Gemini 3.7 Flash vs Muse Ember
Gemini 3.7 Flash and Muse Ember score 80.1% vs 76.6% averaged across five Vision Evals tasks (#8 and #16 of 67 models), so Gemini 3.7 Flash leads overall.
- Gemini 3.7 Flash wins 4 of the 5 tasks, with its widest lead on object detection (70.5% vs 63.8%).
- Muse Ember is about a quarter of the cost: $0.0009 vs $0.0033 overall average cost per sample.
- Muse Ember is faster: 6.1s vs 11.0s per sample.
Roboflow Vision Evals. Full breakdown below.
Compare Gemini 3.7 Flash 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 3.7 Flash vs Muse Ember on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Gemini 3.7 Flash vs Muse Ember Comparison Table
Evals updated November 10, 2026Pricing updated November 10, 2026
| Property | Gemini 3.7 Flash | Muse Ember |
|---|---|---|
| Organization | Meta | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Aug 2026 | Nov 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | Undisclosed | Unknown |
| License | Proprietary | Proprietary |
| Commercial use | commercial use per vendor terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $0.750 | $0.100 |
| Output $/1M | $3.75 | $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 | 80.1% | 76.6% |
| Avg cost / sample | $0.0033 | $0.0009 |
| Avg speed / sample | 11.04s | 6.08s |
| By task | ||
| Object Detection (low) | 70.5% ±1.1, Mean of 3 runs, range 69.4 to 71.5 | 63.8% ±0.5, Mean of 3 runs, range 63.3 to 64.3 |
| Object Detection (high) | 74.3% ±0.8, Mean of 3 runs, range 73.3 to 75.0 | 64.0% ±0.5, Mean of 3 runs, range 63.5 to 64.5 |
| Counting (low) | 78.4% ±1.4, Mean of 3 runs, range 77.0 to 79.7 | 79.7% ±1.4, Mean of 3 runs, range 78.4 to 81.1 |
| Counting (high) | 79.3% ±2.0, Mean of 3 runs, range 77.0 to 81.1 | 79.3% ±0.7, Mean of 3 runs, range 78.4 to 79.7 |
| Identification (low) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 91.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| Identification (high) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 90.6% ±3.1, Mean of 3 runs, range 87.5 to 93.8 |
| OCR (low) | 73.9% | 69.5% |
| by category |
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|
| OCR (high) | 78.8% | 70.5% |
| by category |
|
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| Reasoning (low) | 80.8% ±2.0, Mean of 3 runs, range 78.8 to 82.8 | 78.2% ±1.3, Mean of 3 runs, range 76.8 to 79.5 |
| Reasoning (high) | 81.9% ±1.3, Mean of 3 runs, range 80.1 to 82.8 | 81.9% ±1.7, Mean of 3 runs, range 80.1 to 83.4 |
Gemini 3.7 Flash vs Muse Ember: Overview
Gemini 3.7 Flash is a proprietary multimodal model from Google, positioned in the Flash branch of the Gemini 3 series that trades some of the capacity of the larger Pro models for lower latency and lower cost per token. It accepts interleaved text and image input alongside other modalities handled by the Gemini family and returns text, and it continues the series pattern of exposing a configurable thinking budget so that reasoning effort can be scaled up for harder problems or reduced for high throughput extraction, routing and classification work. The model is announced roughly three weeks after Gemini 3.6 Flash, part of an unusually fast iteration cadence within the Flash line.
Google reports gains concentrated in agentic coding and front end generation, citing a WebDev Arena Elo of 1588 for this release compared with 1538 for the preceding Flash model, and describes it as producing more functional layouts and more feature complete applications in fewer prompts. Weights are not published and the architecture, parameter count and training corpus are undisclosed, consistent with prior Gemini releases. Visual capability follows the Flash lineage, covering image and document understanding, chart and diagram interpretation, text recognition in images, and general visual question answering.
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 3.7 Flash performed better. It scores higher on 4 of the five vision tasks and averages 80.1% (#8 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 Object Detection benchmark at low effort, Gemini 3.7 Flash leads with 70.5% against 63.8%. 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 $0.0033. Gemini 3.7 Flash is priced at $0.75 per 1M input tokens and $3.75 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 11.0s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.