moonshotai/Kimi-K2.5

📰 Tech Blog | 📄 Paper

0. Changelog

• 2026.1.29:
• The default system prompt might cause confusion to users and unexpected behaviours, so we remove it.
• The token ` is incorrect; it has been replaced with ` in the chat template.

1. Model Introduction

Kimi K2.5 is an open-source, native multimodal agentic model built through continual pretraining on approximately 15 trillion mixed visual and text tokens atop Kimi-K2-Base. It seamlessly integrates vision and language understanding with advanced agentic capabilities, instant and thinking modes, as well as conversational and agentic paradigms.

Key Features

• Native Multimodality: Pre-trained on vision–language tokens, K2.5 excels in visual knowledge, cross-modal reasoning, and agentic tool use grounded in visual inputs.
• Coding with Vision: K2.5 generates code from visual specifications (UI designs, video workflows) and autonomously orchestrates tools for visual data processing.
• Agent Swarm: K2.5 transitions from single-agent scaling to a self-directed, coordinated swarm-like execution scheme. It decomposes complex tasks into parallel sub-tasks executed by dynamically instantiated, domain-specific agents.

2. Model Summary

3. Evaluation Results

Benchmark Kimi K2.5 (Thinking) GPT-5.2 (xhigh) Claude 4.5 Opus (Extended Thinking) Gemini 3 Pro (High Thinking Level) DeepSeek V3.2 (Thinking) Qwen3-VL- 235B-A22B- Thinking

Reasoning & Knowledge

HLE-Full 30.1 34.5 30.8 37.5 25.1† -

HLE-Full (w/ tools) 50.2 45.5 43.2 45.8 40.8† -

AIME 2025 96.1 100 92.8 95.0 93.1 -

HMMT 2025 (Feb) 95.4 99.4 92.9* 97.3* 92.5 -

IMO-AnswerBench 81.8 86.3 78.5* 83.1* 78.3 -

GPQA-Diamond 87.6 92.4 87.0 91.9 82.4 -

MMLU-Pro 87.1 86.7* 89.3* 90.1 85.0 -

Image & Video

MMMU-Pro 78.5 79.5* 74.0 81.0 - 69.3

CharXiv (RQ) 77.5 82.1 67.2* 81.4 - 66.1

MathVision 84.2 83.0 77.1* 86.1* - 74.6

MathVista (mini) 90.1 82.8* 80.2* 89.8* - 85.8

ZeroBench 9 9* 3* 8* - 4*

ZeroBench (w/ tools) 11 7* 9* 12* - 3*

OCRBench 92.3 80.7* 86.5* 90.3* - 87.5

OmniDocBench 1.5 88.8 85.7 87.7* 88.5 - 82.0*

InfoVQA (val) 92.6 84* 76.9* 57.2* - 89.5

SimpleVQA 71.2 55.8* 69.7* 69.7* - 56.8*

WorldVQA 46.3 28.0 36.8 47.4 - 23.5

VideoMMMU 86.6 85.9 84.4* 87.6 - 80.0

MMVU 80.4 80.8* 77.3 77.5 - 71.1

MotionBench 70.4 64.8 60.3 70.3 - -

VideoMME 87.4 86.0* - 88.4* - 79.0

LongVideoBench 79.8 76.5* 67.2* 77.7* - 65.6*

LVBench 75.9 - - 73.5* - 63.6

Coding

SWE-Bench Verified 76.8 80.0 80.9 76.2 73.1 -

SWE-Bench Pro 50.7 55.6 55.4* - - -

SWE-Bench Multilingual 73.0 72.0 77.5 65.0 70.2 -

Terminal Bench 2.0 50.8 54.0 59.3 54.2 46.4 -

PaperBench 63.5 63.7* 72.9* - 47.1 -

CyberGym 41.3 - 50.6 39.9* 17.3* -

SciCode 48.7 52.1 49.5 56.1 38.9 -

OJBench (cpp) 57.4 - 54.6* 68.5* 54.7* -

LiveCodeBench (v6) 85.0 - 82.2* 87.4* 83.3 -

Long Context

Longbench v2 61.0 54.5* 64.4* 68.2* 59.8* -

AA-LCR 70.0 72.3* 71.3* 65.3* 64.3* <td align="cente