Mixture of Experts - Tags

Items tagged with Mixture of Experts (14)

DeepSeek V4 Flash

Models

DeepSeek small agent model: 284B/13B MoE, 1M context, MIT weights, $0.14/$0.28 per 1M tokens.

#DeepSeek

45000000DR: 95AS: 88

GLM-5.3-Flash

Models

Zhipu's first natively multimodal GLM (confirmed as the 'Ox Alpha' stealth model): 320B MoE with 18B active, 1M context, MIT open weights at $0.15/M input.

#Zhipu AI

1000DR: 80AS: 80

LongCat 2.0

Models

Meituan's open-weight MoE model: 1.6T total / ~48B active params, 1M context, MIT. Strong on coding and agentic tasks (2026-08-25).

#Mixture of Experts

1000DR: 80AS: 80

Qwen3.8-Flash-Next

Models

Alibaba's open-weight architecture preview of Qwen4: 125B multimodal MoE with 6B active plus a 51B N-gram table, 262K native context, at $0.16/M input.

#Qwen

1000DR: 80AS: 80

WeLM-617B

Models

WeChat AI's 617B MoE model with 23B active parameters, demonstrating the first sequence-length scaling at frontier scale via Hidden Decoding, powering WeChat's Xiaowei assistant.

#Mixture of Experts

500000DR: 80AS: 75

MiniMax M2.1

Models

Open-source 230B parameter MoE model optimized for multi-language coding, agentic workflows, and real-world development tasks with 74% SWE-bench performance.

#Open Source

500000DR: 68AS: 72

NVIDIA Nemotron 3.5 Lightning 30B A3B

Models

NVIDIA's efficient open-weight 30B MoE hybrid model with 3B active parameters, 1M-token context, and single-GPU deployment for local reasoning and coding.

#NVIDIA

345DR: 100AS: 100

WizardLM-2 8x22B

Models

Previous WizardLM-2 8x22B snapshot. Rechecked: not Microsoft's 2026 default. Drop leftover most-advanced / SoTA open crown.

#Microsoft

100DR: 100AS: 100

DeepSeek V3

Models

Previous DeepSeek V3 snapshot. Rechecked: live path is V4 Flash/Pro. Not a GPT-4o / Claude 3.5 census.

#DeepSeek

135DR: 95AS: 88

DeepSeek V4 represents the next generation of DeepSeek's flagship AI models, building upon the success of V3 with enhanced capabilities in reasoning, multimodal understanding, and agent-based interactions.

#DeepSeek

135DR: 95AS: 88

Gemma 4 26B A4B

Models

Google DeepMind's open-weight 25.2B MoE model with 3.8B active parameters, 256K context, multimodal input, and a commercially permissive Apache 2.0 license.

#Google

1000DR: 80AS: 80

KAT-Coder V2.5

Models

Kwaipilot's open-weight agentic coding model: 35B MoE, 3B active per token, Qwen3.6 base, Apache 2.0, 262K context, top PinchBench tool-use score.

#Open Weights

1000DR: 80AS: 80

Laguna S 2.1

Models

Poolside's open-weight 118B MoE coding model with 8B active parameters, a 1M-token context window, native interleaved reasoning, and an OpenMDW-1.1 license.

#Open Weights

1000DR: 80AS: 80