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GPT-5.6 Sol vs GPT-6.1 Sol

Compare GPT-5.6 Sol and GPT-6.1 Sol side-by-side. See how these vision models stack up in OCR, Image Captioning, Object Detection, Open Prompt, and Classification.

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GPT-5.6 Sol vs GPT-6.1 Sol on Vision Evals

GPT-6.1 Sol scores higher on 4 of the five Vision Evals tasks.

The widest gap is Reasoning, where GPT-6.1 Sol leads 83.7% to 66.0%.

Overall, GPT-5.6 Sol averages 72.4% (#18 of 63) against 81.7% (#5 of 63) for GPT-6.1 Sol.

GPT-6.1 Sol is cheaper ($0.0068 vs $0.019 per sample), while GPT-5.6 Sol is faster (12.2s vs 12.8s per sample).

GPT-5.6 SolGPT-6.1 Sol

GPT-5.6 Sol vs GPT-6.1 Sol Comparison Table

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

PropertyGPT-5.6 SolGPT-6.1 Sol
OrganizationOpenAIOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateJul 2026Sep 2026
Context Window1.5M1.1M
ParametersUnknownundisclosed
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$4.00$2.00
Output $/1M$20.00$10.00
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
72.4%
81.7%
Avg cost / sample$0.019$0.0068
Avg speed / sample12.16s12.82s
By task
Object Detection (low)
68.4%
±0.7, Mean of 3 runs, range 67.9 to 69.3
$0.031
80.8%
±0.1, Mean of 3 runs, range 80.7 to 80.9
$0.010
Object Detection (high)
68.4%
±0.8, Mean of 3 runs, range 67.7 to 69.3
$0.071
81.6%
±0.4, Mean of 3 runs, range 81.1 to 82.0
$0.022
Counting (low)
74.3%
±1.4, Mean of 3 runs, range 73.0 to 75.7
$0.0098
78.8%
±3.4, Mean of 3 runs, range 75.7 to 82.4
$0.0037
Counting (high)
76.1%
±2.0, Mean of 3 runs, range 74.3 to 78.4
$0.016
80.2%
±3.4, Mean of 3 runs, range 77.0 to 83.8
$0.0057
Identification (low)
89.6%
±4.7, Mean of 3 runs, range 84.4 to 93.8
$0.0056
89.6%
±1.6, Mean of 3 runs, range 87.5 to 90.6
$0.0025
Identification (high)
92.7%
±1.6, Mean of 3 runs, range 90.6 to 93.8
$0.0060
88.5%
±1.6, Mean of 3 runs, range 87.5 to 90.6
$0.0030
OCR (low)
63.6%
$0.019
75.8%
$0.0069
by category
Single value
43.5%
Transcription
91.7%
Structured JSON
82.0%
Text localization
52.2%
Single value
58.3%
Transcription
95.7%
Structured JSON
89.5%
Text localization
74.4%
OCR (high)
65.3%
$0.048
75.8%
$0.016
by category
Single value
46.1%
Transcription
92.3%
Structured JSON
83.9%
Text localization
51.6%
Single value
56.5%
Transcription
96.0%
Structured JSON
90.4%
Text localization
76.7%
Reasoning (low)
66.0%
±2.6, Mean of 3 runs, range 63.6 to 68.9
$0.0086
83.7%
±1.3, Mean of 3 runs, range 82.1 to 84.8
$0.0033
Reasoning (high)
71.7%
±1.3, Mean of 3 runs, range 70.2 to 72.8
$0.012
88.7%
±2.0, Mean of 3 runs, range 87.4 to 91.4
$0.0042

GPT-5.6 Sol vs GPT-6.1 Sol: 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. 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.

GPT-6.1 Sol

GPT-6.1 Sol is a reasoning model in OpenAI's GPT-6 series that accepts text and image input and returns text. It is an upgrade to GPT-6 Sol positioned to approach the intelligence of the larger GPT-6 Astra model on agentic coding, computer use, and professional knowledge work. The model exposes an adjustable reasoning effort control, ranging from low settings for simple turns to maximum settings for harder tasks, and can be driven with tool use enabled or disabled. It operates over a context window of roughly one million tokens and emits up to 128,000 output tokens in a single response, which supports long-running agent loops over large codebases and multi-document collections. Audio and video inputs are not supported.

On the visual side, the model is evaluated on GDP.pdf, a benchmark that asks professional questions about complex PDF documents containing tables, charts, diagrams, and fine-print details, and on OSWorld 2.0, which measures agents operating graphical computer applications. OpenAI reports that GPT-6.1 Sol performs on par with or better than GPT-6 Sol across its image input safety evaluations, and that the share of responses containing a factual error at low reasoning effort falls from 11.4 percent to 7.7 percent.

Frequently Asked Questions

On Roboflow's Vision Evals, GPT-6.1 Sol performed better. It scores higher on 4 of the five vision tasks and averages 81.7% (#5 of 63) against 72.4% (#18 of 63) for GPT-5.6 Sol. 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-6.1 Sol leads with 83.7% against 66.0%. This is the widest gap between the two models across the benchmark's tasks.

GPT-6.1 Sol is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0068 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-6.1 Sol is priced at $2.00 per 1M input tokens and $10.00 per 1M output. Actual costs depend on your image sizes, prompts, and output length.

GPT-5.6 Sol is faster. Across Roboflow's Vision Evals it averaged 12.2s per inference against 12.8s. 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.