GPT-5.2 Benchmarks: Coding, Reasoning, Math, and Multimodal Performance Analysis

OpenAI just dropped GPT-5.2 quickly after Gemini 3 Pro stunned the AI space. It’s seems to be built with an emphasis to power teams pushing real workloads with serious upgrades in performance across coding, math, planning, and multimodal tasks, compared to 5.1 back.

 It’s available now through the API, with Instant, Thinking, and Pro variants rolling out across ChatGPT paid plans.

 If you’re evaluating models for production agents or complex workflow automation, GPT-5.2 is positioned as a serious contender. Here’s what the numbers actually show.

 💡 Want to see how GPT 5.2 compares to Gemini Pro 3, Claude Opus 4.5, or Grok 4.1 for your use case? Compare them in Vellum ! Key observations of reported benchmarks

 While benchmarks are inherently limited and may not fully capture real-world utility, they are our only quantifiable way to measure progress. From the reported data, we can conclude a few things:

 Reasoning: The most compelling data points are the high scores on ARC-AGI-2 (52.9%) and GPQA Diamond (92.4%). This massive leap in abstract reasoning, beating out Gemini 3 Pro (31.1%) and Claude Opus 4.5 (37.6%) indicating a core improvement in academic and abstract reasoning. Coding: A new score of 55.6% on the challenging SWE-Bench Pro benchmark confirms its superior ability to handle real-world software engineering tasks across 4 coding languages rather than simply Python. Math: A perfect score on AIME 2025 is impressive, but the strong performance on the new FrontierMath benchmark (40.3% on Tiers 1-3) is more indicative of a robust intrinsic base for mathematical logic, even without relying on coding tools. Long-Horizon Planning: The results on GDPval are arguably the most indicative of practical utility. Beating or tying industry professionals on 70.9% of knowledge work tasks shows an unprecedented ability to handle long-horizon planning and coherent execution in a professional context. Vision: High scores across MMMU-Pro (86.5%) and Video-MMMU (90.5%) suggest a powerful, natively multimodal architecture capable of reasoning across temporal and spatial dimensions simultaneously.

 Coding capabilities

 SWE-Bench Pro and SWE-Bench Verified evaluate a model's ability to resolve real-world software issues from GitHub repositories. Unlike the Python-only Verified version, SWE-Bench Pro tests across four languages and is designed to be more challenging and industrially relevant.

 __wf_reserved_inherit While GPT-5.2 Thinking sets a new state of the art of 55.6% on SWE-Bench Pro, on the more established SWE-Bench Verified, it scores 80.0%.

 This comes neck to neck with Claude Opus 4.5 (80.9%) and surpasses Gemini 3 Pro (76.2%). This is an improvement from GPT 5.1 (76.3%) in complex, multi-language bug fixing positioning excellently for professional development workflows.

 Reasoning capabilities

 Reasoning benchmarks evaluate a model's ability to solve complex and novel problems. GPQA Diamond assesses PhD-level scientific knowledge

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