Gemini 2.5 Flash vs GPT-6 Luna
Compare Gemini 2.5 Flash and GPT-6 Luna side-by-side.
Compare Gemini 2.5 Flash 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
Gemini 2.5 Flash vs GPT-6 Luna Comparison Table
Evals updated September 22, 2026Pricing updated September 22, 2026
| Property | Gemini 2.5 Flash | GPT-6 Luna |
|---|---|---|
| Organization | OpenAI | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jul 2025 | Sep 2026 |
| Context Window | 1.0M | 1.1M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.300 | $0.100 |
| Output $/1M | $2.50 | $0.500 |
| 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 | Not evaluated | 68.6% |
| Avg cost / sample | – | $0.0004 |
| Avg speed / sample | – | 11.27s |
| By task | ||
| Object Detection (low) | – | 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) | – | 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) | – | 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) | – | 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) | – | 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) | – | 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 |
Gemini 2.5 Flash vs GPT-6 Luna: Overview
Gemini 2.5 Flash, released on June 17, 2025, is Google DeepMind’s production-ready, efficiency-focused model in the Gemini 2.5 family. It is multimodal, accepting text, images, video, and audio as inputs, with text as the primary output format. The model supports 1 million input tokens and up to 65K output tokens, enabling it to process very large contexts such as books, long video transcripts, or extensive datasets. Its training knowledge extends to January 2025.
Designed as a price-performance leader, Gemini 2.5 Flash balances speed and reasoning power, making it suitable for everyday enterprise and developer use cases without the higher latency and cost of Pro models. It supports advanced workflows like function calling, code execution, search grounding, URL context ingestion, and structured outputs. While efficient and scalable, output length is still limited compared to its input capacity, and multimodal outputs (e.g. image or audio generation) remain restricted to specialized or preview variants.
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.