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GPT-5.6 Luna vs GPT-5.6 Sol

Compare GPT-5.6 Luna and GPT-5.6 Sol 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-5.6 Sol on Vision Evals

GPT-5.6 Sol scores higher on all six Vision Evals tasks.

The widest gap is Reasoning, where GPT-5.6 Sol leads 65.6% to 55.0%.

Overall, GPT-5.6 Luna averages 71.5% (#13 of 25) against 76.9% (#9 of 25) for GPT-5.6 Sol.

GPT-5.6 Luna is both cheaper ($0.0005 vs $0.025 per sample) and faster (6.5s vs 11.7s per sample).

GPT-5.6 LunaGPT-5.6 Sol

GPT-5.6 Luna vs GPT-5.6 Sol Comparison Table

Evals updated August 6, 2026Pricing updated August 11, 2026

PropertyGPT-5.6 LunaGPT-5.6 Sol
OrganizationOpenAIOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateJul 2026Jul 2026
Context Window1.5M1.5M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.100$5.00
Output $/1M$0.600$30.00
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
71.5%
76.9%
Avg cost / sample$0.0005$0.025
Avg speed / sample6.55s11.72s
By task
Object Detection
59.9%
$0.0008
68.2%
$0.045
Counting
66.2%
$0.0003
73.0%
$0.013
Identification
78.1%
$0.0002
81.3%
$0.0070
OCR
88.4%
$0.0006
90.7%
$0.032
Data Extraction
81.4%
$0.0002
82.5%
$0.0085
Reasoning (low)
55.0%
$0.0003
65.6%
$0.011
Reasoning (high)
60.9%
$0.0007
72.2%
$0.016

GPT-5.6 Luna vs GPT-5.6 Sol: 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 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-5.6 Sol

GPT-5.6 Sol is the flagship model in OpenAI's GPT-5.6 family, which also includes Terra (a balanced everyday-work tier) and Luna (a fast, cost-efficient tier). Sol is designed for demanding reasoning, long-horizon agentic workflows, software engineering, computer use, scientific research, and cybersecurity tasks. It introduces two new capability modes: a "max" reasoning effort setting that allocates additional compute time for difficult problems, and an "ultra" mode that coordinates multiple subagents in parallel to accelerate complex, multi-step work. The model supports native multimodal input, allowing it to process screenshots, diagrams, charts, documents, and photographs alongside text. A reported context window of approximately 1.5 million tokens enables processing of large codebases, lengthy research documents, and extended agentic sessions.

GPT-5.6 Sol was announced on June 26, 2026, initially in a limited preview for trusted partners, and reached general availability on July 9, 2026. On the Agents' Last Exam benchmark, which evaluates long-running professional workflows across 55 fields, Sol scores 53.6. On Terminal-Bench 2.1, which tests command-line agentic coding workflows, Sol Ultra achieves 91.9%. The model also demonstrates gains in life sciences evaluations, including long-horizon genomics and quantitative biology analyses. OpenAI paired the release with its most extensive safety evaluation to date, combining human red teaming with large-scale automated testing, and classified Sol as High capability in both cybersecurity and biological risk under its Preparedness Framework, though it does not cross the Critical threshold in either category.

Frequently Asked Questions

On Roboflow's Vision Evals, GPT-5.6 Sol performed better. It scores higher on all six vision tasks and averages 76.9% (#9 of 25) against 71.5% (#13 of 25) 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 Reasoning benchmark at low effort, GPT-5.6 Sol leads with 65.6% against 55.0%. 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 $0.025. GPT-5.6 Luna is priced at $0.10 per 1M input tokens and $0.60 per 1M output; GPT-5.6 Sol is priced at $5.00 per 1M input tokens and $30.00 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 6.5s per inference against 11.7s. 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.