Model
Model explorer
Kimi K2
OPENMoonshot AI · Kimi K2 family · released Jul 11, 2025
1T-parameter MoE agentic model trained with the MuonClip optimizer; open-sourced under a modified MIT license.
ReasoningCodingVisionFunction callingTool useAgentic
1526.6
Elo · rank #117
Parameters
1000B
Active params
32B (MoE)
Context
128K tokens
Architecture
Mixture-of-Experts, 384 experts (8 active + 1 shared/token), 61 layers, MLA attention
License
Modified MIT License
Languages
—
API price (in/out)
$0.57 / $2.3
Modalities
text
Benchmark results
Bar shows position within the tracked field; marker = field best
best: Claude Opus 4.5 · 89.4%
best: GPT-5.2 · 100.0%
best: Claude Fable 5 · 1505
best: Hunyuan-A13B · 78.3%
best: GPT-5.6 · 94.6%
best: Claude Sonnet 5 · 57.4%
best: Gemma 4 26B A4B · 98.5%
best: DeepSeek-V4-Pro (Think Max) · 93.5%
best: GPT-5 · 99.4%
best: Claude Fable 5 · 91.5%
best: Qwen3.7-Max · 95.0%
best: OpenAI o3 · 92.9%
best: GPT-4.5 · 62.5%
best: Qwen3.7-Max · 73.6%
best: Claude Fable 5 · 95.0%
best: Trinity-Large-Thinking · 88.0%
best: Claude Sonnet 4.6 · 91.7%
best: Claude Opus 4.6 · 99.3%
best: Claude Opus 4.5 (High) · 59.3%
Run it locally
VRAM @ Q4
600 GB
VRAM @ FP16
2000 GB
Fits on (Q4)
Multi-node cluster required
Does not fit on a single RTX 4090 (24GB); practical Q4 quants need ~250-600GB combined RAM/VRAM, with heavy CPU/SSD offload reported at only ~5-10 tok/s
Quantizations
GGUF
Kimi K2 family
Elo progression across releases
API price $0.57/$2.3 · each benchmark row carries its own source badge (see methodology)