Gemma 4 12B vs Muse Spark 1.1
Compare Gemma 4 12B and Muse Spark 1.1 side-by-side.
Compare Gemma 4 12B vs Muse Spark 1.1 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.
Models in this comparison
Gemma 4 12B vs Muse Spark 1.1 Comparison Table
Evals updated September 3, 2026Pricing updated September 3, 2026
| Property | Gemma 4 12B | Muse Spark 1.1 |
|---|---|---|
| Organization | Meta | |
| Category | open | closed |
| Modality | multimodal | multimodal |
| Release Date | Jun 2026 | Jul 2026 |
| Context Window | — | 1.0M |
| Parameters | 12B | |
| License | Apache 2.0 | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $1.25 | |
| Output $/1M | $4.25 | |
| Vision Tasks | ||
| Captioning | Demo | |
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| Chart Question Answering | ||
| Classification | Demo | |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | |
| Model Features | ||
| Multimodal Vision | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | Not evaluated | 80.5% |
| Avg cost / sample | – | $0.0067 |
| Avg speed / sample | – | 7.07s |
| By task | ||
| Object Detection (low) | – | 60.6% ±1.9, Mean of 3 runs, range 58.4 to 62.1 |
| Object Detection (high) | – | 60.0% ±0.5, Mean of 3 runs, range 59.6 to 60.7 |
| Counting (low) | – | 76.6% ±1.3, Mean of 3 runs, range 75.7 to 78.4 |
| Counting (high) | – | 76.1% ±0.7, Mean of 3 runs, range 75.7 to 77.0 |
| Identification (low) | – | 91.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| Identification (high) | – | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| OCR (low) | – | 92.6% ±0.5, Mean of 3 runs, range 92.1 to 93.1 |
| OCR (high) | – | 92.8% ±0.3, Mean of 3 runs, range 92.6 to 93.2 |
| Data Extraction (low) | – | 87.6% ±1.0, Mean of 3 runs, range 86.6 to 88.7 |
| Data Extraction (high) | – | 89.0% ±1.0, Mean of 3 runs, range 87.6 to 89.7 |
| Reasoning (low) | – | 74.2% ±1.7, Mean of 3 runs, range 72.2 to 75.5 |
| Reasoning (high) | – | 76.6% ±1.3, Mean of 3 runs, range 75.5 to 78.2 |
Gemma 4 12B vs Muse Spark 1.1: Overview
Gemma 4 12B is an open-weight multimodal model from Google in the Gemma 4 family. It is intended for text and image understanding tasks such as visual question answering, OCR, captioning, and document understanding, with a smaller parameter footprint than the larger Gemma 4 variants.
This entry is connected to Roboflow Playground vision evals for comparison. No runnable Playground workflow is configured yet, so the model page is used for discovery and benchmark context rather than direct hosted inference.
Muse Spark 1.1 is a natively multimodal reasoning model from Meta Superintelligence Labs, released on July 9, 2026, as a significant upgrade to the original Muse Spark. The model accepts text, image, video, PDF, and audio as input and produces text output. It operates with a 1-million-token context window (1,048,576 tokens per the Meta Model API documentation) and is designed specifically for agentic tasks that require planning, tool use, computer use, and multi-agent orchestration. The model runs in a "Thinking" mode, where adjustable reasoning effort is applied before generating a response. It can function both as a main agent gathering context, forming plans, and delegating to parallel subagents and as a subagent that adheres to assigned tasks and escalates when needed. It is trained to decide autonomously when to write automation scripts versus interact directly with a user interface.
Muse Spark 1.1 supports a range of multimodal capabilities including visual perception, image and video captioning, visual-to-code generation, and document analysis. The model was evaluated under Meta's Advanced AI Scaling Framework across frontier risk categories including chemical and biological threats, cybersecurity, and loss-of-control scenarios. Parameter count, architecture details, and training data composition are not publicly disclosed. The model is proprietary and closed-weight, accessible to consumers through the Meta AI app and to developers via the Meta Model API, which launched in public preview alongside this release.
Frequently Asked Questions
Gemma 4 12B has not yet been evaluated on Roboflow's current Vision Evals, so this comparison shows specs, licensing, and pricing rather than benchmark scores.
Gemma 4 12B is released under Apache 2.0, while Muse Spark 1.1 uses Proprietary. Licensing often matters more than raw accuracy for commercial deployments, so check the terms against how you plan to ship.