DeepSeek Model Criticisms and Concerns | Generated by AI
Question: What are some unpopular opinions about DeepSeek V3.2 and its Speciale model?
Answer:
DeepSeek V3.2, released in late 2025, is an open-source large language model from the Chinese AI firm DeepSeek AI, featuring sparse attention mechanisms for efficiency and strong benchmark performance in reasoning tasks like math olympiads. Its Speciale variant is a fine-tuned version optimized for deep reasoning, though it lacks tool-calling support. While the model has garnered praise for its cost-effectiveness and accessibility, it has also sparked debate. Below, I’ve compiled key unpopular opinions drawn from developer forums, social media, and expert analyses. These views challenge the hype around its “frontier-level” capabilities, often highlighting gaps between benchmarks and real-world utility.
Unpopular Opinions on DeepSeek V3.2
- It’s overhyped and benchmaxxed for leaderboards, not practical use: Despite topping charts like AIME (mid-90s scores) and Codeforces (near 2700 Elo), early testers report mediocre real-world performance in coding, agent workflows, and daily tasks—often half as effective as OpenAI or Google models. The focus on synthetic data and RL (10% of pre-training budget) boosts metrics but leads to verbose outputs and narrow knowledge, making it feel like “great numbers, mid experience.”
- DeepSeek models, including V3.2, are overrated and hallucinate excessively: Popular for creative writing and roleplay, they’re criticized for constant hallucinations, stubbornness in following story directions, and looping responses. This makes them unreliable for nuanced tasks like RP or planning, where models like Claude feel more “human” with better narrative polish and less repetition.
- It’s not a true efficiency breakthrough—it’s just clever optimization of known techniques: Claims of building it cheaply (e.g., under $6M) ignore subsidies, IP questions (e.g., similarities to OpenAI’s o1), and the fact that sparse attention (DSA) and RL distillation are industry-standard tweaks, not novel inventions. It’s more “bringing pieces together” than revolutionizing AI, and the low cost risks undercutting U.S. firms without addressing core limits like energy inefficiency compared to human cognition.
- DeepSeek V3.2 is dangerous due to potential backdoors and censorship: As a Chinese model, it’s viewed as a security risk—hackable for tracing users—and heavily biased toward CCP narratives (e.g., dodging Tiananmen Square queries while answering similar ones about other countries). This erodes trust, pushes agendas subtly, and makes it unsuitable for sensitive work, despite its open-source appeal for uncensored local runs.
- It’s not a real threat to closed-source giants like OpenAI: While cheaper and open, it trails in usability, context length (shorter than 128k rivals), multimodality, and end-to-end problem-solving. The viral buzz is geopolitical theater, not a paradigm shift—U.S. firms can just adapt its tricks, and consumer reluctance to Chinese APIs limits adoption.
Unpopular Opinions Specific to the Speciale Model
- Speciale excels in benchmarks but fails at simple pattern recognition: It ties GPT-5 on hard reasoning (e.g., 5/6 IMO golds) but struggles with “easier” tasks needing no deep thought, like basic patterns—mirroring flaws in advanced models like o3 Pro. Testers call it “amazing on paper” but disappointing in mixed workflows.
- It’s too slow and buggy for real adoption: At ~30 tokens/second with long response times, plus glitchy tool-calling and overthinking loops, Speciale feels unpolished despite its reasoning focus. Early users prefer the base V3.2 for speed, calling Speciale a “crawling Ferrari” that’s great for niche math/coding but impractical daily.
- Speciale lacks “soul” and feels utilitarian, not creative: Optimized for reasoning, it prioritizes raw retrieval over aesthetic communication—responses are efficient but bland, like “Android vs. iOS” in AI form. This makes it solid for agents but inferior for writing, recipes, or vacations compared to more “crafted” models.
Overall, these opinions paint DeepSeek V3.2 and Speciale as a bold open-source win for efficiency and math/coding niches, but not the AGI-killer some claim. The model’s MIT-licensed weights on Hugging Face invite experimentation, yet skeptics argue it exposes AI’s broader limits: benchmarks ≠ utility, and cheap scale doesn’t fix hallucinations or biases.
Deepseek releases cheap general imo level model before openAI (https://www.reddit.com/r/singularity/comments/1pbazan/deepseek_releases_cheap_general_imo_level_model/)
deepseek-ai/DeepSeek-V3.2 · Hugging Face (https://www.reddit.com/r/LocalLLaMA/comments/1pb9xm3/deepseekaideepseekv32hugging_face/)
*Opinion: Deepseek models are overrated* (https://www.reddit.com/r/SillyTavernAI/comments/1kfxdc1/opinion_deepseek_models_are_overrated/)
*Unpopular Opinion: Deepseek has rat-effed OpenAI’s 2025 business model* (https://www.reddit.com/r/ChatGPT/comments/1ibj4io/unpopular_opinion_deepseek_has_rateffed_openais/)
*DeepSeek is highly biased, don’t use it* (https://medium.com/data-science-in-your-pocket/deepseek-is-highly-biased-dont-use-it-2cb0358647f9)
*There’s real technical achievement, but the marketing is exaggerated* (@witec on X)
DeepSeek V3.2 looks wild on benchmarks… but early testers aren’t impressed (@VraserX on X)
deepseek feels like it’s just optimizing for raw information retrieval (@signulll on X)