Parameters
72B
Context Length
131K
Modality
Text
Architecture
Dense
License
MIT License
Release Date
16 Jun 2025
Knowledge Cutoff
-
API Pricing (per 1M)
Input: $0.29 · Output: $1.15
VRAM requirements for different quantization methods and context sizes
1,024 tokens
Consumer
8x RTX 4090
24GB VRAM
Datacenter
3x NVIDIA A100
80GB VRAM
Apple Silicon
2x Apple M3 Max
128GB VRAM
131,072 tokens
Consumer
10x RTX 4090
24GB VRAM
Datacenter
3x NVIDIA A100
80GB VRAM
Apple Silicon
2x Apple M3 Max
128GB VRAM
No evaluation benchmarks for Kimi-Dev-72B available.
Overall Rank
-
Coding Rank
-
Kimi-Dev-72B is an open-weights software engineering model by Moonshot AI built on Qwen2.5-72B and fine-tuned for autonomous issue resolution. Utilizing a dual BugFixer/TestWriter architecture, it specializes in codebase localization and verified code patches.
Attention
Attention Structure
Multi-Head Attention
Attention Heads
64
Key-Value Heads
8
Attention Head Dimension
-
Position Embedding
Absolute Position Embedding
RoPE Theta
1,000,000
Sliding Window Attention
No
Sliding Window Size
131,072
Sliding Window Ratio
-
Linear Attention
-
Linear Attention Ratio
-
Normalization
RMS Normalization
Activation Function
SwigLU
Dimensions
Hidden Dimension Size
8,192
Number of Layers
80
FFN Intermediate Size (Dense)
29,568
Multi-Token Prediction Heads
-
Tokenizer
Vocabulary Size
152,064
Moonshot AI's Kimi model family, exemplified by Kimi K2, employs a Mixture-of-Experts architecture with one trillion total parameters. Designed for natural language generation and agentic capabilities, it features a 128K token context window. The models are open-weight and optimized with the Muon optimizer for stable training.
APX AI
Online