O3 vs O4-mini: Rethinking AI with Smarter Reasoning
The o3 and o4-mini models released by OpenAI are considered among the most advanced general-purpose reasoning models to date, featuring powerful visual understanding and tool-using capabilities. Research shows that O3 excels in coding, mathematics, science, and visual tasks, while O4-mini is better suited for fast-response and cost-sensitive scenarios. Both models demonstrate significant improvements in performance and safety, not only reducing major error rates effectively but also enhancing safeguards against high-risk content through more robust refusal mechanisms.
Model functions and features
O3 and O4-mini are designed to tackle complex problems, equipped with a range of practical tools such as web search, Python analysis, and image generation. O3 is well-suited for handling highly complex reasoning tasks, while O4-mini is optimized for high-volume, low-latency environments, offering efficient and cost-effective solutions. Both models can reference past conversations to enable more personalized interactions. With tool integration, they can access real-time information and perform data processing more efficiently, significantly enhancing their overall reasoning capabilities.
Performance improvements
Compared to the earlier o1 model, o3 has reduced major errors by 20%, showing outstanding performance particularly in programming, business, and creative ideation. o4-mini outperforms o3-mini in non-STEM tasks and data science, and its efficiency allows for higher usage limits. These improvements make the models more appealing for applications that demand high accuracy and efficiency.
Additionally, both models have made significant strides in instruction following and conversational fluency, enabling better understanding of user intent and more natural interactions. For example, they can reference previous conversations to deliver more personalized responses.
Benchmark results
To evaluate the performance of these new models, OpenAI provides detailed comparisons across multiple benchmarks, covering areas such as math, science, coding, and visual reasoning. Here are the results for key benchmarks:
OpenAI
AIME : Tests advanced math skills; O4-mini achieved accuracy rates of 93.4% in 2024 and 92.7% in 2025.
GPQA : Focuses on PhD-level science questions; O3 scored 83.3% without tools, while O4-mini scored 81.4%.
MMMU Assesses college-level visual problem solving; O3 and O4-mini scored 82.9% and 81.6%, respectively.
MathVista: Evaluates visual mathematical reasoning; O3 achieved 86.8%, and O4-mini 84.3%.
CharXiv-Reasoning: Tests scientific figure interpretation; O3 scored 78.6%, O4-mini 72.0%.
SWE-Bench: Validates software engineering tasks; O3 scored 69.1%, and O4-mini 68.1%.
Deep Research: Tackles interdisciplinary expert-level questions.
These results highlight the strong performance of O3 and O4-mini across multiple domains—especially when enhanced by tool use.
Safety measures
Safety is a top priority for OpenAI. The new models include new rejection hints for biorisk, malware, and jailbreaking to prevent the generation of harmful or dangerous content. Inference LLM monitoring successfully flagged approximately 99% of dangerous conversations in human red team testing. According to OpenAI’s Readiness Framework (updating our Readiness Framework), these models all fell below the “high” threshold in the biological and chemical risk, cybersecurity, and AI self-improvement categories. Detailed safety results can be found in the system cards (o3 and o4-mini system cards).
Biorisk prevents the model from generating harmful content related to biological threats, malware prevents the generation of malicious code, and jailbreaking prevents the model from being manipulated to perform unintended tasks. These measures are implemented through the reconstruction of secure training data and system-level mitigations to ensure the model performs well under the most rigorous security tests.
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