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AI Benchmarks

2 posts with the tag “AI Benchmarks”

OpenAI's GPT-5.2: A Workhorse AI That Outpaces Gemini 3 Pro and Opus 4.5

OpenAI has dropped GPT-5.2, a release that outshines even GPT-5 in scope and performance. This isn’t a minor patch—it’s the outcome of an internal “code red” push kicked off by Sam Altman after Google’s Gemini 3 launch. The OpenAI team shifted into overdrive, racing to reclaim their edge, and the results are staggering: GPT-5.2 dominates benchmarks against Gemini 3 Pro and Anthropic’s Opus 4.5 across reasoning, math, coding, and more.

Pietro, a key tester, called it a “serious leap forward” in complex reasoning, math, coding, and simulations—highlighting its one-shot build of a 3D graphics engine. Available now in ChatGPT and via OpenRouter, GPT-5.2 comes in three flavors:

  • GPT-5.2 Classic: The speedy default for everyday ChatGPT use.
  • GPT-5.2 Thinking: Enhanced reasoning with options like light, standard, extended, and heavy.
  • GPT-5.2 Pro (and Extended Pro): Released simultaneously this time, with a “juice level” (reasoning compute) up to 768—far beyond the 128-256 of prior models. This Pro tier justifies the $200 ChatGPT plan, enabling hours-long deep thinking.

Massive Gains in Context, Vision, and Reliability

Section titled “Massive Gains in Context, Vision, and Reliability”

GPT-5.2 nails long-context retrieval, hitting near-perfect scores on OpenAI’s MRCv2 needle-in-haystack tests up to 256k tokens. For coding marathons or extended tasks, fewer chat resets are needed— a boon over GPT-5.1.

Vision capabilities have surged, rivaling Gemini 3’s multimodal strengths. On screenshot analysis, it identifies details like VGA ports, HDMI, and USB-C on a motherboard with precision that GPT-5.1 couldn’t touch. Hallucinations drop 30-40%, with an official rate of just 0.8%, making it ideal for fact-checking, education, or high-stakes apps.

Benchmark Domination: Best-in-Class Everywhere

Section titled “Benchmark Domination: Best-in-Class Everywhere”

Forget incremental tweaks—GPT-5.2 resets leaderboards:

BenchmarkFocusGPT-5.2 Scorevs. Gemini 3 Provs. Opus 4.5
SWE-bench ProSoftware Engineering55.6%CrushesCrushes
GPQA DiamondHard Science Q&ATopSlightly aheadAhead
SciFigure ReasoningScientific FiguresBestBestBest
FrontierMath / AIMEMathBest / SaturatedBestBest
ARC-AGI v1Visual ReasoningTop+20%+15%
ARC-AGI v2Advanced VisualMassive leapTopTop
GDP ValReal-World Tasks71% win vs. expertsN/AN/A

It even tops OpenAI’s fine-tuned Codex models (Max, standard, Mini) for coding. Internally, it replicates 55% of research engineers’ pull requests—real-world features and fixes from top talent.

In cybersecurity’s CTF benchmark (realistic hacking scenarios, 12-shot pass@12), it’s best-in-class. And on ARC-AGI, efficiency exploded: from o1’s 88% at $4,500/task to GPT-5.2 Pro’s higher score at $11— a 390x cost drop in one year.

While GPT-5.1 chased chit-chat (e.g., “I spilled coffee—am I an idiot?”), GPT-5.2 targets pros. On business tasks, it beats experts 70.9% of the time—at <1% cost and 11x speed. Wharton prof Ethan Mollick praises the GDP Val: GPT-5.2 wins head-to-head on 4-8 hour expert tasks 71% of the time, per human judges.

Excel/Google Sheets? GPT-5.2 crafts Fortune 500-level financial models with pro formatting—six-figure junior IB analyst territory. Presentations? From one screenshot of notes, GPT-5.2 Thinking (extended) spent 19 minutes to output a polished PowerPoint rivaling hours of human work.

Coding Powerhouse: Live Demo of an Anti-Hacker Agent

Section titled “Coding Powerhouse: Live Demo of an Anti-Hacker Agent”

In Cursor with the Codex extension (select GPT-5.2 Pro, medium/high reasoning), it built a terminal CLI agent from scratch. Using pipx, it scans networks (interfaces, routes, Wi-Fi details), queries the user (location, purpose), pipes data to GPT-5.2 via OpenRouter, and delivers a risk verdict—like “safe, risk 3/10” for a home setup, with HTTPS tips.

Codex outthinks lazier rivals (Claude, Gemini) on deep tasks, reasoning for minutes without fatigue. Pro with extra-high effort? Hours of compute for bug hunts or complex builds.

Sam Altman teased “Christmas presents” next week—more ChatGPT tweaks incoming. GPT-5.2 proves LLMs aren’t plateauing; OpenAI’s back, fighting Google’s lead. For coders, analysts, or builders: test it now. This is the first model ready to handle real workloads without babysitting.

However, this “Code Red” velocity warrants a pause for skepticism. When a company shifts into “overdrive” to reclaim a lead, what safeguards get compressed? The push for “juice levels” of 768 and hours-long reasoning isn’t just an engineering feat—it’s an environmental and safety gamble. As we’ve discussed regarding AI’s water footprint, these massive inference loads carry a tangible physical cost. Moreover, racing to beat Gemini 3 risks prioritizing benchmark dominance over robust alignment, a tension that historically leads to “patch later” mentalities. We must ask: are we building a safer intelligence, or just a faster one?

GPT-5.2: AI Crosses the Threshold into Human-Level Project Delivery

OpenAI’s latest release, GPT-5.2, isn’t just another incremental update—it’s a paradigm shift. In under an hour of “thinking” time, it can deliver fully functional 3D games and simulations, complete with destructible environments, physics, scoring systems, and interactive controls. Prompt it to build a city destruction shooter where players fly through skyscrapers, unleash miniguns and rockets, and rack up combos via chain reactions. The result? A downloadable zip file containing a complete project folder, ready to run in a browser using Three.js. No piecemeal code snippets; this is handover-ready production work.

One standout demo: a 3D spherical planet running Conway’s Game of Life, complete with asteroid impacts, customizable bloom effects, meteor intervals, pause/step controls, and camera manipulation. Another: a cosmic visualization tour of sci-fi megastructures like Dyson spheres, orbital elevators, and neon spire cities, with autopilot fly-throughs and adjustable field-of-view. These aren’t static renders—they’re interactive, real-time experiences generated in a single shot after 20-55 minutes of extended reasoning.

The GDP-Val Benchmark: Measuring Real Economic Value

Section titled “The GDP-Val Benchmark: Measuring Real Economic Value”

The true bombshell lies in the GDP-Val benchmark, a rigorous test of AI’s ability to complete profession-level projects across sectors like engineering, finance, healthcare, and marketing. Unlike toy benchmarks focused on trivia or puzzles, GDP-Val assigns tasks mimicking actual workflows:

  • Manufacturing Engineer: Design a 3D cable reel stand for an assembly line, including exploded views.
  • Financial Analyst: Build a competitor landscape for last-mile delivery services.
  • Registered Nurse: Analyze skin lesion images and draft a consultation report.
  • Event Planner: Optimize table layouts for a vendor fair or craft a luxury Bahamas itinerary.

Humans and AIs tackle these blindly, then industry experts—with an average of 14 years experience from firms like Goldman Sachs, Boeing, Google, and the US Department of Defense—judge the outputs without knowing the source. Ratings cover quality, completeness, and adherence to specs.

Results for GPT-5.2 Pro? A staggering 74% win-or-tie rate against human experts (60% outright wins). This vaults past prior leaders: OpenAI’s own GPT-5 High at 38.8%, Claude 4.1 Opus at 47.6%. Just months ago, humans dominated; now AI does—consistently producing superior deliverables like flawless Excel audits, polished sales brochures, and verifiable 3D models.

ModelWin/Tie RateWin Rate
Claude 4.1 Opus (Sept 2025)47.6%~35%
GPT-5 High (Sept 2025)38.8%~25%
GPT-5.2 Pro74%60%

Experts noted leaps in polish: “Exciting and noticeable… appears done by a professional company with staff… surprisingly well-designed layout.”

Beyond Benchmarks: Intelligence Curves and Cost Plummets

Section titled “Beyond Benchmarks: Intelligence Curves and Cost Plummets”

GPT-5.2 shines on staples too—SWE-Bench Verified jumps, AIME 2025 hits 100%, ARC-AGI verified scores over 90% in extended modes. But the real insight is “intelligence curves”: plot performance (y-axis) against compute budget (x-axis, via tokens/thinking time). New models shift the entire curve rightward, delivering smarter outputs per dollar.

Costs? Sam Altman highlights a 390x reduction in one year. What cost $45,000 per complex task now pennies out. GPT-5.2 Pro’s “extended thinking” mode promises even more, potentially overnight project marathons.

Labor Replacement: From Hype to Economic Reality

Section titled “Labor Replacement: From Hype to Economic Reality”

This isn’t sci-fi. Hand AI a project; it deliberates like a remote contractor, returning zipped deliverables. Iterate? Another 20-30 minutes yields refinements—faster ship speeds, balanced lighting, new weapons. Early glitches (e.g., over-bright effects) stem from blind code generation, but prompts like “single-file output” fix them.

Skeptics call it “fancy autocomplete” that hallucinates. Fair, but irrelevant—accuracy matters. Humans “autocomplete” from memory; if outputs beat 14-year pros 60% of the time, incentives flip. Why hire at $100k/year when AI delivers better, 400x cheaper?

The curve is crossing: from humans > AI to AI > humans across knowledge work. Demand for code, reports, designs explodes elastically in tech; inelastic fields like nursing or finance face direct hits. Transition bumpy? Absolutely. But dismissal as “bubble” ignores exponential gains.

GPT-5.2 feels like assigning tasks to an AI employee. Wait for iterations—full videos incoming. The future of work just accelerated.