DeepSeek AI’s Latest Efficiency Boost and Chatbot Personality Breakdown
DeepSeek AI, the Chinese-developed large language model, has drawn attention through technical updates and talks about its user-facing traits. In late September 2025, the company released an experimental version called DeepSeek-V3.2-Exp, which cuts computing costs for AI tasks. This version builds on the earlier V3.1-Terminus model and adds DeepSeek Sparse Attention, or DSA. According to WebProNews reporting, DSA focuses on main parts of data during processing, which helps manage long inputs without the typical slowdowns from standard attention methods.
Sparse Attention: Making AI Faster and Cheaper
The heart of V3.2-Exp is its 685-billion-parameter Mixture-of-Experts setup, combined with DSA’s method of compressing and picking data tokens. DeepSeek’s own platform updates, as covered by WebProNews, show this setup handles long contexts up to three times faster than before. For users, that means API costs for long sequences fall by more than half, which works for apps on limited hardware.
Tests from places like Medium’s Barnacle Goose review (updated November 19, 2025) show the model performs well in reasoning tasks without drops in quality. DataCamp’s tutorial explains the improved handling of long contexts, with examples for adding it to projects. On X, DeepSeek_AI posts point out quality remains steady while long-context performance gets better. CNBC called the release an experimental step beyond V3.1, with faster training and inference times.
- Hugging Face now hosts the model weights for free download and testing.
- DeepSeek’s GitHub repos include code for running inferences.
- vLLM announced support on September 29, 2025, including options for different hardware like Blackwell GPUs, with plans for AMD and TPUs.
Reports from WinBuzzer and Moneycontrol mention the open-source approach and live API changes on Hugging Face. Red Hat Developer explains how vLLM runs it on business hardware, and O’Reilly Radar’s November 2025 article views it as a move toward AI that uses fewer resources in small setups. Chat-Deep.ai’s comparison shows lower latency and better scores for long contexts than V3.1-Terminus. TechRadar and SC Media note security problems tied to censorship in DeepSeek-R1, like broken code from sensitive prompts.
Remio.ai points to the speed improvements for quick inferences, and DeepSeek’s November 18 X update fixed a RoPE problem. vLLM’s TileLang kernel support turns DSA into a good example for future sparse attention projects.
DeepSeek’s Chatbot Style: Talkative and Transparent
Now to how DeepSeek talks to users, a German analysis from taz.de describes it as the talkative one among bots. Released in January 2025, DeepSeek sets itself apart with its “Deep-Think” feature, which shows step-by-step reasoning before answers. This makes responses feel long, like a know-it-all spilling every thought.
The article compares DeepSeek to other bots like ChatGPT (quick but makes mistakes) and Grok (fact-based), and says DeepSeek’s long explanations might come from its Chinese background, where companies answer to the state. It mentions concerns about data use but focuses on the wordy style without proof of hidden reasons. t3n reports on researchers who cut DeepSeek R1’s size by more than half and removed censorship.
DeepSeek’s open model allows changes and broader use, which could push sparse attention in other AI work. Moonshot AI, a related effort, hit a $4B valuation with low-cost models that beat DeepSeek, as Chosun covers.
