GPT-5.2 is a competent math model that handles calculus, statistics, and applied mathematics reliably. Here's an honest assessment of where it stands against the math-specialized competition.
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GPT-5.2 is a solid choice for applied mathematics, statistics, and computational math, particularly when combined with code execution. It handles calculus, probability, linear algebra, and data-oriented math problems well. However, it is not the top model for pure mathematical reasoning: DeepSeek V3 dominates on competition-level problems and formal proofs, while Claude Sonnet 4.5 provides clearer step-by-step explanations for educational use. GPT-5.2 earns its place through reliability and its ability to seamlessly combine mathematical reasoning with code.
GPT-5.2 excels at the kind of math that shows up in real-world applications: statistics, probability, optimization problems, and financial modeling. It handles these tasks more reliably than it handles abstract theoretical math.
GPT-5.2 can derive a formula and immediately produce working Python, R, or MATLAB code to compute it. This tight coupling between mathematical reasoning and code generation is a significant productivity advantage for engineers and data scientists.
When you need math results in a specific format, such as LaTeX, JSON with computed values, or structured tables, GPT-5.2 follows formatting instructions more consistently than other models, reducing manual cleanup.
From basic algebra through graduate-level statistics, GPT-5.2 covers the full breadth of standard mathematics curricula. It handles standard textbook problems across all common topics with consistent accuracy.
On olympiad-style and competition math, GPT-5.2 trails DeepSeek V3 significantly. If your use case involves advanced combinatorics, number theory proofs, or mathematical competition prep, DeepSeek is the better model.
GPT-5.2 sometimes skips intermediate steps or makes logical leaps in proofs. Claude Sonnet 4.5 provides more thorough, pedagogically clear derivations that are better suited for educational contexts.
Like all LLMs, GPT-5.2 can make arithmetic errors in multi-step calculations. For critical computations, always verify results with code execution or use GPT-5.2's own code interpreter to double-check numerical answers.
Ask GPT-5.2 to write and execute code for any computation that involves more than a few arithmetic steps to avoid calculation errors.
For proofs and derivations, explicitly request 'show every intermediate step' to reduce the chance of logical gaps.
Use LLMWise Compare mode to send hard math problems to both GPT-5.2 and DeepSeek V3 simultaneously and cross-check their solutions.
Specify the output format (LaTeX, plain text, or code) in your prompt to get results you can directly paste into your workflow.
How GPT-5.2 stacks up for math workloads based on practical evaluation.
DeepSeek V3
Compare both models for math on LLMWise
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