GPT-5.6 Sol vs Kimi K2.5
Compare GPT-5.6 Sol and Kimi K2.5 side-by-side. See how these vision models stack up in OCR, Image Captioning, and Open Prompt.
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GPT-5.6 Sol vs Kimi K2.5: Overview
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.
Kimi K2.5 is a frontier-scale multimodal AI model developed by Moonshot AI and released on January 27, 2026. As a significant advancement within the Kimi K2 family, it utilizes a sparse Mixture-of-Experts (MoE) architecture with 1 trillion total parameters (32 billion active per inference) and a massive 256K-token context window. The model features native multimodal integration via a 400M-parameter MoonViT encoder, allowing it to process text, images, and video frames simultaneously. Built for both speed and depth, it offers "Instant" and "Thinking" modes, the latter of which excels at expert-level reasoning, scoring 50.2% on the Humanity’s Last Exam (HLE) benchmark when equipped with tools.
The model is released under a Modified MIT License, which remains open-weight but requires attribution for high-revenue commercial entities. It introduces an "Agent Swarm" paradigm capable of coordinating up to 100 specialized sub-agents for parallel workflows, significantly reducing latency in complex research tasks. For vision tasks, Kimi K2.5 demonstrates strong autonomous visual debugging capabilities, where it can inspect its own generated UI outputs against visual specifications to iteratively refine frontend code. This makes it a powerful choice for developers testing automated UI reconstruction, high-fidelity OCR document processing, and multi-step agentic research grounded in complex visual data.
GPT-5.6 Sol vs Kimi K2.5 Comparison Table
| Property | GPT-5.6 Sol | Kimi K2.5 |
|---|---|---|
| Organization | OpenAI | Moonshot AI |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Jul 2026 | Jan 2026 |
| Context Window | 1.5M | 256K |
| Parameters | 1T | |
| License | Proprietary | Modified MIT |
| Pricing per 1M tokens | ||
| Input $/1M | $5.00 | $0.375 |
| Output $/1M | $30.00 | $2.02 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Chart Question Answering | ||
| classification | Demo | |
| Document Question Answering | ||
| object-detection | Demo | |
| Model Features | ||
| Multimodal Vision | ||
| LLMs with Vision Capabilities | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Visual Understanding | ||
| Overall Score | 35.82% | |
| Avg Response Time | 14.81s | |
| Median input tokensincl. image tokens | 1.6K | |
| Median output tokens | 766 | |
| Est. cost / taskon this benchmark | $0.0021 | |
| Defect Detection | 46.7%(7/15) | |
| Document Understanding | 55.6%(5/9) | |
| Object Counting | 10%(1/10) | |
| Object Understanding | 42.9%(6/14) | |
| Spatial Understanding | 26.3%(5/19) | |
| OCR | ||
| Overall Score | 19.65% | |
| Avg Response Time | 13.09s | |
| Median input tokensincl. image tokens | 119 | |
| Median output tokens | 258 | |
| Est. cost / taskon this benchmark | $0.0006 | |
| Focused Scene OCR | 10.1%(10/99) | |
| Handwritten Math | 50%(5/10) | |
| License Plate Recognition | 6.7%(2/30) | |
| Text Recognition | 26.7%(8/30) | |
| VQA & Extraction | 33.3%(20/60) | |
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