GPT-5.2 Review: OpenAI's Most Capable Model Tested

OpenAI has been on an aggressive release cadence, and GPT-5.2 represents the culmination of everything the company has learned about scaling, instruction following, and multimodal intelligence. After weeks of testing across coding, research, creative writing, and data analysis workflows, we can confidently say this is the most capable proprietary model available today. But capability alone does not tell the full story.

Three Modes, One Model

The headline feature of GPT-5.2 is its three-mode architecture: Instant, Thinking, and Pro. Instant mode is optimized for low-latency responses and everyday chat. It is snappy, conversational, and surprisingly capable for routine queries. Thinking mode engages a chain-of-thought process that substantially improves accuracy on complex math, science, and logic problems. Pro mode takes this further with extended compute, spending significantly more time reasoning before responding.

In practice, the mode system works well. Instant mode handles most daily tasks without noticeable quality loss compared to GPT-4o. Thinking mode is where things get interesting for professionals, delivering step-by-step breakdowns that make it easy to verify the model's logic. Pro mode is expensive (both in tokens and latency) but genuinely impressive for hard problems. We observed it correctly solving multi-step physics derivations and intricate contract analysis tasks that stumped Thinking mode.

The 400K token context window is a meaningful upgrade from GPT-4's limits. We tested it with entire codebases, lengthy legal documents, and concatenated research papers. Retrieval accuracy remained strong through roughly 300K tokens, with some degradation toward the tail end of the window. It is not quite at the level of models offering 1M context, but for most practical use cases, 400K is more than sufficient.

Benchmark Performance

The numbers speak for themselves. GPT-5.2 scores 93.2% on GPQA Diamond, a graduate-level science benchmark that has historically been brutal for AI systems. It achieves a perfect 100% on AIME 2025, the American Invitational Mathematics Examination, confirming its elite mathematical reasoning. On SWE-Bench, the software engineering benchmark that tests real-world bug fixing, it hits 80%, a substantial lead over most competitors.

These are not cherry-picked results. Across a wide battery of evaluations, GPT-5.2 consistently places at or near the top. Where it particularly shines is on tasks that require integrating multiple skills: reading a chart, understanding the underlying data, performing calculations, and explaining the result in plain language.

Strengths

Multi-step projects are where GPT-5.2 truly excels. We tasked it with building a complete data pipeline from raw CSV ingestion through transformation to visualization, and it maintained coherent context across dozens of back-and-forth exchanges. The model remembers project constraints, respects earlier decisions, and builds incrementally rather than starting from scratch each turn.

Spreadsheet and data analysis capabilities are best-in-class. GPT-5.2 can parse complex Excel formulas, generate pivot table logic, and write Python pandas code that correctly handles edge cases like missing data and type mismatches. For business analysts, this alone justifies the subscription.

Code generation across Python, JavaScript, TypeScript, Rust, and Go is excellent. The model produces clean, well-structured code with appropriate error handling. It understands modern frameworks and libraries and rarely hallucinates API calls that do not exist.

Image perception has improved markedly. GPT-5.2 can read handwritten notes, interpret complex diagrams, extract data from photographs of whiteboards, and analyze medical imaging with reasonable accuracy (though obviously not as a diagnostic tool). The multimodal integration feels seamless rather than bolted-on.

Weaknesses

GPT-5.2 is not without flaws. Pricing remains steep, especially for Pro mode, which can burn through API credits quickly on extended reasoning tasks. For individual developers or small teams, the cost can add up fast.

Hallucinations have been reduced but not eliminated. We caught the model confidently fabricating citations in roughly 5-8% of research-oriented queries. The Thinking and Pro modes are better about self-correcting, but users still need to verify factual claims.

Creative writing, while competent, tends toward a recognizable "GPT voice" that can feel formulaic. Writers looking for genuinely distinctive prose may find themselves doing significant editing. The model is better at structure and clarity than at style and voice.

Latency in Pro mode can be frustrating. Responses sometimes take 30-60 seconds, which breaks the flow of interactive work. This is the price of extended reasoning, but it is worth noting for users who value speed.

Pros and Cons

Pros:

• Three-mode system offers flexibility for different task complexities
• Record-breaking benchmark performance across reasoning, math, and coding
• 400K context handles large documents and codebases effectively
• Best-in-class data analysis and spreadsheet capabilities
• Strong multimodal image understanding

Cons:

• Expensive, especially Pro mode for heavy API users
• Hallucinations persist at a low but non-trivial rate
• Creative writing lacks distinctive voice
• Pro mode latency can disrupt interactive workflows
• 400K context trails competitors offering 1M tokens

Verdict: 9.2/10

GPT-5.2 is the best all-around proprietary model available today. It sets the standard for multi-step reasoning, coding, and data analysis. The three-mode architecture is a smart design that lets users trade latency for accuracy on demand. If you need one model that can handle virtually any knowledge-work task at a high level, this is the one to choose. The main caveats are cost and the lingering hallucination problem, but neither is severe enough to dethrone it from the top spot. For teams and professionals who depend on AI for daily work, GPT-5.2 is the new baseline.