Slippage vs Price Impact: What Each One Costs You on Solana

You set slippage to 1%, hit swap, and the price impact line says 6%. Which number is the one that costs you? Both, in different ways, and mixing them up is one of the most common reasons people overpay on Solana swaps or keep getting failed transactions. Slippage vs price impact sounds like jargon, but it's really two separate questions: how much does my own trade move the price, and how much worse am I willing to accept if the market moves before my trade lands? I'll walk through both in plain words, straight from the Jupiter, Raydium and Uniswap docs, plus what it means when you're trading thin meme coins. Sources are linked so you can check me.

What is price impact?

Price impact is the price move your own trade causes. Most swaps on Solana don't hit an order book. They trade against a pool, a pot of two tokens whose ratio sets the price. When you buy, you take tokens out and put SOL in, so every bit of your order gets a slightly worse price than the bit before it. Uniswap's swap docs describe it as the price shifting continuously during the swap, which leaves your final price somewhere between where it started and where your trade pushed it. They call it "an inextricable part of AMM design" (AMM means automated market maker, the pool model).

The key point: price impact depends on your size compared to the pool. Jupiter's risk page puts it simply: the larger your trade compared to the pool, the more the price moves against you. A small buy in a deep pool barely moves anything; in a tiny pool it can move a lot.

And it's knowable before you click. Raydium's docs call price impact "a deterministic property of a trade against a specific pool state", meaning it can be fully computed from the trade size and the pool's reserves before you submit.

What is slippage, then?

Slippage is what changes between the quote and the moment your trade actually lands on-chain. Other people's trades hit the pool first, the price moves, and you get a different result than the one you were quoted. Raydium says it's a function of latency, other transactions and the order they land in, "not of the pool math."

Raydium's own example makes the split clear: a quote with 1% price impact that lands in the next block against an otherwise quiet pool has 0% slippage. The 1% was all price impact. If another trade hits the pool first and you land 0.2% worse, that extra 0.2% is slippage.

Your slippage tolerance is the setting that decides how much of that drift you'll accept. It gets written into the transaction as a minimum amount out (Raydium's minAmountOut, Uniswap's amountOutMinimum). If the fill would come in below that floor, the program refuses and the swap fails instead of filling at a worse price.

How do slippage and price impact add up?

This is the part people miss. Your tolerance is a buffer measured from the quote, and the quote already includes price impact. Raydium spells it out: for a trade with 2% price impact and a 0.5% tolerance, the minimum out sits about 2.5% below the pre-trade price. The two roughly stack.

So a low slippage setting doesn't protect you from a big price impact. If the swap screen says 6% impact and you set 1% slippage, you're still accepting a fill up to around 7% worse than the price you saw on the chart.

On Jupiter, check whether that impact number includes fees. Jupiter's help article on price impact says the displayed figure includes its Ultra fees (2 to 10 bps depending on the pair) and, for gasless trades, fees of up to 10% of your input. It also says that if price impact is high, it strongly recommends avoiding the trade.

Why is high slippage a bigger risk on meme coins?

Two reasons: thin pools and sandwich bots. Thin pools first. Jupiter's risk page says tokens still on a launchpad bonding curve (like Pump.fun) only have the liquidity the curve provides, so price impact "can be significantly higher than on established pools." It also lists difficulty exiting as a risk: selling a large position may not be possible at the expected price, "or at all." Selling a whole bag into a thin pool is where impact hurts most. I covered how that eats paper gains in realized vs unrealized P&L.

Then sandwiches. Helius's Solana MEV report describes how a sandwich works: a bot buys right before your trade to push the price to the worst level your slippage setting allows, your trade fills there, and the bot sells right after for a profit. The report says meme coin traders are especially exposed because they tend to set high slippage on illiquid, volatile tokens. In plain words, your slippage tolerance tells the bot how much room it has to work. Set 30% "just to make sure it goes through" and you've told it there's up to 30% to take.

How to set slippage without guessing

There's no single right number, and Raydium says so directly. What there is, is a sane process:

• Read price impact first. If it's high, the fix is a smaller trade or a deeper pool, not a higher slippage setting. Raydium suggests swap screens flag impact above about 2% as high.
• Match tolerance to the pair. Raydium's UI defaults are 0.1% for stable-to-stable, 0.5% for stable-to-major like USDC-SOL, 1% for major pairs and 3 to 5% for volatile meme or long-tail tokens.
• Use auto slippage if you trust the tool. Jupiter's Real-Time Slippage Estimator (RTSE) picks a tolerance per swap from token type, recent volatility and failure rates. Its developer route defaults to a fixed 0.5% unless RTSE is turned on.
• Treat a failed swap as information. A revert from a tight limit means the price ran past what you'd accept. On Solana you still pay the network fee, which I broke down in priority fees and compute units, but that's usually cheaper than a bad fill.
• Size so you can get out. Before buying, ask what selling the whole position would do to the price. If the exit impact is ugly, the position is too big for that pool.

What the settings don't fix

Tight slippage limits damage per trade. It doesn't make a bad trade good:

• Quotes are snapshots. Price impact is computed from the pool as it was when you got the quote.
• Tight means more failures. On volatile tokens a low tolerance raises your revert rate. That's the tradeoff, not a bug.
• Stop losses can miss. Jupiter notes that if price drops past a stop-loss trigger by more than your slippage tolerance, the order won't execute.

Everything here comes from the protocols' own docs and Helius's research, not from tests I ran. Exact numbers on any given trade depend on the pool and the moment.

The takeaway

Price impact is what your size does to the pool, and you can see it before you click. Slippage is drift after the quote, and your tolerance is the most of it you'll eat. They stack, so read impact first, keep tolerance as tight as the pair allows and size so the exit works too. Most losses on thin coins don't come from one bad setting. They come from small costs piling up trade after trade. If you want to see how a wallet actually traded before you follow it, reading a Solana wallet is the next step, and more walkthroughs live in the guides. What slippage setting do you use on fresh launches, and has a failed swap ever saved you from a worse fill?

Still building. Still here. 🫡