DataLearner logo
DE

DeepSeek-V2-236B-Chat

Chat modelDeepSeek VDeepSeek V2

DeepSeek-V2-MoE-236B-Chat

Release date: 2024-05-06Updated: 2024-06-07 09:09:07592
Parameters
236B
Context length
128K
Chinese support
Supported
Reasoning ability

DeepSeek-V2-MoE-236B-Chat is an AI model published by DeepSeek-AI, released on 2024-05-06, for Chat model, with 236B parameters, and 128K context length, requiring about 472GB storage, with a 17.80 score on Aider-Polyglot.

Data sourced primarily from official releases (GitHub, Hugging Face, papers), then benchmark leaderboards, then third-party evaluators. Learn about our data methodology

DeepSeek-V2-236B-Chat

Model basics

Reasoning traces
Not supported
Thinking modes
Thinking modes not supported
Context length
128K tokens
Max output length
No data
Model type
Chat model
Modality (in / out)
No data
Release date
2024-05-06
Model file size
472GB
MoE architecture
No
Total params / Active params
236B / N/A
Knowledge cutoff
No data
DeepSeek-V2-236B-Chat

Open source & experience

Code license
Weights license
DeepSeek License Agreement- Commercial use permitted
Live demo
No live demo
DeepSeek-V2-236B-Chat

Official resources

Paper
No paper available
DataLearnerAI blog
No blog post yet
DeepSeek-V2-236B-Chat

API details

API speed
No data
No public API pricing yet.
DeepSeek-V2-236B-Chat

Benchmark Results

DeepSeek-V2-236B-Chat currently shows benchmark results led by Aider-Polyglot (52 / 59, score 17.80). This page also consolidates core specs, context limits, and API pricing so you can evaluate the model from benchmark results and deployment constraints together.

Thinking

Agent Level Benchmark

1 evaluations
Benchmark / mode
Score
Rank/total
Aider-Polyglot
Standard Mode
17.80
52 / 59

Compare with other models

No curated comparisons for this model yet.

Want a custom combination? Open the compare tool

DeepSeek-V2-236B-Chat

Publisher

DeepSeek-V2-MoE-236B-Chat

Model Overview

DeepSeek-V2-MoE-236B-Chat is an AI model published by DeepSeek-AI, released on 2024-05-06, for Chat model, with 236B parameters, and 128K context length, requiring about 472GB storage, with a 17.80 score on Aider-Polyglot.

DataLearner on WeChat

Follow DataLearner on WeChat for AI model updates and research notes.

DataLearner WeChat QR code