GPT-5.6 Sol vs Muse Glimmer 30B
Compare GPT-5.6 Sol and Muse Glimmer 30B side-by-side. See how these vision models stack up in OCR, Image Captioning, Object Detection, Open Prompt, and Classification.
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Models in this comparison
GPT-5.6 Sol vs Muse Glimmer 30B on Vision Evals
GPT-5.6 Sol scores higher on 4 of the six Vision Evals tasks.
The widest gap is Object Detection, where GPT-5.6 Sol leads 68.4% to 41.0%.
Overall, GPT-5.6 Sol averages 79.0% (#16 of 61) against 70.8% (#29 of 61) for Muse Glimmer 30B.
Muse Glimmer 30B is both cheaper ($0.0011 vs $0.0088 per sample) and faster (8.7s vs 10.3s per sample).
GPT-5.6 Sol vs Muse Glimmer 30B Comparison Table
Evals updated September 29, 2026Pricing updated September 29, 2026
| Property | GPT-5.6 Sol | Muse Glimmer 30B |
|---|---|---|
| Organization | OpenAI | Meta |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Jul 2026 | Aug 2026 |
| Context Window | 1.5M | 131K |
| Parameters | 29.6B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $0.300 |
| Output $/1M | $10.00 | $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 | 79.0% | 70.8% |
| Avg cost / sample | $0.0088 | $0.0011 |
| Avg speed / sample | 10.32s | 8.70s |
| By task | ||
| Object Detection (low) | 68.4% ±0.7, Mean of 3 runs, range 67.9 to 69.3 | 41.0% |
| Object Detection (high) | 68.4% ±0.8, Mean of 3 runs, range 67.7 to 69.3 | – |
| Counting (low) | 74.3% ±1.4, Mean of 3 runs, range 73.0 to 75.7 | 66.2% |
| Counting (high) | 76.1% ±2.0, Mean of 3 runs, range 74.3 to 78.4 | – |
| Identification (low) | 89.6% ±4.7, Mean of 3 runs, range 84.4 to 93.8 | 81.3% |
| Identification (high) | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 | – |
| OCR (low) | 90.7% ±0.1, Mean of 3 runs, range 90.6 to 90.7 | 92.1% |
| OCR (high) | 90.2% ±0.2, Mean of 3 runs, range 90.0 to 90.4 | – |
| Data Extraction (low) | 84.9% ±1.0, Mean of 3 runs, range 83.5 to 85.6 | 86.6% |
| Data Extraction (high) | 86.9% ±0.5, Mean of 3 runs, range 86.6 to 87.6 | – |
| Reasoning (low) | 66.0% ±2.6, Mean of 3 runs, range 63.6 to 68.9 | 57.6% |
| Reasoning (high) | 71.7% ±1.3, Mean of 3 runs, range 70.2 to 72.8 | 62.9% |
GPT-5.6 Sol vs Muse Glimmer 30B: Overview
GPT-5.6 Sol is the flagship model in OpenAI's GPT-5.6 family, which also includes Terra (a balanced everyday-work tier) and Luna (a fast, cost-efficient tier). Sol is designed for demanding reasoning, long-horizon agentic workflows, software engineering, computer use, scientific research, and cybersecurity tasks. It introduces two new capability modes: a "max" reasoning effort setting that allocates additional compute time for difficult problems, and an "ultra" mode that coordinates multiple subagents in parallel to accelerate complex, multi-step work. The model supports native multimodal input, allowing it to process screenshots, diagrams, charts, documents, and photographs alongside text. A reported context window of approximately 1.5 million tokens enables processing of large codebases, lengthy research documents, and extended agentic sessions.
GPT-5.6 Sol was announced on June 26, 2026, initially in a limited preview for trusted partners, and reached general availability on July 9, 2026. On the Agents' Last Exam benchmark, which evaluates long-running professional workflows across 55 fields, Sol scores 53.6. On Terminal-Bench 2.1, which tests command-line agentic coding workflows, Sol Ultra achieves 91.9%. The model also demonstrates gains in life sciences evaluations, including long-horizon genomics and quantitative biology analyses. OpenAI paired the release with its most extensive safety evaluation to date, combining human red teaming with large-scale automated testing, and classified Sol as High capability in both cybersecurity and biological risk under its Preparedness Framework, though it does not cross the Critical threshold in either category.
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, GPT-5.6 Sol performed better. It scores higher on 4 of the six vision tasks and averages 79.0% (#16 of 61) against 70.8% (#29 of 61) 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, GPT-5.6 Sol leads with 68.4% 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 $0.0088. GPT-5.6 Sol is priced at $2.00 per 1M input tokens and $10.00 per 1M output; Muse Glimmer 30B is priced at $0.30 per 1M input tokens and $1.20 per 1M output. 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 10.3s. 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 OCR and image captioning in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.