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GPT-5.4 Mini vs GPT-5.6 Sol

Compare GPT-5.4 Mini and GPT-5.6 Sol side-by-side. See how these vision models stack up in Open Prompt, Object Detection, Classification, Image Captioning, and OCR.

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Run the same image across every model that supports a task and compare their outputs side-by-side.

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OpenAIGPT-5.4 Mini
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OpenAIGPT-5.6 Sol
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Models in this comparison

GPT-5.4 Mini vs GPT-5.6 Sol on Vision Evals

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

The widest gap is Object Detection, where GPT-5.6 Sol leads 68.4% to 15.8%.

Overall, GPT-5.4 Mini averages 53.2% (#49 of 61) against 72.4% (#16 of 61) for GPT-5.6 Sol.

GPT-5.4 Mini is both cheaper ($0.0034 vs $0.0096 per sample) and faster (5.5s vs 12.2s per sample).

GPT-5.4 MiniGPT-5.6 Sol

GPT-5.4 Mini vs GPT-5.6 Sol Comparison Table

Evals updated October 8, 2026Pricing updated October 8, 2026

PropertyGPT-5.4 MiniGPT-5.6 Sol
OrganizationOpenAIOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateMar 2026Jul 2026
Context Window400K1.5M
ParametersUnknownUnknown
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.750$2.00
Output $/1M$4.50$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
Model Features
Foundation VisionSupportedSupported
LLMs with Vision CapabilitiesSupportedSupported
Multimodal VisionSupportedSupported
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
53.2%
72.4%
Avg cost / sample$0.0034$0.0096
Avg speed / sample5.51s12.16s
By task
Object Detection (low)
15.8%
±0.4, Mean of 3 runs, range 15.3 to 16.1
$0.0044
68.4%
±0.7, Mean of 3 runs, range 67.9 to 69.3
$0.015
Object Detection (high)
16.6%
±0.8, Mean of 3 runs, range 15.8 to 17.4
$0.030
68.4%
±0.8, Mean of 3 runs, range 67.7 to 69.3
$0.035
Counting (low)
58.6%
±2.0, Mean of 3 runs, range 56.8 to 60.8
$0.0019
74.3%
±1.4, Mean of 3 runs, range 73.0 to 75.7
$0.0049
Counting (high)
64.9%
±2.0, Mean of 3 runs, range 63.5 to 67.6
$0.0073
76.1%
±2.0, Mean of 3 runs, range 74.3 to 78.4
$0.0078
Identification (low)
83.3%
±1.6, Mean of 3 runs, range 81.3 to 84.4
$0.0013
89.6%
±4.7, Mean of 3 runs, range 84.4 to 93.8
$0.0028
Identification (high)
82.3%
±3.1, Mean of 3 runs, range 78.1 to 84.4
$0.0055
92.7%
±1.6, Mean of 3 runs, range 90.6 to 93.8
$0.0030
OCR (low)
51.1%
$0.0035
63.6%
$0.0095
by category
Single value
41.3%
Transcription
77.3%
Structured JSON
73.6%
Text localization
4.1%
Single value
43.5%
Transcription
91.7%
Structured JSON
82.0%
Text localization
52.2%
OCR (high)
55.8%
$0.027
65.3%
$0.024
by category
Single value
47.0%
Transcription
78.5%
Structured JSON
79.0%
Text localization
6.1%
Single value
46.1%
Transcription
92.3%
Structured JSON
83.9%
Text localization
51.6%
Reasoning (low)
57.0%
±3.3, Mean of 3 runs, range 54.3 to 60.9
$0.0022
66.0%
±2.6, Mean of 3 runs, range 63.6 to 68.9
$0.0043
Reasoning (high)
64.0%
±1.3, Mean of 3 runs, range 62.9 to 65.6
$0.0096
71.7%
±1.3, Mean of 3 runs, range 70.2 to 72.8
$0.0061

GPT-5.4 Mini vs GPT-5.6 Sol: Overview

GPT-5.4 Mini

GPT-5.4 mini is a fast, cost-efficient model developed by OpenAI and released on March 17, 2026, optimized for high-throughput workloads and subagent orchestration. It supports text and image inputs within a 400,000-token context window, making it ideal for processing extensive visual datasets and large codebases in a single request. Designed for low-latency production environments, the model integrates with key API features including function calling, web search, and tool-based computer use, allowing it to assist in automated workflows that require navigating digital interfaces.

Compared to the previous GPT-5 mini, this version runs more than twice as fast while approaching the performance levels of the flagship GPT-5.4 on reasoning and coding benchmarks. While the larger GPT-5.4 introduces native, state-of-the-art computer-use capabilities, GPT-5.4 mini provides a scalable alternative for interpreting screenshots and reasoning over dense UI layouts. For vision tasks on Playground, it excels at extracting structured information from visual documents and assisting in agentic tasks that involve real-time interpretation of software interfaces alongside text.

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 five vision tasks and averages 72.4% (#16 of 61) against 53.2% (#49 of 61) for GPT-5.4 Mini. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.

No. On the Vision Evals Object Detection benchmark at low effort, GPT-5.6 Sol leads with 68.4% against 15.8%. This is the widest gap between the two models across the benchmark's tasks.

GPT-5.4 Mini is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0034 per sample against $0.0096. GPT-5.4 Mini is priced at $0.75 per 1M input tokens and $4.50 per 1M output; GPT-5.6 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-5.4 Mini is faster. Across Roboflow's Vision Evals it averaged 5.5s 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 open prompts and object detection in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.