Kalshi vs Polymarket: Which Prediction Market to Use in 2025

Prediction markets are having their moment. The 2024 US election proved they outperformed every major poll, and now billions of dollars flow through Kalshi and Polymarket every month. But they're quite different platforms. Here's the complete comparison.

Quick Summary

| Feature | Kalshi | Polymarket | |---------|--------|------------| | Regulation | US-regulated (CFTC) | Decentralized (USDC on Polygon) | | US access | Yes (most states) | Restricted (but VPN possible) | | Settlement | USD | USDC stablecoin | | Fees | 0-7% depending on market | 2% on wins | | Liquidity | Medium | High | | API | Yes (REST) | Yes (REST + WebSocket) | | Market types | Finance, politics, weather, sports | Politics, crypto, sports, culture | | Bot-friendly | Yes | Yes |

Kalshi: The Regulated Option

Kalshi is the first federally regulated prediction market in the US, regulated by the CFTC (Commodity Futures Trading Commission). This is significant — it means:

Your money is protected: Client funds segregated from company funds
Legal in most US states: No gray area
Institutional participation: Hedge funds can trade here legally

Best Kalshi Markets

Federal Reserve interest rate decisions: Huge liquidity, predictable timing
CPI/inflation reports: Economic data is Kalshi's specialty
US election markets: Very deep liquidity around major elections
Weather events: Unique market type not found elsewhere

Kalshi API Example

import requests
import os

KALSHI_API_KEY = os.getenv("KALSHI_API_KEY")
BASE_URL = "https://trading-api.kalshi.com/trade-api/v2"

headers = {
    "Authorization": f"Bearer {KALSHI_API_KEY}",
    "Content-Type": "application/json"
}

def get_markets(limit=20, status="open"):
    """Get open markets."""
    r = requests.get(f"{BASE_URL}/markets", params={"limit": limit, "status": status}, headers=headers)
    return r.json().get("markets", [])

def get_market_orderbook(ticker: str):
    """Get order book for a market."""
    r = requests.get(f"{BASE_URL}/markets/{ticker}/orderbook", headers=headers)
    return r.json()

def place_order(ticker: str, side: str, count: int, price: int):
    """Place a limit order. Price in cents (50 = $0.50)."""
    payload = {
        "action": "buy",
        "side": side,   # "yes" or "no"
        "ticker": ticker,
        "count": count,
        "type": "limit",
        "yes_price": price if side == "yes" else 100 - price,
        "no_price": price if side == "no" else 100 - price,
    }
    r = requests.post(f"{BASE_URL}/portfolio/orders", json=payload, headers=headers)
    return r.json()

# Find Fed rate markets
markets = get_markets(limit=50)
fed_markets = [m for m in markets if "FED" in m.get("ticker", "").upper() or "rate" in m.get("title", "").lower()]
for m in fed_markets[:3]:
    print(f"{m['ticker']}: {m['title']} — Yes: {m.get('yes_bid')}/No: {m.get('no_bid')}")

Polymarket: The Decentralized Giant

Polymarket runs on Polygon (an Ethereum L2), using USDC as its currency. Despite regulatory challenges in the US, it's the largest prediction market by volume globally.

Why Traders Prefer Polymarket

More market variety: From Oscar winners to AI progress to crypto prices
Higher liquidity: Crypto-native users bring more capital
Transparent: All trades on-chain, verifiable
No KYC for most users (though US access is technically restricted)

Polymarket API Example

import requests
from datetime import datetime

BASE_URL = "https://clob.polymarket.com"
GAMMA_URL = "https://gamma-api.polymarket.com"

def search_markets(query: str, limit: int = 10) -> list:
    """Search for active markets."""
    r = requests.get(
        f"{GAMMA_URL}/markets",
        params={"active": True, "closed": False, "_limit": limit, "q": query},
        timeout=10
    )
    return r.json()

def get_market_prices(condition_id: str) -> dict:
    """Get current YES/NO prices for a market."""
    r = requests.get(f"{BASE_URL}/price", params={"token_id": condition_id})
    return r.json()

def get_orderbook(token_id: str) -> dict:
    """Get order book for a specific token."""
    r = requests.get(f"{BASE_URL}/book", params={"token_id": token_id})
    return r.json()

# Find BTC price markets
btc_markets = search_markets("Bitcoin price", limit=5)
for market in btc_markets:
    print(f"Market: {market.get('question', 'N/A')}")
    print(f"  Volume: ${float(market.get('volume', 0)):,.0f}")
    print(f"  Closes: {market.get('end_date_iso', 'N/A')}")
    print()

Fees Comparison

Kalshi

0% on winning side when you're taking liquidity from the order book
7% maximum fee on some markets
Effectively 1-3% round-trip for most markets

Polymarket

2% fee on winning trades only
No fee on losing trades
Gas fees on Polygon are negligible (under $0.01)

Example: $100 bet on YES at 60 cents

If YES wins: You receive $166.67 minus fees
- Kalshi (3% fee): $161.67
- Polymarket (2% on win): $163.33

Liquidity Comparison

Higher liquidity = tighter spreads = better prices.

For major political events (US elections, Fed decisions):

Polymarket wins by far — often 10x+ more volume than Kalshi

For regulated financial markets (CPI, NFP, GDP):

Kalshi wins — these markets don't exist on Polymarket

For niche markets:

Polymarket has more variety and typically better liquidity

Building an Arbitrage Bot

When the same event trades on both platforms, price discrepancies emerge:

import requests
from dataclasses import dataclass

@dataclass
class MarketOpportunity:
    event: str
    kalshi_yes: float
    poly_yes: float
    arb_pct: float

def find_arb_opportunities() -> list[MarketOpportunity]:
    """Find events priced differently on Kalshi and Polymarket."""
    # This would require a manual mapping of corresponding markets
    # Here's the structure:
    
    mapped_markets = [
        {
            "event": "BTC above $70k by end of March",
            "kalshi_ticker": "KXBTC-70K-MAR",
            "poly_condition": "0x123...",
        }
    ]
    
    opportunities = []
    
    for market in mapped_markets:
        try:
            # Get Kalshi price
            kalshi_r = requests.get(f"{BASE_URL}/markets/{market['kalshi_ticker']}", headers=headers)
            kalshi_yes = kalshi_r.json().get("yes_ask", 50) / 100
            
            # Get Polymarket price
            poly_r = requests.get(f"https://clob.polymarket.com/price?token_id={market['poly_condition']}")
            poly_yes = float(poly_r.json().get("price", 0.5))
            
            # Calculate arbitrage
            if abs(kalshi_yes - poly_yes) > 0.03:  # >3% difference
                arb = abs(kalshi_yes - poly_yes) * 100
                opportunities.append(MarketOpportunity(
                    event=market["event"],
                    kalshi_yes=kalshi_yes,
                    poly_yes=poly_yes,
                    arb_pct=arb
                ))
        except Exception:
            pass
    
    return sorted(opportunities, key=lambda x: x.arb_pct, reverse=True)

Which Should You Use?

Use Kalshi if:

You're in the US and want fully regulated trading
You trade economic data (CPI, Fed rates, NFP)
You want USD settlement
You're running a compliant fund

Use Polymarket if:

You want the widest variety of markets
You want maximum liquidity on political events
You're comfortable with crypto/USDC
You want on-chain transparency

Use both if:

You're looking for arbitrage opportunities
You want to cross-reference prices as part of your research

For most prediction market traders, Polymarket offers better prices and more markets. But Kalshi's regulatory clarity is genuinely valuable for US-based professionals.