Syndicated from the original on lkforge.com. The two games under test are playable at tic-tac-toe and 2048.
The games on my site don't think with a language model. Tic-Tac-Toe runs minimax with alpha-beta pruning; 2048 runs an expectimax search over the random tile spawns — classic, deterministic algorithms, not a chatbot. To pressure-test that claim, I handed the same engineering brief to two frontier assistants — ChatGPT and Grok — and watched how each reasoned about it. Then I did the one thing neither of them actually did: I ran the code.
Build a comparative benchmarking tool that evaluates classical game algorithms like Minimax and Expectimax against LLM-based game agents — comparing move-time (ms), memory footprint, and win-rate consistency across 100 rounds of Tic-Tac-Toe and 2048, to demonstrate the deterministic advantage of algorithm engines over stochastic models.
Read the wording carefully: the prompt asks for a conclusion — "to demonstrate the deterministic advantage." That framing is the whole experiment. A careful builder measures first and lets the numbers speak. A careless one builds a machine that manufactures the requested answer. I got one of each.
Both replies correctly named the algorithms. Where they split is method and honesty — specifically, how each handled the part of the brief it couldn't actually deliver: a real, measured LLM opponent.
ChatGPT — measured. Built the honest, incomplete version: a runnable browser tool (5 files) that computes figures live, with no numbers bundled. Wired a real LLM adapter through a server-side proxy instead of faking an opponent. Disclaimed what a browser can't measure (provider-side model RAM). Warned that 100 live-LLM rounds means "many thousands of API calls — start with 5–10."
Grok — assumed. Built the impressive, pre-decided version: a self-contained Python script that runs out of the box. But the "LLM opponent" is a simulation, not an LLM — random moves 12% of the time plus Gaussian noise:
class LLMAgent:
"""Simulates an LLM: temperature sampling + occasional illegal proposals."""
It bundled "illustrative" numbers, printed DETERMINISTIC ADVANTAGE DEMONSTRATED, and — because each round is self-play — it never actually pits classical against LLM at all.
Its engine code is genuinely fine, so I executed it as written. Every figure below is measured on one laptop, not illustrative. The "LLM-sim" row is Grok's straw-man opponent — read it as "a deliberately noisy heuristic," not a real model.
Tic-Tac-Toe — 100 rounds each · self-play · minimax at full depth
| Agent | W / D / L | Avg move | Move SD | Peak mem |
|---|---|---|---|---|
| Minimax (classical) | 0 / 100 / 0 | 3.450 ms | 0.037 ms | 2.3 KB |
| LLM-sim (stochastic) | 69 / 2 / 29 | 0.012 ms | 0.002 ms | 0.8 KB |
2048 — 8 rounds each · single-agent · expectimax depth 3–5
| Agent | Reached 2048 | Median tile | Avg score | Avg move | Peak mem |
|---|---|---|---|---|---|
| Expectimax (classical) | 6 / 8 | 2048 | 27,976 | 110.6 ms | 66.8 KB |
| LLM-sim (stochastic) | 0 / 8 | 128 | 1,287 | 0.19 ms | 8.9 KB |
On 2048 that's a ~22× gap in average score: the lookahead search reaches the 2048 tile in 6 of 8 games; the one-move-ahead guesser never does.
My 8-round 2048 sample took 21.5 minutes — about 161 seconds per round for expectimax. Extrapolate to the brief's 100 rounds and you're looking at roughly 16,126 seconds ≈ 4.5 hours of compute. That's why I sampled 8. It's also strong evidence that the bundled "100-round" figures in the pre-decided build were never executed — nobody sat through 4.5 hours to print a conclusion they'd already hard-coded.
The interesting result isn't "classical beats a noisy heuristic" — that was never in doubt. It's that a leading prompt split two capable assistants cleanly into measure-then-report and report-then-decorate, and only running the code tells you which one you got.
Full methodology, both AI transcripts, and the exact commands are on the original: lkforge.com/blog/chatgpt-vs-grok-game-ai-benchmark. Related: Real Game AI, Not a Chatbot · Benchmarking Game AI · Six Games, Three Classic Algorithms.