Kimi K3.1 Pro Introduced: China's Most Powerful Artificial Intelligence Model

Developed by Moonshot, Kimi K3.1 Pro attracts attention in the world of artificial intelligence with its 2.8 trillion parameters and high performance.

Kimi K3.1 Pro, developed by Moonshot, appears as a new turning point in the world of artificial intelligence. The new model of the China-based company managed to surpass its competitors such as GPT-6 Astra and Claude Fable 5.1 by scoring 55 points on the Artificial Analysis Intelligence Index.

The technical infrastructure of the model offers a very ambitious structure with 2.8 trillion total parameters and 104 billion active parameters per token. The system, which has a local context window of 1 million tokens, uses the NoPE architecture, which completely disables traditional RoPE layers.

Technical Architecture and Performance

This architecture allows the model to perform more flexible context management and increase computational efficiency. MXFP4 weights and MXFP8 activations supported by the quantization-aware training process enable more efficient use of hardware resources.

Kimi K3.1 Pro uses a hybrid structure called Kimi Delta Attention (KDA) instead of the traditional quadratic attention mechanism. This system significantly reduces the size of the KV cache by combining recurrent and hybrid interleaved states in a 3:1 ratio.

Three of the four auxiliary units in the model work like a quick note system that summarizes important information. The fourth unit, thanks to the Gated Multi-Head Latent Attention layer, can take a snapshot of the entire context and compress and decompress the data when needed.

This method makes it possible for the model to operate without losing speed even in long contexts. Some K3.1 Pro is equipped with 24 Gated Multi-Head Latent Attention layers placed every three layers within its 69-layer structure.

Security and Usability

Moonshot stated that cybersecurity-related data was removed from the training phase during the development of the model. The company observed that previous versions of the model were able to detect vulnerabilities in systems such as the Linux kernel and Node.js with high success.

Despite these limitations, the model exhibits a high success rate of 81 percent in the xdev-bench-hard test. Open weights of some K3.1 Pro are planned to be made available to the general public on November 4.

On the pricing side, there is a 33 percent discount compared to the Kimi K3 model. The new prices set for login, logout and cache hit tokens offer a more accessible structure for developers.

The fact that the model ranks 8th in the Code Arena ranking proves that its coding abilities are also quite advanced. How do you think this performance of Kimi K3.1 Pro will change the balance in the artificial intelligence market?