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GPT-5.6 Sol vs GPT-6 Luna

GPT-5.6 Sol and GPT-6 Luna score 72.4% vs 69.7% averaged across five Vision Evals tasks (#19 and #22 of 65 models), so GPT-5.6 Sol leads overall.

  • GPT-5.6 Sol wins all 5 tasks, with its widest lead on OCR (63.6% vs 59.8%).
  • GPT-6 Luna is under a tenth of the cost: $0.0004 vs $0.019 overall average cost per sample.
  • GPT-6 Luna is faster: 10.2s vs 12.2s per sample.

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

Score by taskGPT-5.6 SolGPT-6 Luna
OCR
63.6
59.8
Object Detection
68.4
65.5
Counting
74.3
71.6
Identification
89.6
87.5
Reasoning
66.0
64.2

Roboflow Vision Evals. Full breakdown below.

GPT-5.6 Sol vs GPT-6 Luna Comparison Table

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

PropertyGPT-5.6 SolGPT-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$4.00$0.100
Output $/1M$20.00$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
Overall
72.4%
69.7%
Avg cost / sample$0.019$0.0004
Avg speed / sample12.16s10.21s
By task
Object Detection (low)
68.4%
±0.7, Mean of 3 runs, range 67.9 to 69.3
$0.031
65.5%
±0.4, Mean of 3 runs, range 65.2 to 66.0
$0.0006
Object Detection (high)
68.4%
±0.8, Mean of 3 runs, range 67.7 to 69.3
$0.071
68.0%
±0.7, Mean of 3 runs, range 67.3 to 68.6
$0.0014
Counting (low)
74.3%
±1.4, Mean of 3 runs, range 73.0 to 75.7
$0.0098
71.6%
±0.0, Mean of 3 runs, range 71.6 to 71.6
$0.0002
Counting (high)
76.1%
±2.0, Mean of 3 runs, range 74.3 to 78.4
$0.016
72.1%
±0.7, Mean of 3 runs, range 71.6 to 73.0
$0.0004
Identification (low)
89.6%
±4.7, Mean of 3 runs, range 84.4 to 93.8
$0.0056
87.5%
±3.1, Mean of 3 runs, range 84.4 to 90.6
$0.0002
Identification (high)
92.7%
±1.6, Mean of 3 runs, range 90.6 to 93.8
$0.0060
87.5%
±3.1, Mean of 3 runs, range 84.4 to 90.6
$0.0002
OCR (low)
63.6%
$0.019
59.8%
$0.0004
by category
Single value
43.5%
Transcription
91.7%
Structured JSON
82.0%
Text localization
52.2%
Single value
41.7%
Transcription
80.0%
Structured JSON
78.2%
Text localization
47.4%
OCR (high)
65.3%
$0.048
62.5%
$0.0013
by category
Single value
46.1%
Transcription
92.3%
Structured JSON
83.9%
Text localization
51.6%
Single value
44.4%
Transcription
84.0%
Structured JSON
81.0%
Text localization
50.0%
Reasoning (low)
66.0%
±2.6, Mean of 3 runs, range 63.6 to 68.9
$0.0086
64.2%
±3.0, Mean of 3 runs, range 60.3 to 66.2
$0.0003
Reasoning (high)
71.7%
±1.3, Mean of 3 runs, range 70.2 to 72.8
$0.012
71.1%
±2.3, Mean of 3 runs, range 68.2 to 72.8
$0.0004

GPT-5.6 Sol vs GPT-6 Luna: Overview

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. 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.

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-5.6 Sol performed better. It scores higher on all five vision tasks and averages 72.4% (#19 of 65) against 69.7% (#22 of 65) for GPT-6 Luna. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.

Yes. On the Vision Evals OCR benchmark at low effort, GPT-5.6 Sol leads with 63.6% against 59.8%. 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 $0.019. GPT-5.6 Sol is priced at $4.00 per 1M input tokens and $20.00 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-6 Luna is faster. Across Roboflow's Vision Evals it averaged 10.2s per inference against 12.2s. 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 OCR and image captioning in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.