Kimi 2.5 LLM Full Evolution: k0-math Reasoning Model & Multimodal Capabilities Leap

In today’s fiercely competitive Chinese LLM market, Moonshot AI has once again delivered a blockbuster result.

Recently, Kimi welcomed a comprehensive version iteration (widely known in the industry as Kimi 2.5). This update is not just a stack of parameters, but a qualitative leap in reasoning capability and multimodal understanding by introducing Reinforcement Learning at the underlying architecture level.

1. Core Breakthrough: k0-math Reasoning Model

The most eye-catching focus of this update is undoubtedly the release of the k0-math model. This is Kimi’s first reasoning model adopting the Reinforcement Learning technical route, aiming directly at OpenAI’s o1 series.

“Thinking” Like a Human

k0-math no longer just predicts the next word based on probability but introduces a mechanism similar to Chain of Thought.

• Deep Thinking: Before outputting the final answer, the model performs multi-step logical deduction internally.
• Self-Correction: Faced with complex math problems or code logic, the model can self-verify, reflect, and correct its path just like a human.

Test Performance: In multiple benchmark tests, the math problem-solving and logical reasoning capabilities demonstrated by k0-math are already comparable to OpenAI o1-mini or even o1-preview, marking a key step for Chinese LLMs in the field of “Deep Reasoning”.

2. Evolution of Vision and Hearing: Full Multimodal Capabilities

Kimi 2.5 has also become more adept at handling non-text information.

• Long Video Understanding: The current Kimi is like a stenographer with a photographic memory. You can throw it a meeting recording or tutorial video lasting several hours, and it can quickly extract key points, summarize abstracts, and even answer questions about video details.
• Complex Chart Parsing: For financial practitioners or researchers, Kimi can now accurately identify complex tables and trend charts in financial reports and directly extract and analyze data, significantly improving work efficiency.

3. Consolidation and Deepening of Long-Context Advantage

As Kimi’s “signature skill”, Long Context capability has been further optimized in version 2.5.

• Faster Response: While maintaining a 2 million token context window, the Time To First Token (TTFT) and overall reasoning speed have improved significantly, saying goodbye to the long wait when processing long documents.
• Needle In A Haystack: Even when retrieving a tiny detail from massive information, Kimi 2.5’s accuracy is near perfect, with a more solid memory.

4. Intelligent Search Capability

Network search has always been Kimi’s strength, and the new version makes this experience even smoother.

• Structured Integration: Kimi no longer just lists search results but performs cross-verification, deduplication, and logical reorganization of multi-source information, directly providing a clear and deep answer.
• Precise Sourcing: Every point is backed by clear source links, ensuring information transparency and verifiability.

Summary: From “Reader” to “Thinker”

If the previous Kimi was a “Super Reader” with extremely fast reading speed and superb memory, then Kimi 2.5 equipped with k0-math is evolving into a “Thinker” capable of handling complex logic and deep planning.

For developers and ordinary users, Kimi’s evolution means it is no longer just a chatbot or document summary tool, but a true productivity partner capable of assisting in solving complex math problems, writing high-quality code, and analyzing multimodal data.

Reference: This article is based on Moonshot AI’s recently released technical updates and public evaluations.