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o3-mini vs DeepSeek-R1: Which One is Safer?

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arxiv 2501.18438 v2 pith:YHRZ7IQ3 submitted 2025-01-30 cs.SE cs.AI

classification cs.SEcs.AI
keywords deepseek-r1o3-minisafetyllmsopenaiautomatedcostperformance
verification ladder T0 review T1 audit T2 compute T3 formal
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The irruption of DeepSeek-R1 constitutes a turning point for the AI industry in general and the LLMs in particular. Its capabilities have demonstrated outstanding performance in several tasks, including creative thinking, code generation, maths and automated program repair, at apparently lower execution cost. However, LLMs must adhere to an important qualitative property, i.e., their alignment with safety and human values. A clear competitor of DeepSeek-R1 is its American counterpart, OpenAI's o3-mini model, which is expected to set high standards in terms of performance, safety and cost. In this technical report, we systematically assess the safety level of both DeepSeek-R1 (70b version) and OpenAI's o3-mini (beta version). To this end, we make use of our recently released automated safety testing tool, named ASTRAL. By leveraging this tool, we automatically and systematically generated and executed 1,260 test inputs on both models. After conducting a semi-automated assessment of the outcomes provided by both LLMs, the results indicate that DeepSeek-R1 produces significantly more unsafe responses (12%) than OpenAI's o3-mini (1.2%).

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Is Reasoning All You Need? Probing Bias in the Age of Reasoning Language Models

    cs.CL 2025-07 conditional novelty 5.0 of 10

    Using the CLEAR-Bias benchmark, the authors find that base language models are generally more robust to bias elicitation than CoT-prompted or reasoning-enabled models.

  2. Reasoner for Real-World Event Detection: Scaling Reinforcement Learning via Adaptive Perplexity-Aware Sampling Strategy

    cs.LG 2025-07 conditional novelty 4.0 of 10

    APARL combines a pass-rate-based adaptive sampler with KL-regularized DAPO reinforcement learning and reports F1 improvements of 17.19% in-domain and 9.59% out-of-domain for customer service anomaly detection.

  3. Efficient Strategy for Improving Large Language Model (LLM) Capabilities

    cs.CL 2025-08 unverdicted novelty 3.0 of 10

    Proposes a combined data-selection, training-strategy, and architecture-adjustment approach to improve LLM capability under resource constraints.

  4. DeepSeek in Healthcare: A Survey of Capabilities, Risks, and Clinical Applications of Open-Source Large Language Models

    cs.CL 2025-06 conditional

    A narrative review of DeepSeek-R1's healthcare capabilities, risks, and applications, without new experiments.

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