Roboflow

GPT-5.4 vs GPT-6 Luna

GPT-6 Luna: 69.7% average across five Vision Evals tasks (#22 of 65 models). We have not run GPT-5.4 through Roboflow's Vision Evals, so there is no head-to-head score. Run both on your own image below.

OpenAI

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OpenAIGPT-5.4
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OpenAIGPT-6 Luna
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GPT-5.4 vs GPT-6 Luna Comparison Table

Evals updated October 26, 2026Pricing updated November 7, 2026

PropertyGPT-5.4GPT-6 Luna
OrganizationOpenAIOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateMar 2026Sep 2026
Context Window1.1M1.1M
ParametersUnknownUnknown
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$2.50$0.100
Output $/1M$15.00$0.500
Vision Tasks
Promptable Concept SegmentationNot listedDemo
Both support 10 more tasks
Model Features
Both support 3 more features
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
OverallNot 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
$0.0006
Object Detection (high)–
68.0%
±0.7, Mean of 3 runs, range 67.3 to 68.6
$0.0014
Counting (low)–
71.6%
±0.0, Mean of 3 runs, range 71.6 to 71.6
$0.0002
Counting (high)–
72.1%
±0.7, Mean of 3 runs, range 71.6 to 73.0
$0.0004
Identification (low)–
87.5%
±3.1, Mean of 3 runs, range 84.4 to 90.6
$0.0002
Identification (high)–
87.5%
±3.1, Mean of 3 runs, range 84.4 to 90.6
$0.0002
OCR (low)–
59.8%
$0.0004
by category
Single value
41.7%
Transcription
80.0%
Structured JSON
78.2%
Text localization
47.4%
OCR (high)–
62.5%
$0.0013
by category
Single value
44.4%
Transcription
84.0%
Structured JSON
81.0%
Text localization
50.0%
Reasoning (low)–
64.2%
±3.0, Mean of 3 runs, range 60.3 to 66.2
$0.0003
Reasoning (high)–
71.1%
±2.3, Mean of 3 runs, range 68.2 to 72.8
$0.0004

GPT-5.4 vs GPT-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-6 Luna

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.