How to Use Poker Solvers

A poker solver takes a defined game tree — starting stacks, bet sizes, board texture, and ranges for both players — and iterates through millions of strategy combinations until it converges on a Nash equilibrium solution. At equilibrium, neither player can improve their expected value by unilaterally changing their strategy.

The most widely used solvers are PioSOLVER, GTO+, and MonkerSolver (for PLO). Each takes the same fundamental inputs: preflop ranges for both players, the board cards, available bet sizes, and stack depth. The solver then outputs the optimal frequency for every action (bet, check, raise, fold, call) with every hand in both players' ranges.

Importantly, solver solutions are not "the answer" — they are one answer given specific assumptions. Change the bet sizes, alter the ranges, or modify the stack depth, and the solution changes. Solvers show you what perfect play looks like under specific conditions, but real poker involves imperfect opponents and dynamic situations.

The computational power required is significant. A single flop solve with multiple bet sizes can take 5-30 minutes on a modern computer. Turn and river solves are faster because the game tree is smaller. Full preflop solutions require supercomputer-level resources, which is why most players use pre-computed preflop ranges.

The most common mistake beginners make with solvers is treating mixed strategies as commandments. When a solver says "bet AQ 70% of the time and check 30%," it doesn't mean you should flip a mental coin. It means AQ is close to indifferent between betting and checking, and you should choose based on exploitative reads against your specific opponent.

Focus on the pure or near-pure strategies first. Hands the solver bets 95%+ of the time should always be bet. Hands the solver checks 95%+ of the time should always be checked. These are the clear-cut situations where the solver's recommendation is unambiguous.

Look for patterns across hand categories rather than memorizing individual hand frequencies. On a dry Ah-8s-3c board, you'll notice the solver c-bets most of its range at a small sizing, checks back medium pairs, and uses the larger sizing primarily with strong value hands and specific bluffs. These category-level patterns transfer across similar board textures.

Pay special attention to which hands the solver uses as bluffs. GTO bluffing isn't random — solvers consistently choose hands with backdoor equity (backdoor flush draws, backdoor straight draws) as bluffs because they have the best chance of improving when called. This principle applies to every street of play.

The gap between knowing solver solutions and applying them in real time is where most players fail. You cannot replicate exact solver frequencies at the table — the goal is to build simplified strategies that approximate GTO play closely enough to be unexploitable while remaining executable under time pressure.

Create decision trees for common spots. For example: "In a single-raised pot as the preflop raiser on a dry board, I c-bet my entire range at 33% pot. On a wet board, I c-bet my strong hands and draws at 75% pot and check everything else." This two-rule system approximates the solver's complex output and can be executed instantly.

Use solver knowledge as a baseline and deviate exploitatively when you have reads. If the solver says to bet 33% of pot with your entire range, but your opponent folds to c-bets 75% of the time, increase your c-bet frequency to 100% and use a larger size. The solver tells you what's balanced; your reads tell you when to deviate.

Review your played hands against solver solutions during study sessions. Filter your database for the largest pots you've played, run the solver on those specific scenarios, and compare your actual decisions to the GTO solution. This feedback loop is the fastest way to improve because it connects abstract theory to your real decision-making.