DeepSeek-V2-Chat
236B
Model parameter quantity
DeepSeek
Affiliated organization
Open Source
License Type
May 5, 2024
Release time
Model Introduction
DeepSeek-V2 is a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. It comprises 236B total parameters, of which 21B are activated for each token. Compared with DeepSeek 67B, DeepSeek-V2 achieves stronger performance, and meanwhile saves 42.5% of training costs, reduces the KV cache by 93.3%, and boosts the maximum generation throughput to 5.76 times.
Comprehensive score
Language dialogue
Knowledge reserve
Reasoning association
Mathematical calculation
Code writing
Command following
Language comprehension ability
Often makes semantic misjudgments, leading to obvious logical disconnects in responses.
5.0
Knowledge coverage scope
Has significant knowledge blind spots, often showing factual errors and repeating outdated information.
6.3
Reasoning ability
Unable to maintain coherent reasoning chains, often causing inverted causality or miscalculations.
4.1
Model comparison
DeepSeek-V2-Chat vs Qwen2.5-7B-Instruct
Like Qwen2, the Qwen2.5 language models support up to 128K tokens and can generate up to 8K tokens. They also maintain multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.
DeepSeek-V2-Chat vs Doubao-1.5-thinking-pro-250415
The new deep thinking model Doubao-1.5 performs outstandingly in professional fields such as mathematics, programming, scientific reasoning, and general tasks such as creative writing. It has reached or is close to the industry's top tier level on multiple authoritative benchmarks such as AIME 2024, Codeforces, and GPQA
DeepSeek-V2-Chat vs Step-1-8K
Step-1-8K is an API model produced by Step Star, with the model version number being step-1-8k.
Related model
DeepSeek-V2-Chat-0628
DeepSeek-V2 is a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. It comprises 236B total parameters, of which 21B are activated for each token. Compared with DeepSeek 67B, DeepSeek-V2 achieves stronger performance, and meanwhile saves 42.5% of training costs, reduces the KV cache by 93.3%, and boosts the maximum generation throughput to 5.76 times.
DeepSeek-V2.5
DeepSeek-V2.5 is an upgraded version that combines DeepSeek-V2-Chat and DeepSeek-Coder-V2-Instruct. The new model integrates the general and coding abilities of the two previous versions.
DeepSeek-V3-0324
DeepSeek-V3 outperforms other open-source models such as Qwen2.5-72B and Llama-3.1-405B in multiple evaluations and matches the performance of top-tier closed-source models like GPT-4 and Claude-3.5-Sonnet.
DeepSeek-V2-Lite-Chat
DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model presented by DeepSeek, DeepSeek-V2-Lite is a lite version of it.
DeepSeek-R1
DeepSeek-R1 is a model trained through large-scale Reinforcement Learning (RL) without using Supervised Fine-Tuning (SFT) as an initial step. Its performance in mathematics, coding, and reasoning tasks is comparable to that of OpenAI-o1.
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