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MoonshotAI

Moonshot AI: Kimi K2.5

Kimi K2.5 Overview

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.

Kimi K2.5 Interactive Demo

Kimi K2.5 Details & Performance

Details

Resources

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Vision Tasks

Vision LanguageOCRVisual Question AnsweringCaptioning

Features

Multimodal Vision

Usage

Past 30 Days

Performance

Avg. Latency

Arena Rankings

Kimi K2.5 Vision Evals

Visual Understanding

74 models · 67 tasks
HighestLowest
This model#71 of 7435.82% pass rate · better than 1%
Score35.82%pass rate across 67 tasks
Speed14.81savg response per task
Cost$0.0021 / task$0.375 in · $2.02 out / 1M
Tokens2.7K / task1.6K in · 766 out
Score key:≥75%40–74%<40%
CategoryPassedScore
Document Understanding5 / 9
55.6%
Defect Detection7 / 15
46.7%
Object Understanding6 / 14
42.9%
Spatial Understanding5 / 19
26.3%
Object Counting1 / 10
10%

OCR

55 models · 229 tasks
HighestLowest
This model#55 of 5519.65% pass rate · better than 0%
Score19.65%pass rate across 229 tasks
Speed13.09savg response per task
Cost$0.0006 / task$0.375 in · $2.02 out / 1M
Tokens706 / task119 in · 258 out
Score key:≥75%40–74%<40%
CategoryPassedScore
Handwritten Math5 / 10
50%
VQA & Extraction20 / 60
33.3%
Text Recognition8 / 30
26.7%
Focused Scene OCR10 / 99
10.1%
License Plate Recognition2 / 30
6.7%

Scores based on a single evaluation run · Methodology

View all Vision Evals →

Kimi K2.5 Pricing

Kimi K2.5 costs $0.375 per 1M input tokens and $2.02 per 1M output tokens.

Input$0.375 / 1M tokens
Output$2.02 / 1M tokens

Pricing updated Jul 4, 2026

Price vs. performance

Estimated cost per task vs. Visual Understanding score, for this model and others ranked near it. Upper-left is the sweet spot (high quality, low cost).

6 of 6 models plotted

ModelScoreMedian tokensEst. cost / taskCompare
AnthropicClaude Haiku 4.558.2%2.3K$0.0030Compare
OpenAIGPT-5 Nano58.2%2.7K$0.0003Compare
QwenQwen3.5 397B A17B58.2%1.5K$0.0006Compare
GoogleGemini 2.5 Flash55.2%476$0.0005Compare
GoogleGemini 2.5 Flash-Lite53.7%301<$0.0001Compare
MoonshotAIKimi K2.5(this model)35.8%2.7K$0.0021—

Alternatives to Kimi K2.5

Other models worth comparing for similar use cases.

OpenAI
GPT-5.5
GPT-5.5 is a multimodal large language model released by OpenAI on April 23, 2026, engineered for autonomous, multi-step knowledge work and agentic workflows. It accepts text, images, and code as input, featuring enhanced spatial reasoning and visual grounding to support its computer use capabilities for operating software and navigating UI elements. Built to execute complex workflows end-to-end, the model interprets loosely defined tasks, selects appropriate tools, and performs self-verification with minimal user intervention. It is available in a standard version, a Thinking mode for extended reasoning budgets, and a Pro variant that uses parallel test-time compute for maximum precision on complex tasks.Co-optimized with NVIDIA for GB200 NVL72 infrastructure, GPT-5.5 delivers per-token latency comparable to its predecessor GPT-5.4 while maintaining a 1-million-token context window. Despite increased capability, the model achieves greater token efficiency in coding and data analysis workflows, often completing tasks with fewer total tokens than previous versions. OpenAI reports a 60% reduction in hallucination rate compared to GPT-5.4, improving reliability for accuracy-sensitive applications. API access is available via the Responses and Chat Completions endpoints at $5 per million input tokens and $30 per million output tokens, double the unit price of GPT-5.4.
OpenAI
GPT-5
GPT-5, released by OpenAI in August 2025, is a multimodal large language model that advances beyond the GPT-4 family with a new “unified system” architecture. This design allows the model to dynamically choose between fast responses and extended reasoning depending on task complexity. It supports text, code, and images, alongside stronger tool use and agentic workflows, making it more adaptable for real-world problem solving. While its exact context window size is not disclosed, GPT-5 is optimized for long-horizon reasoning and multi-step tool chaining, indicating substantially expanded capacity over its predecessors.The release introduced specialized variants: GPT-5 Pro, offering extended reasoning for complex workflows, and GPT-5 Codex, optimized for advanced coding tasks such as large-scale refactoring and code review. GPT-5 shows benchmark gains in coding, biomedical reasoning, multimodal analysis, and scientific tasks. Developers also gain new controls, such as verbosity and personalization parameters, for greater steerability. With these improvements, GPT-5 positions itself as OpenAI’s most capable and versatile model, suited for enterprise automation, research, healthcare, and sophisticated coding environments.
Qwen
Qwen3 VL 235B A22B Instruct
Qwen3 VL 235B A22B Instruct is a flagship multimodal vision-language model developed by Qwen (Alibaba Cloud), designed for instruction-following tasks that combine advanced text generation with visual understanding. It serves as a high-end open-weight model for developers and researchers building multimodal AI systems that require strong reasoning, perception, and long-context capabilities.The model supports interleaved text and image inputs, very long context windows (up to roughly 256K tokens), and efficient inference through a mixture-of-experts architecture with about 22B active parameters out of 235B total. In today’s landscape, it competes with top-tier proprietary vision-language models while offering the advantages of open weights and flexible deployment. Typical applications include multimodal assistants, document and image analysis, visual reasoning, and large-context instruction-based workflows.

Kimi K2.5 License

Modified MIT

License terms and commercial-use guidance for Kimi K2.5.

License information is provided as a guide and is not legal advice.