Gemma 3 12B vs Muse Spark 1.1
Compare Gemma 3 12B and Muse Spark 1.1 side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.
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Gemma 3 12B vs Muse Spark 1.1 Comparison Table
Evals updated September 3, 2026Pricing updated September 3, 2026
| Property | Gemma 3 12B | Muse Spark 1.1 |
|---|---|---|
| Organization | Meta | |
| Category | open | closed |
| Modality | multimodal | multimodal |
| Release Date | Mar 2025 | Jul 2026 |
| Context Window | 128K | 1.0M |
| Parameters | 12B | |
| License | Custom | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.050 | $1.25 |
| Output $/1M | $0.150 | $4.25 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Object Detection | Demo | |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
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 3 12B vs Muse Spark 1.1: Overview
Gemma 3 12B, announced by Google DeepMind on March 12, 2025, is part of the open-weight Gemma 3 family, designed to provide a balance between capability and accessibility. With around 12 billion parameters, it supports multimodal input (text + images) and outputs text, making it useful for reasoning, summarization, Q&A, and visual understanding tasks. The model supports an input context of 128,000 tokens and typically generates up to ~8,000 tokens in output.
The 12B variant is instruction-tuned (“Gemma-3-12B-IT”) and optimized for multilingual use across more than 140 languages. It can run on a single GPU or TPU, offering a lighter compute footprint than very large proprietary models, while still achieving strong performance in reasoning benchmarks. Quantized and lower-precision variants are available to improve efficiency. Limitations include smaller output lengths relative to input capacity, scaling hardware needs at larger sizes, and performance below massive proprietary models on the most complex multimodal or reasoning-heavy tasks.
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 3 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 3 12B is released under Custom, 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.
Yes. The comparison demo on this page runs both models on the same image side by side for image captioning and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.