I Let AI Solve a Hard LeetCode Problem in 23 Seconds—and Questioned Everything

Last week’s LeetCode contest, I hit a wall. A Hard DP problem. Forty-seven minutes of my life, gone. I submitted a solution that technically worked—then it timed out on the bloody hidden test cases.

I was fuming.

After the contest, I threw the problem at GPT-5.1-Codex-Max. It spat out a solution in 23 seconds. Not just any solution—one that ran roughly 40 times faster than mine. I sat there staring at the screen, feeling this bizarre mix of awe and existential dread. Part of me thought, “This thing is incredible.” The other part thought, “…have I been wasting three years grinding algorithms?”

I’ve been hammering this model with algorithmic problems for months now. I’ve tripped over its quirks, discovered genuinely mind-bending capabilities, and occasionally caught it lying to my face. Here’s what I’ve actually learned—no corporate fluff, no “leveraging the power of AI” nonsense.

So what even is this thing?

GPT-5.1-Codex-Max dropped in March 2025, a specialised variant of OpenAI’s GPT-5.1 architecture built specifically for code. And before you roll your eyes at “yet another code model”—this one’s different in one crucial way.

It doesn’t just generate working code. It analyses optimality.

It’ll tell you whether your solution’s time and space complexity is asymptotically optimal. If it’s not, it’ll give you an improved version—and walk you through the derivation. That’s the bit that matters. Previous Codex versions would spit out something that compiled and call it a day. This one acts more like a senior engineer doing a thorough code review.

I ran a side-by-side comparison with Claude 3.5 Sonnet and DeepSeek-Coder-V2 across roughly 30 LeetCode Hard problems. GPT-5.1-Codex-Max’s first-pass acceptance rate came out 15–20 percentage points higher. But honestly? The numbers aren’t what impressed me. What impressed me was the commentary. The complexity proofs. The edge-case analysis. That’s where the learning actually happens.

Case 1: The graph problem that humbled me

The problem was one of those LeetCode classics—shortest paths in a weighted undirected graph that must pass through a specific set of nodes. At its core, it’s a Frankenstein stitch-up of state-compression DP and Dijkstra. Difficulty rating: around 2400, for those keeping score.

Here’s what I wrote: run Dijkstra from each required node, build a condensed graph, then run DP with bitmasking over that. Complexity: O(k · (V+E)logV + k² · 2ᵏ), where k is the number of required nodes. I thought it was elegant. I submitted it, it passed, I even posted about it on my socials feeling rather pleased with myself.

Then GPT-5.1-Codex-Max’s analysis landed, and I went quiet.

It pointed out that my condensed-graph construction had redundant computation. If the shortest path between two required nodes in the original graph doesn’t pass through other required nodes, you can reuse intermediate results instead of running Dijkstra from scratch every time. Its rewritten version used a multi-source Dijkstra with path caching—effectively halving the constant factor for larger k.

What got me, though, weren’t the optimisations. It was the commentary.

It annotated every loop with its exact tight-bound complexity. It also flagged a degenerate case I hadn’t even considered: when the required-node set includes all vertices, the problem collapses into the Travelling Salesman Problem, and it suggested switching to Christofides’ algorithm for a 1.5-approximation.

Wait—correction. Christofides’ algorithm only gives that approximation guarantee for metric TSP. If the graph doesn’t satisfy the triangle inequality, the suggestion’s invalid. But to be fair, the model did mention that prerequisite in a comment. I’d skimmed past it on first read.

This ability—solving the problem and mapping its boundaries—is something I’ve seen senior engineers with half a decade of experience struggle with. They can write code that runs. Asking them to exhaustively reason about edge cases, degeneracy, and alternative approaches? That’s rarer.

Case 2: Rethinking DP states entirely

There’s this edit-distance variant where you’re allowed to swap adjacent characters as an operation. Standard edit distance already lets you insert, delete, and substitute. Adding swaps messes everything up.

The obvious approach is 2D DP: dp[i][j] represents the minimum cost to match prefixes of the two strings. But the moment you introduce swaps, the state transitions turn into a tangled mess—because a swap can affect characters you’ve already “matched.”

I was stuck for almost an hour. I made coffee. I swore at the problem. I went back to tweaking transition equations that kept breaking at the edges. The code I finally submitted was ugly, long, and took three attempts to pass.

GPT-5.1-Codex-Max didn’t patch my approach. It redesigned the state from scratch: dp[i][j][0/1], where the third dimension tracks whether the last character has been swapped. That one extra bit completely decoupled the swap complexity. The transition equations became startlingly clean.

And then it wrote this, in its analysis:

*“The optimal substructure of this problem is hidden in the exchange properties of operation ordering—not in the surface-level character matching.”*

I stared at that sentence for a while.

It wasn’t applying a template. It was reasoning about the structural essence of the problem.

I shared the solution in our algorithms study group afterward. A friend at ByteDance—infrastructure engineer, seven years of experience—said he’d seen a similar state design in an internal code review once, written by a senior dev who’d been on the team for ages. The fact that an AI model can now produce that level of insight consistently is both thrilling and mildly terrifying.

Case 3: Concurrency exposed its blind spots

Right, let’s talk about where it falls over.

I gave it a multi-threaded alternating-print problem—multiple producers, multiple consumers, lock-free ring buffer. The kind of thing that makes you squint at memory barriers and question your life choices.

Its first solution used CAS operations. Looked plausible on the surface. But when I scrutinised the boundary handling for full and empty buffers, there it was: a race condition. When producer and consumer operated on adjacent slots simultaneously, data could get silently overwritten. ThreadSanitizer lit up like a Christmas tree.

I pointed out the issue. It apologised—genuinely, it said “you’re right, here’s the fix”—and produced a revised version using dual counters to decouple read and write pointers. Better. But under extreme contention, the revised code still had ABA problem vulnerabilities. I compared it against Dmitry Vyukov’s classic lock-free queue implementation, and the gap was… noticeable.

This is tricky territory. The model understands concurrency concepts, and it can pattern-match against common designs. But lock-free programming demands this mental simulation of interleaved thread execution that it just doesn’t do reliably. Memory visibility, instruction reordering, subtle happens-before relationships—it still trips.

My rule now: if it gives me concurrent code involving lock-free structures, I don’t trust it until I’ve run it through ThreadSanitizer and ideally Relacy Race Detector. Its strengths are in algorithm design, not low-level concurrency correctness.

How does it actually judge optimality?

This is the feature I find genuinely fascinating. I spent some time reverse-engineering its analytical process—here’s roughly what it does:

1. Lower-bound derivation. It attempts to prove the theoretical complexity floor of the problem. If a problem requires examining every element, it’ll state outright: “Any algorithm must take Ω(n) time; the current O(n) solution is asymptotically optimal.”

1. Constant-factor scrutiny. Even when the big-O is optimal, it checks whether you’re paying unnecessary overhead. I’ve seen it flag things like: “The inner-loop hash lookup can be replaced with array indexing, since the key domain is bounded.”

1. Space-time trade-off proposals. It’ll actively suggest alternatives. “If input size never exceeds 10⁴, an O(n²) preprocessing step would reduce queries to O(1).”

1. Referencing known optimal algorithms. It sometimes cites academic literature. Once it referenced Tarjan’s 1972 paper and correctly noted the theoretical upper bound of O(n·α(n)). I’ll admit, that made me raise an eyebrow.

There’s a catch, though. It occasionally over-optimises—chasing the asymptotically optimal solution with an implementation so complex that constant factors make it slower in practice than a simple approach. Competitive programmers have a term for this: complexity fraud. It looks elegant on paper, runs like a dog. You’ve still got to exercise your own judgement. Don’t blindly trust the “optimal” label.

How I actually use it (without cheating myself)

After months of working with this thing, here’s what maximises its value:

Treat it as a senior code reviewer, not a code generator. Don’t ask it to write from scratch. Write your solution first, then throw it over for analysis. You’ll spot your own blind spots, and the learning sticks better.

Ask “why” relentlessly. When it gives you a better approach, push it: Where did this insight come from? How’s it different from the standard solution? Why do you think it’s more optimal? I’ve gone four or five rounds of follow-up on a single problem, and those deep threads are where the real gold is.

Test it with pathological inputs. Throw n=0, n=1, all-equal-elements, reverse-sorted, and other degenerate cases at it. This verifies correctness and deepens your understanding of the algorithm’s boundaries.

Don’t outsource your thinking. My habit: struggle with a problem for at least 30 minutes before I even consider asking for help. If you reach for the answer immediately, your algorithmic intuition won’t develop. This model should be your thinking partner, not your homework machine.

Are coding interviews about to become a joke?

At this point you’re probably wondering: if AI can obliterate Hard problems in seconds, what’s even the point of algorithmic interviews anymore?

Here’s my take. Coding interviews won’t disappear—but what they test will shift. Interviewers won’t care whether you can produce the optimal solution from memory. They’ll care whether you can collaborate with AI, whether you can critique its output, whether you can make sound engineering trade-offs among the options it presents.

“Can you leverage AI to solve algorithmic problems?” becomes a skill in its own right.

It’s already happening. In late 2024, I started seeing candidates in interviews use Copilot live. Interviewers didn’t stop them—they just quietly observed how they used it. That’s the future. I’m not training to write quicksort from memory anymore. I’m training my judgement. My taste. My ability to look at an AI-generated solution and say, “This is clever but fragile,” or “This complexity is misleading.”

That’s the muscle worth building now.

What’s your experience?

How often do you lean on AI for coding in your day-to-day work? Ever caught it producing something that looked right but was secretly broken? I’d genuinely love to hear your stories—leave a comment. And if you’ve been using GPT-5.1-Codex-Max yourself, share your findings. We’re all figuring this out as we go.

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