Gemini 3 Flash vs Muse Glimmer 30B
Compare Gemini 3 Flash and Muse Glimmer 30B side-by-side. See how these vision models stack up in Object Detection, Classification, Open Prompt, OCR, and Image Captioning.
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Models in this comparison
Gemini 3 Flash vs Muse Glimmer 30B on Vision Evals
Gemini 3 Flash scores higher on 4 of the six Vision Evals tasks.
The widest gap is Identification, where Gemini 3 Flash leads 93.8% to 81.3%.
Overall, Gemini 3 Flash averages 74.9% (#11 of 30) against 70.8% (#15 of 30) for Muse Glimmer 30B.
Muse Glimmer 30B is cheaper ($0.0013 vs $0.0021 per sample), while Gemini 3 Flash is faster (4.1s vs 8.7s per sample).
Gemini 3 Flash vs Muse Glimmer 30B Comparison Table
Evals updated August 14, 2026Pricing updated August 15, 2026
| Property | Gemini 3 Flash | Muse Glimmer 30B |
|---|---|---|
| Organization | Meta | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Dec 2025 | Aug 2026 |
| Context Window | 1.0M | 131K |
| Parameters | 29.6B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.500 | $0.350 |
| Output $/1M | $3.00 | $1.50 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | Demo |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 74.9% | 70.8% |
| Avg cost / sample | $0.0021 | $0.0013 |
| Avg speed / sample | 4.10s | 8.70s |
| By task | ||
| Object Detection | 38.6% $0.0031 | 41.0% $0.0020 |
| Counting | 67.6% $0.0012 | 66.2% $0.0008 |
| Identification | 93.8% $0.0009 | 81.3% $0.0006 |
| OCR | 87.6% $0.0024 | 92.1% $0.0012 |
| Data Extraction | 96.9% $0.0008 | 86.6% $0.0007 |
| Reasoning (low) | 64.9% $0.0020 | 57.6% $0.0010 |
| Reasoning (high) | 74.2% $0.0040 | 62.9% $0.0033 |
Gemini 3 Flash vs Muse Glimmer 30B: Overview
Gemini 3 Flash is a proprietary multimodal large language model developed by Google through Google DeepMind, designed to deliver fast, cost-efficient reasoning across real-time products and developer workflows. Released in December 2025, it is the Flash-tier variant of the Gemini 3 family, balancing low latency with reasoning quality approaching Pro models.
The model supports text, images, audio, and video, with an exceptionally large context window of roughly one million input tokens and outputs up to ~65k tokens. It emphasizes rapid responses for coding, summarization, analysis, and agentic tasks, and exposes configurable “thinking levels” via API to trade speed for deeper reasoning. Today, Gemini 3 Flash positions itself as a high-throughput, production-ready model, serving as the default in the Gemini app and Google Search’s AI Mode, optimized for scalable, interactive AI applications.
Muse Glimmer 30B is a dense vision language model from Meta built for long-horizon agentic work on local hardware. The architecture pairs a 52-layer causal text decoder with a roughly 1.8B parameter ViT-G/14 perception encoder for about 29.6 billion parameters in total, and it accepts interleaved text and image input so an agent can interpret screenshots, charts, and documents alongside conversation. The decoder uses grouped-query attention with 32 query heads and 2 key-value heads, a repeating pattern of three sliding-window local attention layers followed by one global layer, SwiGLU feed-forward blocks, and rotary position embeddings applied on the local layers, supporting a trained context of 131,072 tokens.
Meta describes the model as distilled from the larger Muse Spark and trained and evaluated around agentic behavior: end-to-end task completion, schema-accurate tool calling, multi-step reasoning across extended workflows, and recovery when a tool call returns an unexpected result. Reasoning effort is selectable across low, medium, high, and xhigh settings, and the model emits channel-scoped reasoning traces together with XML style tool calls rather than JSON, which requires parsers specific to this family. A companion block-diffusion drafter head predicts blocks of 16 tokens per forward pass for speculative decoding, with the main model verifying the proposals in parallel.
Frequently Asked Questions
On Roboflow's Vision Evals, Gemini 3 Flash performed better. It scores higher on 4 of the six vision tasks and averages 74.9% (#11 of 30) against 70.8% (#15 of 30) for Muse Glimmer 30B. 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, Gemini 3 Flash leads with 93.8% against 81.3%. This is the widest gap between the two models across the benchmark's tasks.
Muse Glimmer 30B is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0013 per sample against $0.0021. Gemini 3 Flash is priced at $0.50 per 1M input tokens and $3.00 per 1M output; Muse Glimmer 30B is priced at $0.35 per 1M input tokens and $1.50 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
Gemini 3 Flash is faster. Across Roboflow's Vision Evals it averaged 4.1s per inference against 8.7s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.
Yes. The comparison demo on this page runs both models on the same image side by side for object detection and image classification in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.