GPT-6 Sol vs Kimi K2.5
Compare GPT-6 Sol and Kimi K2.5 side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.
Compare GPT-6 Sol vs Kimi K2.5 live
Run the same image across every model that supports a task and compare their outputs side-by-side.
Extract and compare text from images across multiple models.
Upload an image
Drag and drop an image here, or click to browse
Models in this comparison
GPT-6 Sol vs Kimi K2.5 Comparison Table
Evals updated September 28, 2026Pricing updated September 28, 2026
| Property | GPT-6 Sol | Kimi K2.5 |
|---|---|---|
| Organization | OpenAI | Moonshot AI |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Jan 2026 |
| Context Window | 1.1M | 256K |
| Parameters | undisclosed | 1T |
| License | Proprietary | Modified MIT |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $0.450 |
| Output $/1M | $10.00 | $2.25 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Object Detection | Demo | |
| Promptable Concept Segmentation | Demo | |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 82.3% | Not evaluated |
| Avg cost / sample | $0.0065 | – |
| Avg speed / sample | 9.94s | – |
| By task | ||
| Object Detection (low) | 74.7% ±0.7, Mean of 3 runs, range 74.1 to 75.5 | – |
| Object Detection (high) | 76.4% ±1.2, Mean of 3 runs, range 75.0 to 77.4 | – |
| Counting (low) | 76.6% ±2.0, Mean of 3 runs, range 74.3 to 78.4 | – |
| Counting (high) | 77.5% ±1.3, Mean of 3 runs, range 75.7 to 78.4 | – |
| Identification (low) | 93.8% ±0.0, Mean of 3 runs, range 93.8 to 93.8 | – |
| Identification (high) | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 | – |
| OCR (low) | 91.7% ±0.4, Mean of 3 runs, range 91.2 to 92.1 | – |
| OCR (high) | 92.0% ±0.6, Mean of 3 runs, range 91.4 to 92.6 | – |
| Data Extraction (low) | 84.2% ±0.5, Mean of 3 runs, range 83.5 to 84.5 | – |
| Data Extraction (high) | 83.5% ±2.1, Mean of 3 runs, range 81.4 to 85.6 | – |
| Reasoning (low) | 72.6% ±3.6, Mean of 3 runs, range 69.5 to 76.8 | – |
| Reasoning (high) | 77.7% ±1.0, Mean of 3 runs, range 76.8 to 78.8 | – |
GPT-6 Sol vs Kimi K2.5: Overview
GPT-6 Sol is a proprietary multimodal reasoning model from OpenAI, released on September 22, 2026 alongside GPT-6 Luna as an efficiency-oriented tier of the GPT-6 family that began with GPT-6 Astra. OpenAI states that Sol and Luna are trained with methods similar to those used for Astra, carrying the same work on professional tasks, factuality, coding, computer use, and alignment into models that run faster. Sol accepts text and image input and returns text output, and OpenAI documents a context window of roughly one million tokens together with a knowledge cutoff of April 20, 2026.
The model targets complex coding and agentic workflows and exposes a configurable reasoning effort setting with levels of none, low, medium, high, xhigh, and max, which trades latency and token consumption against answer quality. OpenAI reports results including 33.2% on AutomationBench at xhigh effort and 56.4% on Agents' Last Exam at max effort, while its reported DeepSWE and OSWorld 2.0 figures of 68.8% and 64.4% fall below those of the earlier GPT-5.6 Sol. Its vision behavior covers image understanding tasks such as visual question answering, captioning, document and chart interpretation, and text recognition.
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.