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GPT-4.1 mini vs GPT-5.6 Luna

Compare GPT-4.1 mini and GPT-5.6 Luna side-by-side. See how these vision models stack up in Open Prompt, Image Captioning, OCR, Object Detection, and Classification.

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OpenAIGPT-4.1 mini

GPT-4.1 mini is deprecated and can no longer be run. Details and evals are still available on its model page.

OpenAIGPT-5.6 Luna
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GPT-4.1 mini vs GPT-5.6 Luna: Overview

GPT-4.1 mini

GPT-4.1 mini, released by OpenAI in April 2025, is a smaller, faster, and cheaper variant of GPT-4.1 designed for high-throughput and cost-sensitive applications. It is multimodal, handling both text and images, and inherits the full model’s strengths in coding, structured outputs, and long-context reasoning. With support for up to 1 million tokens, it enables reliable processing of extended documents, multi-file codebases, and lengthy conversations while keeping latency low.

GPT-4.1 mini offers an efficient alternative to GPT-4.1 and replaced GPT-4o mini as the default ChatGPT model in May 2025. Despite being smaller, it matches or outperforms GPT-4o on several benchmarks, particularly for instruction following and real-world coding tasks. Ideal use cases include large-scale conversational systems, affordable developer tools, document analysis, and interactive assistants where speed and cost are critical.

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-4.1 mini vs GPT-5.6 Luna Comparison Table

PropertyGPT-4.1 miniGPT-5.6 Luna
OrganizationOpenAIOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateApr 2025Jul 2026
Context Window1.0M1.5M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.400$1.00
Output $/1M$1.60$6.00
Vision Tasks
CaptioningDemo
ClassificationDemo
Object DetectionDemo
OCRDemo
Vision Language
Visual Question AnsweringDemo
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
70.15%
Avg Response Time4.79s
Median input tokensincl. image tokens1.3K
Median output tokens80
Est. cost / taskon this benchmark$0.0017
Defect Detection
80%(12/15)
Document Understanding
66.7%(6/9)
Object Counting
20%(2/10)
Object Understanding
92.9%(13/14)
Spatial Understanding
73.7%(14/19)
OCR
Overall Score
73.36%
Avg Response Time2.21s
Median input tokensincl. image tokens105
Median output tokens73
Est. cost / taskon this benchmark$0.0005
Focused Scene OCR
66.7%(66/99)
Handwritten Math
20%(2/10)
License Plate Recognition
83.3%(25/30)
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