Gemma 4 31B vs Muse Glimmer 30B
Compare Gemma 4 31B and Muse Glimmer 30B side-by-side. See how these vision models stack up in Image Captioning, OCR, Open Prompt, Object Detection, and Classification.
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Models in this comparison
Gemma 4 31B vs Muse Glimmer 30B on Vision Evals
Muse Glimmer 30B scores higher on 5 of the six Vision Evals tasks.
The widest gap is Counting, where Muse Glimmer 30B leads 66.2% to 51.4%.
Overall, Gemma 4 31B averages 67.0% (#36 of 61) against 70.8% (#29 of 61) for Muse Glimmer 30B.
Muse Glimmer 30B is both cheaper ($0.0011 vs $0.0015 per sample) and faster (8.7s vs 34.4s per sample).
Gemma 4 31B vs Muse Glimmer 30B Comparison Table
Evals updated September 29, 2026Pricing updated September 29, 2026
| Property | Gemma 4 31B | Muse Glimmer 30B |
|---|---|---|
| Organization | Meta | |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Apr 2026 | Aug 2026 |
| Context Window | 256K | 131K |
| Parameters | 31B | 29.6B |
| License | Apache 2.0 | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.090 | $0.300 |
| Output $/1M | $0.340 | $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 | 67.0% | 70.8% |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0015 | $0.0011 |
| Avg speed / sample | 34.36s | 8.70s |
| By task | ||
| Object Detection | 47.5% ±0.6, Mean of 3 runs, range 46.8 to 48.0 | 41.0% |
| Counting | 51.4% ±2.7, Mean of 3 runs, range 48.6 to 54.0 | 66.2% |
| Identification | 79.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 | 81.3% |
| OCR | 90.8% ±0.6, Mean of 3 runs, range 90.2 to 91.5 | 92.1% |
| Data Extraction | 80.4% ±1.0, Mean of 3 runs, range 79.4 to 81.4 | 86.6% |
| Reasoning (low) | 52.8% ±1.3, Mean of 3 runs, range 51.7 to 54.3 | 57.6% |
| Reasoning (high) | – | 62.9% |
Gemma 4 31B vs Muse Glimmer 30B: Overview
Gemma 4 31B is the largest dense model in Google's Gemma 4 family, built from the same research as Gemini 3 and released as open weights under the Apache 2.0 license. It supports a 256K token context window with text and image input, configurable thinking mode for step-by-step reasoning, and multilingual support across 140+ languages. The unquantized model fits on a single 80GB GPU.
For vision tasks, Gemma 4 31B supports image understanding with variable aspect ratios and resolutions, and can output structured bounding boxes for UI element detection, making it useful for document parsing and UI understanding. Compared to Gemma 3, it delivers stronger reasoning and multimodal performance. It is part of a four-size family alongside the 26B A4B MoE variant and two on-device models (E2B, E4B), with the 31B dense variant optimized for output quality and fine-tuning over inference speed.
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, Muse Glimmer 30B performed better. It scores higher on 5 of the six vision tasks and averages 70.8% (#29 of 61) against 67.0% (#36 of 61) for Gemma 4 31B. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Counting benchmark at low effort, Muse Glimmer 30B leads with 66.2% against 51.4%. 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 $0.0015. 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 34.4s. 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 image captioning and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.