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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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OpenAIGPT-5.6 Sol
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MoonshotAIKimi K2.5
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MoonshotAI

GPT-5.6 Sol vs Kimi K2.5: Overview

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.

Kimi K2.5

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

PropertyGPT-5.6 SolKimi K2.5
OrganizationOpenAIMoonshot AI
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateJul 2026Jan 2026
Context Window1.5M256K
Parameters1T
LicenseProprietaryModified MIT
Pricing per 1M tokens
Input $/1M$5.00$0.375
Output $/1M$30.00$2.02
Vision Tasks
CaptioningDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Chart Question Answering
classificationDemo
Document Question Answering
object-detectionDemo
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 Time14.81s
Median input tokensincl. image tokens1.6K
Median output tokens766
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 Time13.09s
Median input tokensincl. image tokens119
Median output tokens258
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