Gemini 4 Argon vs Muse Spark Open
Gemini 4 Argon and Muse Spark Open score 87.5% vs 69.4% averaged across five Vision Evals tasks (#1 and #25 of 65 models), so Gemini 4 Argon leads overall.
- Gemini 4 Argon wins all 5 tasks, with its widest lead on reasoning (89.4% vs 63.8%).
- Muse Spark Open is faster: 7.4s vs 13.4s per sample.
Roboflow Vision Evals. Full breakdown below.
Compare Gemini 4 Argon 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 4 Argon vs Muse Spark Open on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Gemini 4 Argon vs Muse Spark Open Comparison Table
Evals updated October 26, 2026Pricing updated October 27, 2026
| Property | Gemini 4 Argon | Muse Spark Open |
|---|---|---|
| Organization | Meta | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Oct 2026 | Oct 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | Undisclosed | 816B total / 42B active |
| License | Proprietary | Custom |
| Commercial use | commercial use per vendor terms | See license terms |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $1.25 |
| Output $/1M | $10.00 | $4.25 |
| Vision Tasks | ||
| Captioning | Demo | Supported |
| Classification | Demo | Supported |
| Object Detection | Demo | Supported |
| OCR | Demo | Supported |
| Visual Question Answering | Demo | Supported |
| Promptable Concept Segmentation | Demo | Not listed |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 87.5% | 69.4% |
| Avg cost / sample | $0.0058 | $0.0059 |
| Avg speed / sample | 13.40s | 7.37s |
| By task | ||
| Object Detection (low) | 82.5% ±0.5, Mean of 3 runs, range 81.9 to 83.0 | 57.8% ±0.6, Mean of 3 runs, range 57.0 to 58.2 |
| Object Detection (high) | 85.1% ±0.6, Mean of 3 runs, range 84.3 to 85.5 | 60.9% ±0.8, Mean of 3 runs, range 60.3 to 61.9 |
| Counting (low) | 86.0% ±0.7, Mean of 3 runs, range 85.1 to 86.5 | 75.7% ±2.7, Mean of 3 runs, range 73.0 to 78.4 |
| Counting (high) | 90.5% ±1.4, Mean of 3 runs, range 89.2 to 91.9 | 75.2% ±4.1, Mean of 3 runs, range 71.6 to 79.7 |
| Identification (low) | 93.8% ±0.0, Mean of 3 runs, range 93.8 to 93.8 | 82.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 |
| Identification (high) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 80.2% ±3.1, Mean of 3 runs, range 78.1 to 84.4 |
| OCR (low) | 85.7% | 67.6% |
| by category |
|
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| OCR (high) | 88.0% | 68.6% |
| by category |
|
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| Reasoning (low) | 89.4% ±1.3, Mean of 3 runs, range 88.1 to 90.7 | 63.8% ±1.0, Mean of 3 runs, range 62.9 to 64.9 |
| Reasoning (high) | 91.4% ±0.7, Mean of 3 runs, range 90.7 to 92.0 | 65.1% ±3.0, Mean of 3 runs, range 62.3 to 68.2 |
Gemini 4 Argon vs Muse Spark Open: Overview
Gemini 4 Argon is Google DeepMind's frontier Gemini 4 model, built to sustain extended reasoning across long-horizon workflows in software engineering, enterprise knowledge work such as legal and finance, scientific analysis, and defensive cybersecurity. It accepts text, image, video, and document input and returns text, operating over a one million token context window. The model raises the maximum output length to one million tokens, compared with a 64K ceiling on earlier Gemini releases, so a single reasoning trajectory can run for hundreds of thousands of tokens before terminating.
On the visual side, Argon targets multimodal document comprehension, professional chart analysis, and retrieval of specific details from long recordings, reporting 91.7% on LVBench for long-video understanding and 71.6% on Chartography for chart reasoning. Google also reports 77.9% on DeepSWE v1.1, 51.3% on AutomationBench, 68% on CWE-bench v1, and first place on the Vals Index, which aggregates finance, coding, legal, and tax tasks. Following Gemini API conventions, it localizes objects by emitting bounding boxes as [ymin, xmin, ymax, xmax] integers normalized to a 0 to 1000 range, which supports prompt driven detection and grounding alongside captioning, document parsing, and visual question answering. Google describes adversarial training and automated red teaming for indirect prompt injection resistance, plus chain-of-thought and action monitors that halt execution when behavior drifts outside user intent.
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 4 Argon performed better. It scores higher on all five vision tasks and averages 87.5% (#1 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 Reasoning benchmark at low effort, Gemini 4 Argon leads with 89.4% against 63.8%. This is the widest gap between the two models across the benchmark's tasks.
Gemini 4 Argon is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0058 per sample against $0.0059. Gemini 4 Argon is priced at $2.00 per 1M input tokens and $10.00 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 13.4s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.