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Gemini 3.1 Pro vs GPT-5.6 Luna

Compare Gemini 3.1 Pro and GPT-5.6 Luna side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, Classification, OCR, and Object Detection.

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GoogleGemini 3.1 Pro
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OpenAIGPT-5.6 Luna
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Gemini 3.1 Pro vs GPT-5.6 Luna: Overview

Gemini 3.1 Pro

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-5.6 Luna

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.

Gemini 3.1 Pro vs GPT-5.6 Luna Comparison Table

PropertyGemini 3.1 ProGPT-5.6 Luna
OrganizationGoogleOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateFeb 2026Jul 2026
Context Window1.0M1.5M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$2.00$1.00
Output $/1M$12.00$6.00
Vision Tasks
CaptioningDemoDemo
ClassificationDemoDemo
Object DetectionDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Document Question Answering
Model Features
LLMs with Vision Capabilities
Multimodal Vision
Foundation Vision
Vision Evalspass/fail results · 66 prompts
Score key:≥75%40–74%<40%
Visual Understanding
Overall Score
75.76%
Avg Response Time6.13s
Median input tokensincl. image tokens1.1K
Median output tokens11
Est. cost / taskon this benchmark$0.0024
Defect Detection
73.3%(11/15)
Document Understanding
88.9%(8/9)
Object Counting
44.4%(4/9)
Object Understanding
92.9%(13/14)
Spatial Understanding
73.7%(14/19)
OCR
Overall Score
89.52%
Avg Response Time3.11s
Median input tokensincl. image tokens1.1K
Median output tokens12
Est. cost / taskon this benchmark$0.0024
Focused Scene OCR
94.9%(94/99)
Handwritten Math
90%(9/10)
License Plate Recognition
90%(27/30)
Text Recognition
86.7%(26/30)
VQA & Extraction
81.7%(49/60)

Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology