Kimi K3 Released: World's First Open-Source 3 Trillion Parameter Model, Performance Approaches Closed-Source Flagships

On July 16, 2026, Moonshot AI officially released Kimi K3, the world's first open-source large model with a parameter scale of 3 trillion, based on a Mixture of Experts (MoE) architecture. The model has a total of 2.8 trillion parameters, 896 experts, with only 16 activated per inference, supporting a 100 million token context and native multimodal visual understanding. The complete model weights will be open-sourced on July 27.

Architecture Innovations

Kimi K3 is built on two proprietary technologies:

Kimi Delta Attention (KDA): A hybrid linear attention mechanism that mixes linear attention and full attention in a 3:1 ratio, reducing KV cache by 75% and increasing decoding throughput by up to 6x.
Attention Residuals (AttnRes): Allows the model to selectively retrieve information across layers, achieving about 25% training efficiency improvement at an additional cost of less than 2%.

Combined with the Stable LatentMoE framework (896 experts, 16 activated) and the Quantile Balancing algorithm, overall scaling efficiency is improved by approximately 2.5x compared to the previous generation Kimi K2. Training uses quantization-aware training (MXFP4 weights + MXFP8 activations), and inference implements a prefix caching scheme for KDA, contributed to the vLLM community, achieving a cache hit rate of over 90% under programming workloads.

Performance

According to the Artificial Analysis Intelligence Index, Kimi K3 scores 57 points, ranking third globally, behind Claude Fable 5 (60 points) and GPT-5.6 Sol (59 points), surpassing all other models.

Code Capabilities

Frontend Code Arena: Topped with 1679 points, surpassing Fable 5 (1631) and GPT-5.6 Sol (1618).
SWE Marathon: 42.0 points, the highest among all models.
Terminal Bench 2.1: 88.3 points, second only to GPT-5.6 Sol.
Program Bench: 77.8 points, slightly above Fable 5's 76.8.

Agent and Knowledge Work

BrowseComp: 91.2 points, first place.
SpreadsheetBench 2: 34.8 points, first place.
Automation Bench: 30.8 points, first place.
AA-Briefcase Elo: 1548 points, second place.

Multimodal

CharXiv: 91.3 points, the highest among open-source models.

Autonomous Engineering Capability Demonstration

GPU Kernel Optimization: In the AttnRes task, K3 designed a two-stage kernel algorithm, compressing forward+backward time from 283.6ms to 114.4ms, with performance close to Fable 5.
Building a GPU Compiler from Scratch: Developed MiniTriton, building a tile-level IR layer and PTX code generation pipeline based on MLIR, with performance matching or exceeding Triton and torch.compile, capable of supporting nanoGPT training convergence.
Self-Designed Chip: Completed chip design within 48 hours using open-source EDA tools and the Nangate 45nm process library, integrating 1.46 million standard cells in 4mm², achieving a decoding throughput of over 8700 tokens/s at 100MHz.

Pricing and Deployment

Kimi K3 API pricing: cache hit input $0.3 per million tokens, regular input $3, output $15. The official claims that the single-task cost is about one-third of Fable 5. The model is now available on Kimi web, App, Kimi Work, Kimi Code, and API, with the highest intensity reasoning mode enabled by default.

Known Limitations

Sensitive to historical thinking content; switching during a session may cause quality instability.
Training focuses on long-range difficult tasks, which may lead to over-autonomous decision-making when encountering ambiguous intent.
Overall user experience still lags behind Fable 5 and GPT-5.6 Sol.