Gemini 3.6 Flash vs Muse Spark Open
Gemini 3.6 Flash and Muse Spark Open score 77.1% vs 69.4% averaged across five Vision Evals tasks (#12 and #25 of 65 models), so Gemini 3.6 Flash leads overall.
- Gemini 3.6 Flash wins 4 of the 5 tasks, with its widest lead on reasoning (77.7% vs 63.8%).
- Gemini 3.6 Flash is about half the cost: $0.0033 vs $0.0059 overall average cost per sample.
- Muse Spark Open is faster: 7.4s vs 10.4s per sample.
Roboflow Vision Evals. Full breakdown below.
Compare Gemini 3.6 Flash vs Muse Spark Open 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 3.6 Flash vs Muse Spark Open on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Gemini 3.6 Flash vs Muse Spark Open Comparison Table
Evals updated October 26, 2026Pricing updated October 27, 2026
| Property | Gemini 3.6 Flash | Muse Spark Open |
|---|---|---|
| Organization | Meta | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Jul 2026 | Oct 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | Unknown | 816B total / 42B active |
| License | Proprietary | Custom |
| Commercial use | commercial use per vendor terms | See license terms |
| Pricing per 1M tokens | ||
| Input $/1M | $0.750 | $1.25 |
| Output $/1M | $3.75 | $4.25 |
| Vision Tasks | ||
| Captioning | Demo | Supported |
| Classification | Demo | Supported |
| Object Detection | Demo | Supported |
| OCR | Demo | Supported |
| Visual Question Answering | Demo | Supported |
| Video Classification | Supported | Not listed |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 77.1% | 69.4% |
| Avg cost / sample | $0.0033 | $0.0059 |
| Avg speed / sample | 10.43s | 7.37s |
| By task | ||
| Object Detection (low) | 57.1% ±1.7, Mean of 3 runs, range 55.9 to 59.4 | 57.8% ±0.6, Mean of 3 runs, range 57.0 to 58.2 |
| Object Detection (high) | 70.7% ±0.4, Mean of 3 runs, range 70.3 to 71.2 | 60.9% ±0.8, Mean of 3 runs, range 60.3 to 61.9 |
| Counting (low) | 80.2% ±2.0, Mean of 3 runs, range 78.4 to 82.4 | 75.7% ±2.7, Mean of 3 runs, range 73.0 to 78.4 |
| Counting (high) | 79.3% ±2.7, Mean of 3 runs, range 77.0 to 82.4 | 75.2% ±4.1, Mean of 3 runs, range 71.6 to 79.7 |
| Identification (low) | 99.0% ±1.6, Mean of 3 runs, range 96.9 to 100.0 | 82.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 |
| Identification (high) | 100.0% ±0.0, Mean of 3 runs, range 100.0 to 100.0 | 80.2% ±3.1, Mean of 3 runs, range 78.1 to 84.4 |
| OCR (low) | 71.8% | 67.6% |
| by category |
|
|
| OCR (high) | 77.4% | 68.6% |
| by category |
|
|
| Reasoning (low) | 77.7% ±2.0, Mean of 3 runs, range 76.2 to 80.1 | 63.8% ±1.0, Mean of 3 runs, range 62.9 to 64.9 |
| Reasoning (high) | 81.0% ±2.0, Mean of 3 runs, range 79.5 to 83.4 | 65.1% ±3.0, Mean of 3 runs, range 62.3 to 68.2 |
Gemini 3.6 Flash vs Muse Spark Open: Overview
Gemini 3.6 Flash is a multimodal language model from Google DeepMind, positioned as the workhorse tier in the Gemini 3.x family. It accepts text, image, video, audio, and PDF inputs with a 1 million token context window and produces up to 64,000 output tokens. The model builds directly on Gemini 3.5 Flash, incorporating developer and customer feedback to improve token efficiency, coding quality, and knowledge work performance. According to the Artificial Analysis Index, it consumes 17% fewer output tokens than its predecessor, and on some benchmarks such as DeepSWE, token reduction reaches up to 65%. It supports function calling, structured output, search as a tool, and code execution, and includes computer use as a built-in capability in the Gemini API and Gemini Enterprise.
On coding benchmarks, Gemini 3.6 Flash scores 49% on DeepSWE versus 37% for 3.5 Flash, and 63.9% on MLE Bench versus 49.7%. The model carries a knowledge cutoff of March 2026 and ships with enhanced Frontier Safety safeguards covering chemical, biological, radiological, nuclear, and cyber offense domains, with training to minimize refusals for beneficial uses. It is a proprietary, closed-weights model available in preview through the Gemini API via Google AI Studio, Android Studio, Google Antigravity, Gemini Enterprise, and the Gemini app.
Muse Spark Open is an open-weight multimodal model released by Meta on October 15, 2026. Its sparse mixture-of-experts architecture has 816 billion total parameters and activates 42 billion per token. It accepts text, images, audio and video, produces text, and supports a context window of 1,048,576 tokens. Meta provides BF16 and NVFP4 checkpoints for self-hosting, alongside access through hosted inference partners.
The model is designed for coding, tool use and multi-step reasoning over long tasks. Its visual inputs support workflows involving screenshots, documents and other image content. Vision Evals measures its text understanding, counting, identification, reasoning and object detection at Low and High effort. The published weights use the custom Muse Model License, with commercial use subject to its terms and the accompanying usage policy.
Frequently Asked Questions
On Roboflow's Vision Evals, Gemini 3.6 Flash performed better. It scores higher on 4 of the five vision tasks and averages 77.1% (#12 of 65) against 69.4% (#25 of 65) for Muse Spark Open. 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 3.6 Flash leads with 99.0% against 82.3%. This is the widest gap between the two models across the benchmark's tasks.
Gemini 3.6 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0033 per sample against $0.0059. Gemini 3.6 Flash is priced at $0.75 per 1M input tokens and $3.75 per 1M output; Muse Spark Open 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.
Muse Spark Open is faster. Across Roboflow's Vision Evals it averaged 7.4s per inference against 10.4s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.