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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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OpenAIGPT-5.4 Mini
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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.

GPT-5.4 Mini vs GPT-5.6 Sol Comparison Table

PropertyGPT-5.4 MiniGPT-5.6 Sol
OrganizationOpenAIOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateMar 2026Jul 2026
Context Window400K1.5M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.750$5.00
Output $/1M$4.50$30.00
Vision Tasks
CaptioningDemoDemo
ClassificationDemoDemo
Object DetectionDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Chart Question Answering
Document Question Answering
Model Features
LLMs with Vision Capabilities
Multimodal Vision
Foundation Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Visual Understanding
Overall Score
77.61%
Avg Response Time5.80s
Median input tokensincl. image tokens1.4K
Median output tokens104
Est. cost / taskon this benchmark$0.0015
Defect Detection
73.3%(11/15)
Document Understanding
88.9%(8/9)
Object Counting
40%(4/10)
Object Understanding
92.9%(13/14)
Spatial Understanding
84.2%(16/19)
OCR
Overall Score
77.29%
Avg Response Time3.24s
Median input tokensincl. image tokens105
Median output tokens126
Est. cost / taskon this benchmark$0.0006
Focused Scene OCR
75.8%(75/99)
Handwritten Math
40%(4/10)
License Plate Recognition
86.7%(26/30)
Text Recognition
73.3%(22/30)
VQA & Extraction
83.3%(50/60)

Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology