GPT-5.2 Technical Review: OpenAI's Best Model

GPT-5.2 dropped on December 11, 2025, and it arrives into what can only be described as an unusually crowded sky.

Google's Gemini 3 Pro had landed just three weeks prior. Anthropic's Claude Opus 4.5 followed six days later. DeepSeek's V3.2-Speciale was already posting gold-medal performances on IMO and IOI. And somewhere in Cupertino, reports suggest Apple is preparing its own frontier model announcement.

OpenAI's response? A "code red" memo from Sam Altman, followed by an accelerated release of what the company calls "the most capable model series yet for professional knowledge work."

Having spent the past 48 hours putting GPT-5.2 through its paces across coding, reasoning, and agentic workflows, I can confirm: the hyperbole is, for once, largely justified.

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Architecture and Model Variants#

GPT-5.2 ships in three distinct configurations, each optimized for different use cases:

The tiered approach mirrors what we've seen from DeepSeek's Speciale and Anthropic's extended thinking modes, but OpenAI's implementation feels more polished for production use. You're not choosing between "fast and dumb" or "slow and smart"—you're selecting the appropriate reasoning budget for your task.

The headline numbers are impressive:

• 400,000-token context window — up from 128K in GPT-5.1
• 128,000 max output tokens — enabling full application generation in single passes
• 30% reduction in hallucinations compared to 5.1
• API pricing: $1.75/M input, $14/M output

That context window deserves attention. Four hundred thousand tokens means you can ingest entire codebases, hundreds of documents, or complete research corpora in a single prompt. For agentic workflows that require maintaining state across complex multi-step operations, this changes the calculus significantly.

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Benchmark Performance: The Numbers#

Let me be direct about what these benchmarks show and what they don't.

Mathematical Reasoning#

On AIME 2025, GPT-5.2 Thinking achieves 100% Pass@1. This is not a typo. The model solves every problem on first attempt, matching DeepSeek-V3.2-Speciale's 96% and exceeding Gemini 3 Pro's ~95%.

This represents a genuine inflection point. Two years ago, GPT-4 struggled with competition mathematics. Now we have multiple models saturating high-school Olympiad benchmarks.

Coding and Software Engineering#

The picture is more nuanced here:

Claude Opus 4.5 still leads on SWE-bench Verified—the first model to break 80%—but GPT-5.2 sets a new state-of-the-art on SWE-bench Pro, a harder variant that better reflects real-world bug-fixing complexity.

In my own testing on production codebases, GPT-5.2 Thinking consistently produces more complete implementations than 5.1. The extended output window means it can generate entire feature implementations rather than truncating mid-function.

General Reasoning#

Here's where things get interesting:

GDPval (knowledge work across 44 occupations): GPT-5.2 Thinking beats or ties top industry professionals on 70.9% of comparisons. This is the benchmark OpenAI is most excited about—it measures practical, professional task completion rather than academic puzzles.

ARC-AGI-1: GPT-5.2 Pro scores 90.5%, the first model to cross the 90% threshold on this general reasoning benchmark. For context, GPT-5.1 scored around 18%, and Gemini 3 Pro's Deep Think mode reaches 45%.

That 90.5% number warrants scrutiny. ARC-AGI measures abstract pattern recognition and novel problem-solving—capabilities that have historically been weakest in large language models. Either OpenAI has made a genuine architectural breakthrough, or they've found training data that leaks benchmark solutions. I lean toward the former given the across-the-board improvements, but this deserves independent verification.

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Practical Performance: What I Actually Found#

Benchmarks tell one story. Practical use tells another.

Code Generation#

I tested GPT-5.2 Thinking on several real-world tasks from my development work:

Task 1: Refactoring a 2,000-line React component into smaller modules

GPT-5.2 correctly identified all the logical boundaries, proposed a sensible file structure, and generated the refactored code with proper imports. The 128K output limit meant it could complete the entire refactor in one pass rather than requiring multiple follow-ups.

5.1 would have truncated around the third module and lost context for the remaining imports.

Task 2: Debugging a race condition in a WebSocket handler

I provided the full file (about 800 lines) plus error logs. GPT-5.2 identified the race condition on first attempt, explained why it occurred (a missing mutex around connection state), and provided a fix that actually worked.

More importantly, it caught a secondary issue I hadn't noticed—a potential memory leak in the reconnection logic.

Task 3: Implementing a complex API integration from documentation

I pasted 15,000 tokens of API documentation and asked for a TypeScript client. The generated code was production-ready: proper error handling, retry logic, type definitions matching the API spec, and even helpful comments explaining rate limit considerations.

Extended Reasoning#

The Thinking variant's extended reasoning is genuinely useful for complex problems. When working through a database schema migration that needed to preserve backwards compatibility, the model:

1. Identified all the foreign key constraints that would break
2. Proposed a three-phase migration strategy
3. Generated the migration scripts
4. Wrote validation queries to verify data integrity

This is the kind of multi-step planning that 5.1 struggled with. The model maintains coherent reasoning across thousands of tokens without losing track of constraints established early in the chain.

Where It Still Falls Short#

Not everything is improved:

• Hallucination of library APIs: GPT-5.2 still occasionally invents function signatures, especially for less common packages. The 30% reduction is real but not elimination.

• Overconfidence: Like all frontier models, it presents plausible-sounding but incorrect solutions with full confidence. Verification remains essential.

• Context window utilization: While the 400K window exists, performance degrades on tasks requiring precise recall from early context. The model is better at broad summarization than pinpoint retrieval.

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Competitive Positioning#

Where does GPT-5.2 sit in the December 2025 landscape?

vs. Claude Opus 4.5#

Claude maintains its edge in agentic workflows and tool orchestration. If you're building multi-step agents that need to chain tool calls, manage file operations, and maintain long-running sessions, Opus 4.5 is still the more reliable choice.

GPT-5.2 wins on raw reasoning benchmarks and context window size. For single-shot complex problems with large context, it's the stronger option.

vs. Gemini 3 Pro#

Gemini 3 Pro dominates multimodal tasks—image understanding, UI-from-mockup generation, and visual reasoning. Its 1M token context window also exceeds GPT-5.2's 400K.

GPT-5.2 is stronger on pure text reasoning and code generation, particularly in thinking mode.

vs. DeepSeek V3.2-Speciale#

Speciale is the specialized reasoning engine—gold-medal Olympiad performance at a fraction of the cost ($0.70/M tokens). But it's text-only, no tool calling, and optimized for contest-style problems.

GPT-5.2 is the generalist—strong across all domains with full tool and API support.

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Deployment Considerations#

For teams evaluating GPT-5.2 for production:

When to use GPT-5.2:

• Large codebase analysis requiring 200K+ token context
• Complex reasoning tasks where accuracy matters more than latency
• Workflows that can tolerate $14/M output token costs
• Use cases that benefit from extended output generation

When to prefer alternatives:

• Cost-sensitive applications → DeepSeek V3.2 ($0.70/M)
• Multi-step tool orchestration → Claude Opus 4.5
• Multimodal/vision tasks → Gemini 3 Pro
• Real-time applications → GPT-5.2 Instant or Claude Sonnet 4.5

The optimal strategy for many teams is now multi-model: route simple queries to faster/cheaper models, escalate complex reasoning to GPT-5.2 Thinking, and use specialized models for their respective strengths.

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The Bigger Picture#

GPT-5.2 represents OpenAI's clearest articulation yet of where they see competitive advantage: professional knowledge work.

The GDPval benchmark—measuring performance across 44 occupations—isn't an accident. OpenAI is positioning GPT-5.2 not as an academic achievement but as a practical tool for lawyers, analysts, engineers, and researchers.

The "code red" context matters here. Google's Gemini 3 has been eating into ChatGPT's consumer market share. OpenAI's response is to lean into the enterprise and developer markets where they maintain strongest positioning.

Whether this proves strategically correct remains to be seen. But from a pure capability standpoint, GPT-5.2 is a genuine step forward—particularly for complex reasoning, large-context tasks, and extended code generation.

The AI landscape in December 2025 is remarkable: four models (GPT-5.2, Claude Opus 4.5, Gemini 3 Pro, DeepSeek V3.2) that would each have been considered miraculous two years ago, now competing on specialized strengths rather than overall dominance.

We're no longer asking "which model is best?" We're asking "which model is best for this specific task?" That's a fundamentally different, and more useful, question.

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Building with GPT-5.2? I'd love to hear what you're finding—reach out on Twitter.