identification (high) | – | 90.6% ±3.1, Mean of 3 runs, range 87.5 to 93.8 |
| OCR (low) | 72.6% | 69.5% |
| by category |
|
|
| OCR (high) | 73.1% | 70.5% |
| by category |
|
|
| Reasoning (low) | 72.2% | 78.2% ±1.3, Mean of 3 runs, range 76.8 to 79.5 |
| Reasoning (high) | 74.8% | 81.9% ±1.7, Mean of 3 runs, range 80.1 to 83.4 |
Gemini 3.1 Pro vs Muse Ember: 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 Ember is a proprietary multimodal reasoning model from Meta, released on November 9, 2026. It accepts text, images, video, audio, and PDF documents and returns text. The Meta Model API serves it as muse-ember-1.0, with a 1,048,576-token context window and up to 524,288 output tokens. Reasoning effort is adjustable from minimal to max.
Meta positions Ember for repeated tasks within larger agent workflows: tool calls, routing, coding subtasks, and processing collections of images and documents. It can caption or sort photos and extract fields from records while a larger Muse model handles planning. Meta recommends Spark or Wonder for harder single-shot coding and knowledge-heavy problems. Ember is available as a hosted model; its weights are not publicly released.
Frequently Asked Questions
On Roboflow's Vision Evals, Gemini 3.1 Pro performed better. It scores higher on 3 of the five vision tasks and averages 76.8% (#15 of 67) against 76.6% (#16 of 67) for Muse Ember. 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 91.7%. This is the widest gap between the two models across the benchmark's tasks.
Muse Ember is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0009 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 Ember is priced at $0.10 per 1M input tokens and $0.50 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
Muse Ember is faster. Across Roboflow's Vision Evals it averaged 6.1s per inference against 8.4s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.