Muse Glimmer 30B vs Qwen3.7 Plus
Compare Muse Glimmer 30B and Qwen3.7 Plus side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, Classification, Object Detection, and OCR.
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Models in this comparison
Muse Glimmer 30B vs Qwen3.7 Plus on Vision Evals
Muse Glimmer 30B scores higher on 3 of the five Vision Evals tasks.
The widest gap is Object Detection, where Qwen3.7 Plus leads 60.1% to 41.0%.
Overall, Muse Glimmer 30B averages 61.4% (#32 of 61) against 58.9% (#35 of 61) for Qwen3.7 Plus.
Qwen3.7 Plus is both cheaper ($0.0008 vs $0.0013 per sample) and faster (7.8s vs 10.6s per sample).
Muse Glimmer 30B vs Qwen3.7 Plus Comparison Table
Evals updated October 8, 2026Pricing updated October 8, 2026
| Property | Muse Glimmer 30B | Qwen3.7 Plus |
|---|---|---|
| Organization | Meta | Qwen |
| Category | open | closed |
| Modality | multimodal | — |
| Release Date | Aug 2026 | Jun 2026 |
| Context Window | 131K | — |
| Parameters | 29.6B | Unknown |
| License | Apache 2.0 | Unknown |
| Pricing per 1M tokens | ||
| Input $/1M | $0.300 | $0.320 |
| Output $/1M | $1.20 | $1.28 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Classification | Demo | Demo |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Visual Question Answering | Demo | Demo |
| Chart Question Answering | Supported | Not listed |
| Document Question Answering | Supported | Not listed |
| Image Tagging | Supported | Not listed |
| Multi-Label Classification | Supported | Not listed |
| Vision Language | Supported | Not listed |
| Model Features | ||
| Foundation Vision | Supported | Not listed |
| LLMs with Vision Capabilities | Supported | Not listed |
| Multimodal Vision | Supported | Not listed |
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 61.4% | 58.9% |
| Avg cost / sample | $0.0013 | $0.0008 |
| Avg speed / sample | 10.60s | 7.77s |
| By task | ||
| Object Detection | 41.0% | 60.1% |
| Counting | 66.2% | 50.0% |
| Identification | 81.3% | 84.4% |
| OCR (low) | 60.7% | 60.3% |
| by category |
|
|
| OCR (high) | 60.1% | 65.5% |
| by category |
|
|
| Reasoning (low) | 57.6% | 39.7% |
| Reasoning (high) | 62.9% | 68.2% |
Muse Glimmer 30B vs Qwen3.7 Plus: Overview
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 3 of the five vision tasks and averages 61.4% (#32 of 61) against 58.9% (#35 of 61) for Qwen3.7 Plus. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Object Detection benchmark at low effort, Qwen3.7 Plus leads with 60.1% against 41.0%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3.7 Plus is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0008 per sample against $0.0013. Muse Glimmer 30B is priced at $0.30 per 1M input tokens and $1.20 per 1M output; Qwen3.7 Plus is priced at $0.32 per 1M input tokens and $1.28 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
Qwen3.7 Plus is faster. Across Roboflow's Vision Evals it averaged 7.8s per inference against 10.6s. 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 image captioning and open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.