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Gemini 2.5 Pro vs GPT-5.6 Sol

Compare Gemini 2.5 Pro and GPT-5.6 Sol side-by-side. See how these vision models stack up in Object Detection, Open Prompt, Classification, OCR, and Image Captioning.

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GoogleGemini 2.5 Pro
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OpenAIGPT-5.6 Sol
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Gemini 2.5 Pro vs GPT-5.6 Sol: Overview

Gemini 2.5 Pro

Gemini 2.5 Pro, released on June 17, 2025, is Google DeepMind’s most capable model in the Gemini 2.5 family, optimized for deep reasoning, coding, and complex multimodal tasks. It accepts text, images, audio, video, and PDFs as input and outputs text. The model supports 1 million input tokens with an output capacity of up to 65K tokens, enabling large-scale comprehension of datasets, codebases, and technical documents. Its training knowledge extends to January 2025.

Pro outperforms earlier Gemini 2.0 models across benchmarks, including agentic coding tasks where it achieved ~63.8% on SWE-Bench Verified. It supports structured outputs, function calling, code execution, search grounding, and URL context, making it well-suited for enterprise, STEM, and developer workflows. However, it does not currently support image or audio generation in its stable release, and its higher computational cost and latency make it less efficient than Flash or Flash-Lite. It is available via the Gemini API, Google AI Studio, and Vertex AI.

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.

Gemini 2.5 Pro vs GPT-5.6 Sol Comparison Table

PropertyGemini 2.5 ProGPT-5.6 Sol
OrganizationGoogleOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateJun 2025Jul 2026
Context Window1.0M1.5M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$1.25$5.00
Output $/1M$10.00$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
70.15%
Avg Response Time11.87s
Median input tokensincl. image tokens294
Median output tokens565
Est. cost / taskon this benchmark$0.0060
Defect Detection
73.3%(11/15)
Document Understanding
88.9%(8/9)
Object Counting
20%(2/10)
Object Understanding
78.6%(11/14)
Spatial Understanding
78.9%(15/19)
OCR
Overall Score
78.6%
Avg Response Time4.91s
Median input tokensincl. image tokens290
Median output tokens323
Est. cost / taskon this benchmark$0.0036
Focused Scene OCR
78.8%(78/99)
Handwritten Math
80%(8/10)
License Plate Recognition
90%(27/30)
Text Recognition
73.3%(22/30)
VQA & Extraction
75%(45/60)

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