Grok 4.6 vs Kimi K2.5
Compare Grok 4.6 and Kimi K2.5 side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, and OCR.
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Grok 4.6 vs Kimi K2.5 Comparison Table
Evals updated September 28, 2026Pricing updated September 28, 2026
| Property | Grok 4.6 | Kimi K2.5 |
|---|---|---|
| Organization | SpaceXAI | Moonshot AI |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Aug 2026 | Jan 2026 |
| Context Window | 500K | 256K |
| Parameters | 1T | |
| License | Proprietary | Modified MIT |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $0.450 |
| Output $/1M | $6.00 | $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 | |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 68.7% | Not evaluated |
| Avg cost / sample | $0.0097 | – |
| Avg speed / sample | 17.55s | – |
| By task | ||
| Object Detection (low) | 23.8% ±2.8, Mean of 3 runs, range 20.2 to 25.9 | – |
| Object Detection (high) | 24.0% ±1.0, Mean of 3 runs, range 23.1 to 25.1 | – |
| Counting (low) | 65.8% ±4.1, Mean of 3 runs, range 62.2 to 70.3 | – |
| Counting (high) | 56.8% ±1.4, Mean of 3 runs, range 55.4 to 58.1 | – |
| Identification (low) | 84.4% ±3.1, Mean of 3 runs, range 81.3 to 87.5 | – |
| Identification (high) | 85.4% ±1.6, Mean of 3 runs, range 84.4 to 87.5 | – |
| OCR (low) | 91.8% ±0.3, Mean of 3 runs, range 91.5 to 92.1 | – |
| OCR (high) | 91.6% ±0.2, Mean of 3 runs, range 91.4 to 91.7 | – |
| Data Extraction (low) | 85.6% ±1.0, Mean of 3 runs, range 84.5 to 86.6 | – |
| Data Extraction (high) | 85.6% ±1.0, Mean of 3 runs, range 84.5 to 86.6 | – |
| Reasoning (low) | 61.1% ±1.3, Mean of 3 runs, range 59.6 to 62.3 | – |
| Reasoning (high) | 63.8% ±2.0, Mean of 3 runs, range 62.3 to 66.2 | – |
Grok 4.6 vs Kimi K2.5: Overview
Grok 4.6 is a proprietary reasoning model from xAI aimed at long-running agentic workflows, coding, and knowledge work. It accepts text and image input and returns text, with a 500,000 token context window and a knowledge cutoff of February 1, 2026. The model exposes an adjustable reasoning budget with low, medium, high, and xhigh settings, where high is the default, and it supports function calling, structured outputs, web and X search, and code execution as documented tool behaviors. Its visual capability covers interpreting images supplied alongside text prompts, which places it in the visual question answering and document understanding family, and it can also return object detection boxes as text coordinates when prompted.
xAI characterizes Grok 4.6 as the result of an extended post-training run over the Grok 4.5 lineage rather than a new pretrained base. The described recipe combines curated model-generated reasoning and technical data, engineering data, a revised optimizer, regenerated supervised fine-tuning trajectories, and reinforcement learning across agent environments spanning knowledge work, coding, kernel optimization, web development, and computer-aided design. Parameter count and architecture specifics are not disclosed. Independent measurement from Artificial Analysis places the model at 61 on its Intelligence Index, five points above Grok 4.5.
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.