Gemini 3.1 Flash-Lite vs GPT-6 Luna
GPT-6 Luna: 69.7% average across five Vision Evals tasks (#26 of 69 models). We have not run Gemini 3.1 Flash-Lite through Roboflow's Vision Evals, so there is no head-to-head score. Run both on your own image below.
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Gemini 3.1 Flash-Lite vs GPT-6 Luna Comparison Table
Evals updated November 12, 2026Pricing updated November 13, 2026
| Property | Gemini 3.1 Flash-Lite | GPT-6 Luna |
|---|---|---|
| Organization | OpenAI | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Mar 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 | $0.250 | $0.100 |
| Output $/1M | $1.50 | $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 | Not evaluated | 69.7% |
| Avg cost / sample | – | $0.0004 |
| Avg speed / sample | – | 10.21s |
| By task | ||
| Object Detection (low) | – | 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% ±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) | – | 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) | – | 59.8% |
| by category |
| |
| OCR (high) | – | 62.5% |
| by category |
| |
| Reasoning (low) | – | 64.2% ±3.0, Mean of 3 runs, range 60.3 to 66.2 |
| Reasoning (high) | – | 71.1% ±2.3, Mean of 3 runs, range 68.2 to 72.8 |
Gemini 3.1 Flash-Lite vs GPT-6 Luna: Overview
Gemini 3.1 Flash-Lite is a natively multimodal reasoning model from Google DeepMind in the Gemini 3 series, based on the Gemini 3 Pro architecture. It processes text, image, video, audio, and PDF inputs within a 1 million token context window and produces text output up to 64K tokens. The model targets high-volume, latency-sensitive workloads and supports visual question answering, image and document data extraction, content moderation, classification, translation, automated speech recognition, and agentic data pipelines. It exposes configurable thinking levels of minimal, low, medium, and high, which set the depth of internal reasoning applied per request and let developers balance response quality against cost and latency.
According to Artificial Analysis benchmarks, it produces a 2.5 times faster time to first answer token and a 45% increase in output speed relative to Gemini 2.5 Flash. It also shows improved instruction following, higher audio input quality for automated speech recognition tasks, and support for structured JSON output used in data extraction pipelines.
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.