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GPT-5.4 vs GPT-5.6 Luna

Compare GPT-5.4 and GPT-5.6 Luna side-by-side. See how these vision models stack up in OCR, Image Captioning, Classification, Object Detection, and Open Prompt.

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OpenAIGPT-5.4
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OpenAIGPT-5.6 Luna
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Models in this comparison

OpenAI

GPT-5.4 vs GPT-5.6 Luna: Overview

GPT-5.4

GPT-5.4 is a proprietary multimodal large language model developed by OpenAI and released on March 5, 2026. It is designed for professional workloads such as advanced software development, research, and agentic automation. The model combines the general reasoning capabilities of the GPT-5 series with software engineering improvements derived from GPT-5.3-Codex. In the API and Codex environments it supports context windows of up to 1 million tokens, enabling long-context reasoning and large-scale code or document workflows.

Compared with GPT-5.2, GPT-5.4 reduces false individual claims by 33% and lowers overall response errors by 18%, improving factual reliability across complex tasks. It is also the first general-purpose OpenAI release with native computer-use capabilities, allowing agents to interact with desktops, browsers, and external applications to complete multi-step workflows. The model family includes three variants: GPT-5.4 (standard), GPT-5.4 Pro for higher-performance workloads, and GPT-5.4 Thinking, a reasoning-oriented version in ChatGPT that presents an upfront plan before generating its response. The API also introduces a Tool Search system that allows models to retrieve tool definitions dynamically, reducing token usage in tool-heavy integrations.

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.

GPT-5.4 vs GPT-5.6 Luna Comparison Table

PropertyGPT-5.4GPT-5.6 Luna
OrganizationOpenAIOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateMar 2026Jul 2026
Context Window1.1M1.5M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$2.50$1.00
Output $/1M$15.00$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 · 67 prompts
Score key:≥75%40–74%<40%
Visual Understanding
Overall Score
77.61%
Avg Response Time7.16s
Median input tokensincl. image tokens1.4K
Median output tokens108
Est. cost / taskon this benchmark$0.0052
Defect Detection
86.7%(13/15)
Document Understanding
88.9%(8/9)
Object Counting
40%(4/10)
Object Understanding
85.7%(12/14)
Spatial Understanding
78.9%(15/19)
OCR
Overall Score
79.48%
Avg Response Time3.98s
Median input tokensincl. image tokens105
Median output tokens95
Est. cost / taskon this benchmark$0.0017
Focused Scene OCR
75.8%(75/99)
Handwritten Math
60%(6/10)
License Plate Recognition
90%(27/30)
Text Recognition
83.3%(25/30)
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