DeepSeek Advanced

DeepSeek-V2

DeepSeek-V2 is a 236B-total, 21B-active MoE model with 128K context. Its weights remain available, but the V2 API generation has been superseded.

Language Models (LLMs)Text Open Source

In plain English

What is this model and why does it matter?

DeepSeek-V2 is a 236B-parameter mixture-of-experts model with 21B active parameters and a 128K context window. Its API generation has been superseded.

Historical researchMoE architecture studyExisting compatibility workSelf-hosted experimentation

Model overview

DeepSeek-V2: features, use cases and important details

DeepSeek-V2 is DeepSeek’s 2024 mixture-of-experts language model with 236 billion total parameters and 21 billion activated for each token. DeepSeek’s official repository documents a 128K context window and provides the base and chat-family weights under the DeepSeek Model License.

Why DeepSeek-V2 mattered

The model introduced DeepSeek’s efficiency-focused MoE design and Multi-head Latent Attention approach, aimed at reducing training and inference costs while keeping a large total parameter count. It became the foundation for several V2-series API updates during 2024.

Current status

This is now a historical model generation. DeepSeek’s own December 2024 announcement says the V2 series had reached its final update and that the company was moving to its next foundation-model generation. The public API subsequently moved to DeepSeek-V3, so this page should not show DeepSeek-V2 as a current API model or reuse old V2 token prices.

Who should still consider it

The checkpoint remains relevant for research into DeepSeek’s MoE architecture, reproducibility and compatibility with older workflows. Full-size self-hosting is demanding, and users building a new application should evaluate current DeepSeek models instead.

Get started

How to use this model

Download the official DeepSeek-V2 checkpoint if you specifically need this generation.
Review the DeepSeek Model License.
Provision a large multi-GPU inference environment or a compatible optimized runtime.
Use the checkpoint for research or compatibility testing.
Use newer DeepSeek models for new API integrations.

Copy and try

Example prompts

Compare the efficiency ideas in this MoE architecture with a dense model.
Summarize this long technical paper.
Analyze this Chinese and English technical document.

Capabilities

What it can do

Mixture-of-experts language modeling
128K context
Text generation
Reasoning
Chinese and English tasks

Best for

Practical use cases

Architecture research
Historical model comparison
Long-context experiments
Compatibility testing

Pricing

What does it cost?

Historical open-weight checkpoint under the DeepSeek Model License. The old DeepSeek-V2 API identity has been superseded and should not be given current token pricing.

Simple summaryWeights remain downloadable, but full-size self-hosting is infrastructure intensive. Historical DeepSeek API pricing should not be treated as current.

What stands out

128K context
Efficient MoE design for its scale
Official downloadable weights

Things to consider

Very large infrastructure requirements
Custom model license
Superseded API generation

Limitations

Important restrictions and trade-offs

Base checkpoint is not the same as the historical chat API model
No current DeepSeek-V2 API endpoint should be assumed
Can produce inaccurate output

SimplifyAITools verdict

Our editorial take

Keep as a historical technical profile. DeepSeek-V2 was important for DeepSeek’s MoE architecture, but it is not a current API choice.

References