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GPT-5.4 Mini vs GPT-5.6 Luna

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

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OpenAIGPT-5.4 Mini
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OpenAIGPT-5.6 Luna
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Models in this comparison

GPT-5.4 Mini vs GPT-5.6 Luna on Vision Evals

GPT-5.6 Luna scores higher on 3 of the six Vision Evals tasks.

The widest gap is Object Detection, where GPT-5.6 Luna leads 59.9% to 16.1%.

Overall, GPT-5.4 Mini averages 63.5% (#27 of 30) against 71.5% (#14 of 30) for GPT-5.6 Luna.

GPT-5.6 Luna is cheaper ($0.0005 vs $0.0030 per sample), while GPT-5.4 Mini is faster (5.3s vs 6.5s per sample).

GPT-5.4 MiniGPT-5.6 Luna

GPT-5.4 Mini vs GPT-5.6 Luna Comparison Table

Evals updated August 14, 2026Pricing updated August 14, 2026

PropertyGPT-5.4 MiniGPT-5.6 Luna
OrganizationOpenAIOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateMar 2026Jul 2026
Context Window400K1.5M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.750$0.100
Output $/1M$4.50$0.600
Vision Tasks
CaptioningDemoDemo
Chart Question Answering
ClassificationDemoDemo
Document Question Answering
Image Tagging
Multi-Label Classification
Object DetectionDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort
Overall
63.5%
71.5%
Avg cost / sample$0.0030$0.0005
Avg speed / sample5.35s6.55s
By task
Object Detection
16.1%
$0.0044
59.9%
$0.0008
Counting
60.8%
$0.0019
66.2%
$0.0003
Identification
78.1%
$0.0013
78.1%
$0.0002
OCR
88.1%
$0.0042
88.4%
$0.0006
Data Extraction
82.5%
$0.0014
81.4%
$0.0002
Reasoning (low)
55.6%
$0.0023
55.0%
$0.0003
Reasoning (high)
62.9%
$0.0081
60.9%
$0.0007

GPT-5.4 Mini vs GPT-5.6 Luna: Overview

GPT-5.4 Mini

GPT-5.4 mini is a fast, cost-efficient model developed by OpenAI and released on March 17, 2026, optimized for high-throughput workloads and subagent orchestration. It supports text and image inputs within a 400,000-token context window, making it ideal for processing extensive visual datasets and large codebases in a single request. Designed for low-latency production environments, the model integrates with key API features including function calling, web search, and tool-based computer use, allowing it to assist in automated workflows that require navigating digital interfaces.

Compared to the previous GPT-5 mini, this version runs more than twice as fast while approaching the performance levels of the flagship GPT-5.4 on reasoning and coding benchmarks. While the larger GPT-5.4 introduces native, state-of-the-art computer-use capabilities, GPT-5.4 mini provides a scalable alternative for interpreting screenshots and reasoning over dense UI layouts. For vision tasks on Playground, it excels at extracting structured information from visual documents and assisting in agentic tasks that involve real-time interpretation of software interfaces alongside text.

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.

Frequently Asked Questions

On Roboflow's Vision Evals, GPT-5.6 Luna performed better. It scores higher on 3 of the six vision tasks and averages 71.5% (#14 of 30) against 63.5% (#27 of 30) for GPT-5.4 Mini. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.

No. On the Vision Evals Object Detection benchmark, GPT-5.6 Luna leads with 59.9% against 16.1%. This is the widest gap between the two models across the benchmark's tasks.

GPT-5.6 Luna is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0005 per sample against $0.0030. GPT-5.4 Mini is priced at $0.75 per 1M input tokens and $4.50 per 1M output; GPT-5.6 Luna is priced at $0.10 per 1M input tokens and $0.60 per 1M output. Actual costs depend on your image sizes, prompts, and output length.

GPT-5.4 Mini is faster. Across Roboflow's Vision Evals it averaged 5.3s per inference against 6.5s. 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 open prompts and object detection in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.