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Kimi K2.6

Active Parameters

1T

Context Length

262K

Modality

Multimodal

Auxiliary Parameters

450M

Architecture

Mixture of Experts

KV Cache Rate

69 KB/tok

License

Modified MIT

Release Date

21 Apr 2026

Knowledge Cutoff

-

API Pricing (per 1M)

Input: $0.95 · Output: $4.00

Hardware Sizing

Precision

Context

Workload Memory: 1938.09 GB

FP16 · 8K context

Base Weights: 1922.25 GB

KV Cache: 13.34 GB

Runtime & Graphs: 2.50 GB

Minimum Required Hardware

Cluster: 16x NVIDIA H200 141GB

1938.1 / 2030.4 GB usable · TP 8 · PP 2

Determine requirements for datacenter hardware:

Upgrade to Pro

Architecture Diagram

Input TokensToken EmbeddingPosition: RoPEHidden: 7.2k · Context: 262K · Vocab: 163.8kx 61 layersRMSNormPre-AttentionMulti-Layer Attention64Q / 64KV headsHead dim: 112+RMSNormPre-FFNSparse MoE FFN (9/384 experts)SwiGLUIntermediate: 2k+Final RMSNormOutput Logits

Evaluation Benchmarks

Rank

#103

BenchmarkScoreRank

Graduate-Level QA

GPQA

0.905

22

0.79

31

Agent Arena

Agent Arena

-0.29

37

Agentic Coding

LiveBench Agentic

0.47

44

0.79

52

General Text

Text Arena

1461

52

auto

0.65

53

0.71

53

0.84

53

LiveBench Average

LiveBench Average

0.71

53

Web Development

WebDev Arena

1509

54

0.62

64

Agentic Index

Artificial Analysis
auto

0.20

79

Intelligence Index

Artificial Analysis
auto

0.27

Standard

0.24

95

124

Rankings

Overall Rank

#103

Coding Rank

#56

About Kimi K2.6

Kimi K2.6 is Moonshot AI's open-source native multimodal agentic model with 1T total parameters and 32B activated per token. Built on a hybrid MoE architecture with 61 layers, 384 routed experts + 1 shared, 8 selected per token, MLA attention, and a dedicated MoonViT vision encoder (400M params). Delivers state-of-the-art performance in long-horizon coding (SWE-Bench Pro 58.6%, SWE-Bench Verified 80.2%), agentic workflows (BrowseComp 83.2%, AIME 2026 96.4%, GPQA-Diamond 90.5%), and visual reasoning (MMMU-Pro 79.4%). Supports 256K native context, thinking/instant modes, and thinking preservation across turns. Scalable to 300 sub-agents executing 4,000 coordinated steps. Released April 21, 2026 under Modified MIT License.

Technical Specifications

Attention

Attention Structure

Multi-Layer Attention

Attention Heads

64

Key-Value Heads

64

Position Embedding

ROPE

RoPE Theta

50,000

Normalization

RMS Normalization

Activation Function

SwigLU

Dimensions

Auxiliary Parameters

450M

Hidden Dimension Size

7,168

Number of Layers

61

FFN Intermediate Size (Dense)

2,048

Multi-Token Prediction Heads

0

Tokenizer

Vocabulary Size

163,840

Mixture of Experts

Total Expert Parameters

32.0B

Number of Experts

384

Active Experts

9

Shared Experts

1

FFN Intermediate Size (per Expert)

2,048

Dense Layers Before MoE

1

About Kimi K2.6

Kimi K2.6 is Moonshot AI's latest open-source native multimodal agentic model, advancing practical capabilities in long-horizon coding, coding-driven design, proactive autonomous execution, and swarm-based task orchestration. It transforms simple prompts and visual inputs into production-ready interfaces and full-stack workflows, and can scale horizontally to 300 sub-agents executing 4,000 coordinated steps. Built on the same hybrid MoE architecture as Kimi K2.5 with a dedicated MoonViT vision encoder.


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