Gemini 3.8 Flash vs Muse Glimmer 30B
Compare Gemini 3.8 Flash and Muse Glimmer 30B side-by-side. See how these vision models stack up in Object Detection, Image Captioning, OCR, Classification, and Open Prompt.
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Models in this comparison
Gemini 3.8 Flash vs Muse Glimmer 30B on Vision Evals
Gemini 3.8 Flash scores higher on 5 of the six Vision Evals tasks.
The widest gap is Object Detection, where Gemini 3.8 Flash leads 68.1% to 41.0%.
Overall, Gemini 3.8 Flash averages 85.1% (#3 of 36) against 70.8% (#17 of 36) for Muse Glimmer 30B.
Muse Glimmer 30B is both cheaper ($0.0011 vs $0.0033 per sample) and faster (8.7s vs 11.6s per sample).
Gemini 3.8 Flash vs Muse Glimmer 30B Comparison Table
Evals updated September 2, 2026Pricing updated September 2, 2026
| Property | Gemini 3.8 Flash | Muse Glimmer 30B |
|---|---|---|
| Organization | Meta | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Aug 2026 |
| Context Window | 1.0M | 131K |
| Parameters | 29.6B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.300 | |
| Output $/1M | $1.20 | |
| 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 | 85.1% | 70.8% |
| Avg cost / sample | $0.0033 | $0.0011 |
| Avg speed / sample | 11.65s | 8.70s |
| By task | ||
| Object Detection (low) | 68.1% ±0.8, Mean of 3 runs, range 67.3 to 69.0 | 41.0% |
| Object Detection (high) | 74.8% ±1.1, Mean of 3 runs, range 73.4 to 75.6 | – |
| Counting (low) | 78.8% ±2.7, Mean of 3 runs, range 75.7 to 81.1 | 66.2% |
| Counting (high) | 79.3% ±1.4, Mean of 3 runs, range 78.4 to 81.1 | – |
| Identification (low) | 97.9% ±1.6, Mean of 3 runs, range 96.9 to 100.0 | 81.3% |
| Identification (high) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | – |
| OCR (low) | 87.3% ±0.8, Mean of 3 runs, range 86.5 to 88.2 | 92.1% |
| OCR (high) | 88.8% ±0.7, Mean of 3 runs, range 88.0 to 89.4 | – |
| Data Extraction (low) | 97.3% ±0.5, Mean of 3 runs, range 96.9 to 97.9 | 86.6% |
| Data Extraction (high) | 94.8% ±1.0, Mean of 3 runs, range 93.8 to 95.9 | – |
| Reasoning (low) | 81.2% ±0.3, Mean of 3 runs, range 80.8 to 81.5 | 57.6% |
| Reasoning (high) | 84.5% ±1.0, Mean of 3 runs, range 83.4 to 85.4 | 62.9% |
Gemini 3.8 Flash vs Muse Glimmer 30B: Overview
Gemini 3.8 Flash is a natively multimodal reasoning model in Google's Gemini 3 series, positioned as the speed and cost oriented Flash tier while targeting long-horizon software engineering, autonomous agents, and enterprise workflows. It accepts text, images, video, audio, and PDF documents in a single request and returns text, with an input limit of 1,048,576 tokens and an output limit of 65,536 tokens. Thinking is configurable at low, medium, and high levels, and the model supports function calling, code execution, structured outputs, context caching, search and Maps grounding, file search, and computer use in preview. Image generation, audio generation, and the Live API are not supported.
On vision oriented evaluations the model reports 86.2% on CharXiv Reasoning for chart and figure synthesis and 87.8% on LVBench for long video understanding in agentic mode, alongside 90.8% on Terminal-Bench 2.1 and 61.6% on SWE-Bench Pro for coding. Following Gemini API conventions, it can localize 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 in addition to captioning, document parsing, and visual question answering. The knowledge cutoff is March 2026, though coverage in some domains reflects the January 2025 cutoff shared across the Gemini 3 family.
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 3.8 Flash performed better. It scores higher on 5 of the six vision tasks and averages 85.1% (#3 of 36) against 70.8% (#17 of 36) 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 Object Detection benchmark at low effort, Gemini 3.8 Flash leads with 68.1% against 41.0%. 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 $0.0011 per sample against $0.0033. Actual costs depend on your image sizes, prompts, and output length.
Muse Glimmer 30B is faster. Across Roboflow's Vision Evals it averaged 8.7s per inference against 11.6s. 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 captioning in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.