Gemini 3.1 Pro vs GPT-6 Luna
Gemini 3.1 Pro and GPT-6 Luna score 76.8% vs 69.7% averaged across five Vision Evals tasks (#14 and #22 of 65 models), so Gemini 3.1 Pro leads overall.
- Gemini 3.1 Pro wins 4 of the 5 tasks, with its widest lead on OCR (72.6% vs 59.8%).
- GPT-6 Luna is under a tenth of the cost: $0.0004 vs $0.010 overall average cost per sample.
- Gemini 3.1 Pro is faster: 8.4s vs 10.2s per sample.
Roboflow Vision Evals. Full breakdown below.
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Gemini 3.1 Pro vs GPT-6 Luna on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Gemini 3.1 Pro vs GPT-6 Luna Comparison Table
Evals updated October 26, 2026Pricing updated November 7, 2026
| Property | Gemini 3.1 Pro | GPT-6 Luna |
|---|---|---|
| Organization | OpenAI | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Feb 2026 | Sep 2026 |
| Context Window | 1.0M | 1.1M |
| Parameters | Unknown | Unknown |
| License | Proprietary | Proprietary |
| Commercial use | commercial use per vendor terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $0.100 |
| Output $/1M | $12.00 | $0.500 |
| Vision Tasks | ||
| Promptable Concept Segmentation | Not listed | Demo |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 76.8% | 69.7% |
| Avg cost / sample | $0.010 | $0.0004 |
| Avg speed / sample | 8.44s | 10.21s |
| By task | ||
| Object Detection (low) | 67.4% | 65.5% ±0.4, Mean of 3 runs, range 65.2 to 66.0 |
| Object Detection (high) | – | 68.0% ±0.7, Mean of 3 runs, range 67.3 to 68.6 |
| Counting (low) | 71.6% | 71.6% ±0.0, Mean of 3 runs, range 71.6 to 71.6 |
| Counting (high) | – | 72.1% ±0.7, Mean of 3 runs, range 71.6 to 73.0 |
| Identification (low) | 100.0% | 87.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 |
| Identification (high) | – | 87.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 |
| OCR (low) | 72.6% | 59.8% |
| by category |
|
|
| OCR (high) | 73.1% | 62.5% |
| by category |
|
|
| Reasoning (low) | 72.2% | 64.2% ±3.0, Mean of 3 runs, range 60.3 to 66.2 |
| Reasoning (high) | 74.8% | 71.1% ±2.3, Mean of 3 runs, range 68.2 to 72.8 |
Gemini 3.1 Pro vs GPT-6 Luna: Overview
Gemini 3.1 Pro is a proprietary multimodal model from Google’s Gemini 3 series, released in early 2026 and designed for advanced reasoning across large multimodal datasets. It accepts text, images, audio, video, and documents, supporting up to a 1-million-token input context with up to 64k output tokens. Compared with Gemini 3 Pro, it improves long-context synthesis and multi-step reasoning, enabling more reliable analysis of large documents, datasets, and software codebases.
The model also advances visual understanding and grounding, allowing it to interpret UI screenshots, diagrams, and real-world scenes while referencing specific regions within images or video. These capabilities make Gemini 3.1 Pro well suited for multimodal workflows involving document processing, interface analysis, robotics research, and complex visual reasoning.
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, Gemini 3.1 Pro performed better. It scores higher on 4 of the five vision tasks and averages 76.8% (#14 of 65) against 69.7% (#22 of 65) for GPT-6 Luna. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals OCR benchmark at low effort, Gemini 3.1 Pro leads with 72.6% against 59.8%. 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 $0.010. Gemini 3.1 Pro is priced at $2.00 per 1M input tokens and $12.00 per 1M output; GPT-6 Luna is priced at $0.10 per 1M input tokens and $0.50 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
Gemini 3.1 Pro is faster. Across Roboflow's Vision Evals it averaged 8.4s per inference against 10.2s. 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.