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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Gemini 2.5 Pro vs GPT-5.6 Sol: Overview
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 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
| Property | Gemini 2.5 Pro | GPT-5.6 Sol |
|---|---|---|
| Organization | OpenAI | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jun 2025 | Jul 2026 |
| Context Window | 1.0M | 1.5M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $1.25 | $5.00 |
| Output $/1M | $10.00 | $30.00 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Classification | Demo | Demo |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| 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 Time | 11.87s | |
| Median input tokensincl. image tokens | 294 | |
| Median output tokens | 565 | |
| 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 Time | 4.91s | |
| Median input tokensincl. image tokens | 290 | |
| Median output tokens | 323 | |
| 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