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GPT-5.6 Luna vs GPT-6 Luna

Compare GPT-5.6 Luna and GPT-6 Luna side-by-side. See how these vision models stack up in Classification, Image Captioning, OCR, Object Detection, and Open Prompt.

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Models in this comparison

GPT-5.6 Luna vs GPT-6 Luna on Vision Evals

GPT-6 Luna scores higher on all five Vision Evals tasks.

The widest gap is OCR, where GPT-6 Luna leads 59.8% to 51.8%.

Overall, GPT-5.6 Luna averages 64.7% (#29 of 63) against 69.7% (#21 of 63) for GPT-6 Luna.

GPT-6 Luna is cheaper ($0.0004 vs $0.0011 per sample), while GPT-5.6 Luna is faster (8.6s vs 10.2s per sample).

GPT-5.6 LunaGPT-6 Luna

GPT-5.6 Luna vs GPT-6 Luna Comparison Table

Evals updated October 13, 2026Pricing updated October 16, 2026

PropertyGPT-5.6 LunaGPT-6 Luna
OrganizationOpenAIOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateJul 2026Sep 2026
Context Window1.5M1.1M
ParametersUnknownUnknown
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$0.200$0.100
Output $/1M$1.20$0.500
Vision Tasks
CaptioningDemoDemo
Chart Question AnsweringSupportedSupported
ClassificationDemoDemo
Document Question AnsweringSupportedSupported
Image TaggingSupportedSupported
Multi-Label ClassificationSupportedSupported
Object DetectionDemoDemo
OCRDemoDemo
Vision LanguageSupportedSupported
Visual Question AnsweringDemoDemo
Promptable Concept SegmentationNot listedDemo
Model Features
Foundation VisionSupportedSupported
LLMs with Vision CapabilitiesSupportedSupported
Multimodal VisionSupportedSupported
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
64.7%
69.7%
Avg cost / sample$0.0011$0.0004
Avg speed / sample8.60s10.21s
By task
Object Detection (low)
61.0%
±1.2, Mean of 3 runs, range 59.9 to 62.2
$0.0015
65.5%
±0.4, Mean of 3 runs, range 65.2 to 66.0
$0.0006
Object Detection (high)
62.3%
±1.2, Mean of 3 runs, range 61.4 to 63.8
$0.0050
68.0%
±0.7, Mean of 3 runs, range 67.3 to 68.6
$0.0014
Counting (low)
67.1%
±1.4, Mean of 3 runs, range 66.2 to 68.9
$0.0006
71.6%
±0.0, Mean of 3 runs, range 71.6 to 71.6
$0.0002
Counting (high)
70.7%
±3.4, Mean of 3 runs, range 66.2 to 73.0
$0.0015
72.1%
±0.7, Mean of 3 runs, range 71.6 to 73.0
$0.0004
Identification (low)
83.3%
±1.6, Mean of 3 runs, range 81.3 to 84.4
$0.0004
87.5%
±3.1, Mean of 3 runs, range 84.4 to 90.6
$0.0002
Identification (high)
84.4%
±6.3, Mean of 3 runs, range 78.1 to 90.6
$0.0007
87.5%
±3.1, Mean of 3 runs, range 84.4 to 90.6
$0.0002
OCR (low)
51.8%
$0.0011
59.8%
$0.0004
by category
Single value
29.6%
Transcription
75.5%
Structured JSON
73.6%
Text localization
39.7%
Single value
41.7%
Transcription
80.0%
Structured JSON
78.2%
Text localization
47.4%
OCR (high)
55.7%
$0.0041
62.5%
$0.0013
by category
Single value
33.0%
Transcription
83.5%
Structured JSON
77.0%
Text localization
43.8%
Single value
44.4%
Transcription
84.0%
Structured JSON
81.0%
Text localization
50.0%
Reasoning (low)
60.5%
±5.0, Mean of 3 runs, range 55.0 to 64.9
$0.0006
64.2%
±3.0, Mean of 3 runs, range 60.3 to 66.2
$0.0003
Reasoning (high)
65.6%
±3.6, Mean of 3 runs, range 60.9 to 68.2
$0.0015
71.1%
±2.3, Mean of 3 runs, range 68.2 to 72.8
$0.0004

GPT-5.6 Luna vs GPT-6 Luna: Overview

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 supports 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-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.

Frequently Asked Questions

On Roboflow's Vision Evals, GPT-6 Luna performed better. It scores higher on all five vision tasks and averages 69.7% (#21 of 63) against 64.7% (#29 of 63) for GPT-5.6 Luna. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.

No. On the Vision Evals OCR benchmark at low effort, GPT-6 Luna leads with 59.8% against 51.8%. This is the widest gap between the two models across the benchmark's tasks.

GPT-6 Luna is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0004 per sample against $0.0011. GPT-5.6 Luna is priced at $0.20 per 1M input tokens and $1.20 per 1M output; GPT-6 Luna is priced at $0.10 per 1M input tokens and $0.50 per 1M output. Actual costs depend on your image sizes, prompts, and output length.

GPT-5.6 Luna is faster. Across Roboflow's Vision Evals it averaged 8.6s per inference against 10.2s. 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 image classification and image captioning in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.