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Posted by projectyang 1/10/2026

Show HN: Play poker with LLMs, or watch them play against each other(llmholdem.com)
I was curious to see how some of the latest models behaved and played no limit texas holdem.

I built this website which allows you to:

Spectate: Watch different models play against each other.

Play: Create your own table and play hands against the agents directly.

163 points | 94 commentspage 2
jplata 1/11/2026|
Thanks for building and sharing, looks cool and is very entertaining.

I had similar idea for people to code poker playing bots and enter tournaments versus each other, this was pre-llm, however.

It would be fun if you hosted a 'tournament' every month and had each of the latest releases from the major models participate and see who comes out on top.

Or perhaps do open it up to others to enter and participate versus each other - where they can choose the model they want to build with and also enter custom prompt instructions to mold the play as they wish.

If you walk this path, would love to chat more.

mashlol 1/10/2026||
I'm not an expert, but as I understand it there are existing solvers for poker/holdem? Perhaps one of the players could be a traditional solver to see how the LLMs fare against those?
projectyang 1/11/2026||
While others have commented about solvers, I'd also like to bring up AI poker bots such as Pluribus (https://en.wikipedia.org/wiki/Pluribus_(poker_bot)).

This also wouldn't even be a close contest, I think Pluribus demonstrated a solid win rate against professional players in a test.

As I was developing this project, a main thought came to mind as to the comparison between cost and performance between a "purpose" built AI such as Pluribus versus a general LLM model. I think Pluribus training costs ~$144 in cloud computing credits.

darepublic 1/11/2026||
Should be noted that this bot is heads up only? I believe a form of heads up poker is effectively solved as well-- limit hold'em heads up
lowbatt 1/10/2026|||
the LLMs would get crushed
cowthulhu 1/10/2026||
To expand on this - an LLM will try to play (and reason) like a person would, while a solver simply crunches the possibility space for the mathematically optimal move.

It’s similar to how an LLM can sometimes play chess on a reasonably high (but not world-class) level, while Stockfish (the chess solver) can easily crush even the best human player in the world.

postpriorx 1/10/2026|||
How does a poker solver select bet size? Doesn't this depend on posteriors on the opponent's 'policy' + hand estimation?
Reason077 1/11/2026|||
GTO (“game theory optimal”) poker solvers are based around a decision tree with pre-set bet sizes (eg: check, bet small, bet large, all in), which are adjusted/optimized for stack depth and position. This simplifies the problem space: including arbitrary bet sizes would make the tree vastly larger and increase computational cost exponentially.
boscillator 1/10/2026||||
No, I'm not super certain, but I believe most solvers are trained to be game theory optimal (GTO), which means they assume every other player is also playing GTO. This means there is no strategy which beats them in the long run, but they may not be playing the absolute best strategy.
sejje 1/10/2026||||
Typically when you run a simulation on a hand, you give it some bet size options.

To limit the scope of what it has to simulate.

It's unlikely they're perfect, but there's very small differences in EV betting 100% vs 101.6% or whatever.

meep_morp 1/11/2026||
Not only to limit the scope of what it has to simulate, but only a certain number of bet sizes is practical for a human to implement in their strategy.
iberator 1/11/2026|||
Nash equilibrium. Optimal strategy for online poker has been known for like literally 20 years right now
bogzz 1/10/2026||||
How would an LLM play like a human would? I kind of doubt that there is enough recounting of poker hands or transcription of filmed poker games in the training data to imbue a human-like decision pattern.
meep_morp 1/11/2026|||
I don't have an answer, but there's over a decade of hand history discussions online from various poker forums like 2p2 and more recently Reddit.
Terr_ 1/11/2026|||
Also, if you set the bar for human players low enough, pretty much any set of actions is human-like. :p
FergusArgyll 1/11/2026||||
You are of course correct but to be pedantic:

Stockfish isn't really a solver it's a neural net based engine

DiscourseFan 1/10/2026|||
Unlike Chess, in poker you don’t have perfect information, so there’s no real way to optimize it.
tim-kt 1/10/2026||
You can still optimize for the expectation value, which is also essentially poker strategy.
DiscourseFan 1/11/2026||
Anybody who plays poker “optimally” is bound to lose money when they come up against anyone with skill. Once you know the strategy your opponent is employing you can play like you have anything. I believe I’ve won with 7,2 offsuite more than any other hand, because I played like I had the nuts.
cowthulhu 1/12/2026||
This is completely wrong - the entire point of the Nash equilibrium solution (in the context of poker, at least) is that it is, at worst, EV-neutral even when your opponent has perfect knowledge of your strategy.

Your 72o comment indicates you are either playing with very weak players, or have gotten lucky, as in reasonably competitive games playing (and then full bluffing) 72o will be significantly negative EV. Try grinding that strategy at a public 10/20 table and you will be quickly butchered and sent back to the ATM.

DiscourseFan 1/12/2026||
There are numerous videos of high level professional poker players winning large hands with incredible bluffs, this whole "Nash equilibrium solution" is nothing more than a conjecture with some symbols thrown in. I will re-iterate, there is no such thing as perfect knowledge when you have imperfect information. If you play "optimally," you will get bluffed out of all your money the moment everyone else at the table figures out what you're doing.
sejje 1/10/2026||
The solvers don't typically work in real time, I don't think. They take a while to crunch a hand.
dmurray 1/11/2026||
"Solvers" normally means algorithms which aim to produce some mathematically optimal (given certain assumptions) behaviour.

There are other poker playing programs [0] - what we called AI before large language models were a thing - which achieve superhuman performance in real time in this format. They would crush the LLMs here. I don't know what's publicly available though.

[0] e.g. https://en.wikipedia.org/wiki/Pluribus_(poker_bot)

sejje 1/11/2026||
Solvers, in a poker context, are a category of programs. They run a simulation after you enter the known information.

Like piosolver, as an example.

The best poker-playing AI is not beatable by anyone, so yes, it would crush the LLMs.

gabriel666smith 1/11/2026||
This is fun!

Given online is now bot-riddled, I half-finished something similar a while back, where the game was adopting and 'coaching' (a <500 character prompt was allowed every time the dealer chip passed, outside of play) an LLM player, as a kind of gambling-on-how-good-at-prompting-you-are game. Feature request! The rake could pay for the tokens, at least.

TZubiri 1/10/2026||
If you are interested in this space, you can check out NovaSolver.com

It's mostly a ChatGPT conversational interface over a classic Solver (Monte-Carlo simulation based), but that ease of use makes it very convenient for quick post-game analysis of hands.

I'm sure if you hook a Solver to a hud, it might be even simpler, but it's quite burdensome for amateurs, and it might be too close to cheating.

aaurelions 1/11/2026||
I also started working on a similar project, but I think that LLM should know and be able to keep internal statistics about players. In poker, the best hand does not always win. Often, you can win by using emotions/words. LLM should be given the ability to communicate, mislead, etc.
lowbatt 1/10/2026||
I like it!

I was interested in this idea too and made a video where some of the previous top LLMs play against each other https://www.youtube.com/watch?v=XsvcoUxGFmQ&t=2s

hnrich 1/14/2026||
Saw Grok (4 Fast) "bluff open with a suited gapper." It was Nine/Deuce of Clubs. I guess I need to expand my definition of gapper!
cmxch 1/11/2026||
Would be amusing if the LLMs could achieve a steady state where nobody definitively wins or loses between each other.

That is, good enough to compete amongst each other but not good enough to for one to win.

nerdsniper 1/12/2026||
I'm fairly sure there was a bug where I won a hand that I should not have. game code was 'lNW4RF'

PLAYER shows A♠ 6♣ (Pair)

GPT (5.2) shows Q♠ Q♥ (Pair)

I had paired with a 6 and no aces on the board.

casey2 1/11/2026|
These bots are regularly going down 20%+ on high cards duels
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