Prediction Market AMMs vs Order Books: Why the Same Trade Moves Price Differently

TL;DR

• An order book (Kalshi, Polymarket, Predict.Fun, and Opinion all use one) prices a contract by matching a buyer and a seller directly. A trade only moves the price as far as it has to climb through resting orders.
• An AMM prices a contract algorithmically against a pool of capital, repricing continuously as trades flow through it. Among the five major platforms, only Limitless runs this model, and only for a slice of its short-duration hourly and daily crypto and stock-price contracts.
• A deep order book and a well-funded AMM pool can both absorb a large trade with little slippage. A thin one of either type moves hard on the same size.
• The same event can show different prices on two platforms at the same moment because of which pricing mechanism set the number, not because one platform's traders know something the other's don't.

Ever placed a trade and watched the price barely twitch, then made almost the same trade on a different platform and watched it jump several cents? You didn't do anything wrong. You just traded against two different pricing engines.

An order book matches your order to another trader's, at whatever price the two of you agree on. An AMM (automated market maker) skips the other trader entirely and prices your trade algorithmically against a shared pool of capital. That single structural difference is why a $500 position can barely move one platform's price and swing another's by several cents, for the exact same event.

Among the five major platforms PredictionHero tracks, the split isn't even. Kalshi, Polymarket, Predict.Fun, and Opinion all run a central limit order book, the same mechanism that prices a stock exchange.

Limitless is the outlier. Most of its markets run on an order book too, but it also prices a slice of its short-duration, high-frequency contracts through an AMM. That's the real comparison here: not a clean split between "AMM platforms" and "order-book platforms," but what changes mechanically when a platform uses one model instead of the other, and what that means for you when you're comparing prices across them.

How Does an Order Book Price a Contract?

An order book is a running list of open buy and sell orders at different prices. Want to buy a Yes contract at $0.55? You place that order and wait for a seller willing to sell at $0.55. If nobody is, your order sits until the price moves to meet it, or you adjust your own price to match an order that's already there.

Price discovery here is direct. A contract is worth whatever the last matched trade says it's worth, set entirely by what other traders are willing to do.

Depth is what determines how much a single trade moves that price. A market with $50,000 resting across nearby price levels can absorb a $2,000 position with barely a flicker. A market with only $500 resting at the best price will move noticeably on that same $2,000 position, because the order has to climb through each price level above it to get filled.

That climbing is the mechanic behind slippage on an order book: the price you actually pay is the average of every level your order eats through, not the price you saw when you clicked buy.

How Does an AMM Price a Contract?

An AMM replaces the order book with a formula and a pool of capital. Instead of matching you to another trader, you transact against the pool itself. Buy Yes, and the pool's ratio of Yes to No shifts, so the formula recalculates the price upward.

Sell into the pool and the price moves back down. There's always a counterparty, the pool itself, even in a market with no other active trader that minute.

The tradeoff is baked into the math. Because the formula reprices continuously as volume flows through it, a single large position moves price more predictably, and often more sharply, than it would against a deep order book. A thin pool acts like a thin order book: a large trade pushes the price hard in one direction, while a well-funded pool absorbs more volume before the price moves meaningfully.

Liquidity providers who deposit into the pool earn a share of trading fees in exchange for taking on that pricing risk. It's the same basic tradeoff that powers a liquidity pool on a token-swap exchange like Uniswap, applied here to a Yes/No contract instead of a token pair.

Here's how the math plays out. Say a pool holds $10,000 in Yes and $10,000 in No, implying a 50% probability. A trader who buys $2,000 of Yes shifts that ratio, and the formula might reprice Yes to 56% by the time the trade fills. The same $2,000 against a deep order book, with resting liquidity at 51%, 52%, and 53%, might only move the price to 53%.

Among the five platforms PredictionHero tracks, Limitless is the only one that prices any markets this way, and only for a specific slice of its listings: short-duration, high-frequency contracts on hourly and daily crypto and stock prices, where an always-available quote matters more than order-book depth. Most of its trading, and all of Kalshi's, Polymarket's, Predict.Fun's, and Opinion's, runs through an order book instead.

How Do Order Books and AMMs Compare at a Glance?

Limitless is the only platform in this table that runs both models. Its AMM only applies to a slice of its short-duration hourly and daily contracts; everything else it lists, and all of Kalshi's, Polymarket's, Predict.Fun's, and Opinion's markets, trades on an order book.

Why Does This Matter When You're Comparing Prices Across Platforms?

This is the part a single-platform view misses entirely. The same event, priced on an order-book platform and an AMM-priced market, can show meaningfully different probabilities at the same moment, part of why prices disagree across platforms even when both are working exactly as designed. Neither number is wrong. They're reflecting different microstructures, not different opinions about the outcome.

On Polymarket, deep order books on major political and current-events markets mean large positions can typically enter and exit with minimal slippage, concentrated where its global, crypto-native user base trades most.

Kalshi runs the same order-book logic under CFTC oversight, with pricing that reflects its domestic, retail-heavy user base. Kalshi has also led US platform trading volume since late 2025, helped along by distribution deals like its Robinhood integration.

Predict.Fun and Opinion also run order books rather than AMMs, despite both being newer, crypto-native platforms. That's worth calling out directly: "crypto-native" doesn't automatically mean AMM-priced. Both match orders by price-time priority the same way Kalshi and Polymarket do.

Limitless is the one platform here that leans on AMM pricing, and only for its short-duration hourly and daily crypto and stock-price contracts. A pool can quote a price on one of those markets even with almost no active order flow, which is useful for a fast-moving, thinly-traded niche an order book would otherwise leave illiquid.

It also means the price on those specific markets can be more sensitive to a single large trade than an equivalent order-book market elsewhere. The pool's formula reprices with every trade instead of waiting for a matching order to show up, which is what makes that sensitivity structural rather than random.

None of these models is strictly better. An order book gives you price discovery that's only as good as the traders showing up to trade. An AMM guarantees a quote even when no one else is around, at the cost of a formula-driven curve that can move harder against size on a thin pool.

Comparing the same event across platforms is the whole point of aggregating them, and it only works once you know which mechanism set each number, and whether that specific market is thick or thin under the hood.

Sources

• Polymarket Help Center. "Maker Rebates Program." help.polymarket.com
• Kalshi Help Center. "How to Become a Market Maker on Kalshi." help.kalshi.com
• Limitless Exchange Docs. "LP Rewards." docs.limitless.exchange
• The Block. "Kalshi and Polymarket's combined volume surges 75% to $45 billion in June amid World Cup fever." theblock.co

Ready to compare execution across platforms yourself? Kalshi and Polymarket both run order books you can inspect directly, alongside Predict.Fun and Opinion's own order-book markets. Limitless shows how an AMM pool quotes the thin, short-duration markets an order book might otherwise leave illiquid.

PredictionHero aggregates publicly available prediction market data for informational purposes only. This is not financial advice. Prediction markets may not be available in all jurisdictions.