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⇱ Kimi K2.6: Specifications and GPU VRAM Requirements


Kimi K2.6

Active Parameters

1T

Context Length

262K

Modality

Multimodal

Architecture

Mixture of Experts (MoE)

License

Modified MIT

Release Date

21 Apr 2026

Knowledge Cutoff

-

Technical Specifications

Attention

Attention Structure

Multi-Layer Attention

Attention Heads

64

Key-Value Heads

64

Attention Head Dimension

-

Position Embedding

ROPE

RoPE Theta

50,000

Sliding Window Attention

No

Sliding Window Size

-

Normalization

RMS Normalization

Activation Function

SwigLU

Dimensions

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

Architecture Diagram

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.

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.


Other Kimi K2.6 Models
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Evaluation Benchmarks

Rank

#11

BenchmarkScoreRank

Web Development

WebDev Arena

1515

11

General Text

Text Arena

1462

16

Rankings

Overall Rank

#11

Coding Rank

#24

GPU Requirements

Full Calculator

Choose the quantization method for model weights

Context Size: 1,024 tokens

1k
128k
256k

VRAM Required:

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