Gemma 4 12B vs GPT-5.6 Luna
Compare Gemma 4 12B and GPT-5.6 Luna side-by-side.
Compare Gemma 4 12B vs GPT-5.6 Luna live
Run the same image across every model that supports a task and compare their outputs side-by-side.
These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.
Models in this comparison
Gemma 4 12B vs GPT-5.6 Luna Comparison Table
Evals updated September 3, 2026Pricing updated September 3, 2026
| Property | Gemma 4 12B | GPT-5.6 Luna |
|---|---|---|
| Organization | OpenAI | |
| Category | open | closed |
| Modality | multimodal | multimodal |
| Release Date | Jun 2026 | Jul 2026 |
| Context Window | — | 1.5M |
| Parameters | 12B | |
| License | Apache 2.0 | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.200 | |
| Output $/1M | $1.20 | |
| Vision Tasks | ||
| Captioning | Demo | |
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| Chart Question Answering | ||
| Classification | Demo | |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | |
| Model Features | ||
| Multimodal Vision | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | Not evaluated | 72.7% |
| Avg cost / sample | – | $0.0010 |
| Avg speed / sample | – | 6.55s |
| By task | ||
| Object Detection | – | 59.9% |
| Counting | – | 66.2% |
| Identification | – | 84.4% |
| OCR | – | 88.4% |
| Data Extraction | – | 82.5% |
| Reasoning (low) | – | 55.0% |
| Reasoning (high) | – | 60.9% |
Gemma 4 12B vs GPT-5.6 Luna: Overview
Gemma 4 12B is an open-weight multimodal model from Google in the Gemma 4 family. It is intended for text and image understanding tasks such as visual question answering, OCR, captioning, and document understanding, with a smaller parameter footprint than the larger Gemma 4 variants.
This entry is connected to Roboflow Playground vision evals for comparison. No runnable Playground workflow is configured yet, so the model page is used for discovery and benchmark context rather than direct hosted inference.
GPT-5.6 Luna is the fastest and most cost-efficient model in OpenAI's GPT-5.6 family, which also includes Sol (the flagship tier) and Terra (the balanced mid-tier). Introduced under a new naming convention where the generation number (5.6) and a durable capability tier name (Luna, Terra, Sol) together define each model, Luna occupies the lightweight end of the family and is designed for high-volume, latency-sensitive workloads such as summarization, drafting, autocomplete, classification, and routine automation. The GPT-5.6 family as a whole advances capabilities in software engineering, computer use, professional knowledge work, scientific research, and cybersecurity, with all three tiers rated at the "High" capability level under OpenAI's Preparedness Framework for both cybersecurity and biological/chemical risk domains.
GPT-5.6 Luna supports multimodal input and function calling, and shares the family's 1.5 million token context window. On Terminal-Bench 2.1, Luna scores 82.5%, and on the Artificial Analysis Coding Agent Index it outperforms comparable models at roughly one-quarter the estimated cost of higher-tier alternatives. Luna is priced at $1 per million input tokens and $6 per million output tokens, with cached input reads at $0.10 per million tokens under the GPT-5.6 prompt caching scheme, which introduces explicit cache breakpoints and a 30-minute minimum cache life. The model was previewed on June 26, 2026 to a limited group of trusted partners via the OpenAI API and Codex, with general availability rolling out on July 9, 2026 across ChatGPT, Codex, and the API.
Frequently Asked Questions
Gemma 4 12B has not yet been evaluated on Roboflow's current Vision Evals, so this comparison shows specs, licensing, and pricing rather than benchmark scores.
Gemma 4 12B is released under Apache 2.0, while GPT-5.6 Luna uses Proprietary. Licensing often matters more than raw accuracy for commercial deployments, so check the terms against how you plan to ship.