HomeDeepSeek-V3.2-Exp
671B
Model parameter quantity
DeepSeek
Affiliated organization
Open Source
License Type
September 29, 2025
Release time
Related figures
Model Introduction
The latest experimental version of Deepseek V3 series models.
Comprehensive score
Language dialogue
Knowledge reserve
Reasoning association
Mathematical calculation
Code writing
Command following
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Language comprehension ability
Capable of understanding complex contexts and generating logically coherent sentences, though occasionally off in tone control.
8.0
Knowledge coverage scope
Possesses core knowledge of mainstream disciplines, but has limited coverage of cutting-edge interdisciplinary fields.
8.9
Reasoning ability
Unable to maintain coherent reasoning chains, often causing inverted causality or miscalculations.
6.3
Model comparison
DeepSeek-V3.2-Exp 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-V3.2-Exp vs Hunyuan-T1-20250822
The deep reasoning model independently developed by Tencent adopts the version number hunyuan-t1-20250822.
DeepSeek-V3.2-Exp vs Spark-X1
The inference model Spark X1 released by iFlytek, on the basis of leading domestic mathematical tasks, benchmarks the performance of general tasks such as inference, text generation, and language understanding against OpenAI o1 and DeepSeek R1.
Related model
DeepSeek-V3.2
The latest version of Deepseek V3 series models.
DeepSeek-R1-0528
The latest version of Deepseek R1.
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-R1-0528
The latest version of Deepseek R1.
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.
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