Gemma 4 31B vs GPT-6 Luna
Compare Gemma 4 31B and GPT-6 Luna side-by-side.
Compare Gemma 4 31B vs GPT-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 31B vs GPT-6 Luna on Vision Evals
GPT-6 Luna scores higher on 4 of the six Vision Evals tasks.
The widest gap is Counting, where GPT-6 Luna leads 65.8% to 51.4%.
Overall, Gemma 4 31B averages 67.0% (#34 of 57) against 68.6% (#32 of 57) for GPT-6 Luna.
GPT-6 Luna is both cheaper ($0.0004 vs $0.0012 per sample) and faster (11.3s vs 28.8s per sample).
Gemma 4 31B vs GPT-6 Luna Comparison Table
Evals updated September 22, 2026Pricing updated September 22, 2026
| Property | Gemma 4 31B | GPT-6 Luna |
|---|---|---|
| Organization | OpenAI | |
| Category | open | closed |
| Modality | multimodal | multimodal |
| Release Date | Apr 2026 | Sep 2026 |
| Context Window | 256K | 1.1M |
| Parameters | 31B | |
| License | Apache 2.0 | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.090 | |
| Output $/1M | $0.340 | |
| Vision Tasks | ||
| Captioning | Demo | |
| Chart Question Answering | ||
| Classification | Demo | |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | |
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | 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% | 68.6% |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0012 | $0.0004 |
| Avg speed / sample | 28.79s | 11.27s |
| By task | ||
| Object Detection (low) | 48.2% ±0.2, Mean of 3 runs, range 48.0 to 48.4 | 56.8% ±1.9, Mean of 3 runs, range 54.8 to 58.5 |
| Object Detection (high) | – | 64.1% ±0.5, Mean of 3 runs, range 63.6 to 64.5 |
| Counting (low) | 51.4% ±1.4, Mean of 3 runs, range 50.0 to 52.7 | 65.8% ±1.4, Mean of 3 runs, range 64.9 to 67.6 |
| Counting (high) | – | 64.4% ±2.0, Mean of 3 runs, range 62.2 to 66.2 |
| Identification (low) | 80.2% ±3.1, Mean of 3 runs, range 78.1 to 84.4 | 81.3% ±0.0, Mean of 3 runs, range 81.3 to 81.3 |
| Identification (high) | – | 80.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 |
| OCR (low) | 90.8% ±0.2, Mean of 3 runs, range 90.6 to 90.9 | 87.9% ±0.6, Mean of 3 runs, range 87.2 to 88.3 |
| OCR (high) | – | 88.5% ±0.6, Mean of 3 runs, range 87.9 to 89.2 |
| Data Extraction (low) | 80.4% ±2.6, Mean of 3 runs, range 77.3 to 82.5 | 68.0% ±3.1, Mean of 3 runs, range 65.0 to 71.1 |
| Data Extraction (high) | – | 66.7% ±0.5, Mean of 3 runs, range 66.0 to 67.0 |
| Reasoning (low) | 50.8% ±1.7, Mean of 3 runs, range 49.0 to 52.3 | 52.1% ±2.0, Mean of 3 runs, range 49.7 to 53.6 |
| Reasoning (high) | – | 60.7% ±1.7, Mean of 3 runs, range 58.9 to 62.3 |
Gemma 4 31B vs GPT-6 Luna: 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.
GPT-6 Luna is the fast, cost-efficient tier of OpenAI's GPT-6 model family, sitting below GPT-6 Sol and the larger GPT-6 Astra model that opened the generation. It is a proprietary multimodal transformer that accepts text and image input and returns text, and it exposes an adjustable reasoning effort setting so the same model can run in a low-latency mode or spend additional inference compute on harder problems. OpenAI positions it for high-volume and latency-sensitive workloads such as conversational assistants, classification, and lightweight agentic pipelines, while noting that at higher reasoning effort it handles software engineering and computer-use tasks that previously required a Sol-tier model.
The model supports a context window of roughly 1,050,000 input tokens with a maximum output of 128,000 tokens, which allows long documents, extended agent traces, and large code repositories to be processed in a single request. OpenAI describes the GPT-6 generation as improving factual reliability and adopting a more concise communication style relative to the GPT-5.6 series, and attributes the efficiency of the Sol and Luna tiers to gains in caching and inference rather than to reduced capability. Architecture details, parameter counts, and training data are not published.
Frequently Asked Questions
On Roboflow's Vision Evals, GPT-6 Luna performed better. It scores higher on 4 of the six vision tasks and averages 68.6% (#32 of 57) against 67.0% (#34 of 57) 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, GPT-6 Luna leads with 65.8% against 51.4%. This is the widest gap between the two models across the benchmark's tasks.
GPT-6 Luna is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0004 per sample against $0.0012. Actual costs depend on your image sizes, prompts, and output length.
GPT-6 Luna is faster. Across Roboflow's Vision Evals it averaged 11.3s per inference against 28.8s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.