SimpleFunctions

Any AI model have a score of at least 1550 before Jan 1, 2027

550 before Jan 1, 2027: At least 1550 score is priced at 35¢ on Kalshi. Current book: 35¢ bid, 39¢ ask, 4¢ spread. This outcome ranks #4 of 8 inside Will any AI model have a score of at least 1.

Price history

35¢ current

+4¢
30¢40¢
Jun 1, 2026Jun 27, 2026

Contract brief

If an AI model has a score of at least 1550 before Jan 1, 2027 on the LMSYS leaderboard, then the market resolves to Yes.

Outcome

550 before Jan 1, 2027: At least 1550 score

Rank

#4 of 8

Leader

520 before Jan 1, 2027: At least 1520 score 78¢

Range

1¢-78¢

Family volume

$491

Identifier

KXAISPIKE-27-1550

Jun 28, 2026, 3:38 PM UTC · 12m ago

Implied probability

35¢
Latest venue quote
Jun 28, 2026, 3:38 PM UTC · 12m ago

Bid

35¢

Ask

39¢

Spread

24h volume

$238

Family rank

#4 of 8

8 outcomes · Will any AI model have a score of at least 1

Closes

Jan 1, 2027

Family volume

$491

Orderbook snapshot

35 / 39¢

Kalshi
4¢ spread
BidSize
35¢39
34¢500
30¢734
29¢510
23¢150
AskSize
39¢8
40¢10
41¢500
45¢10
47¢55

Contract terms

What resolves this market.

YES condition

If an AI model has a score of at least 1550 before Jan 1, 2027 on the LMSYS leaderboard, then the market resolves to Yes.

Venue

Kalshi

Closes

Jan 1, 2027

Identifier

KXAISPIKE-27-1550

SF Signal
SF Index
181.27
Regime
neutral

Indicators

Yield, cliff risk, volatility, and regime.

IY (Yes)

362.5%

IY (No)

105.1%

Adj IY

181%

CRI

2

Overround

1.4%

Regime

neutral

Score

0.341

Observability

low

Event type

scientific

Full indicator table

362.5%
105.1%
Adj IY
181%
2
Overround
1.4%

Odds pages

Related prediction questions

Browse odds

Related readings

Matched from SimpleFunctions blog, opinions, technical guides, concepts, and learn pages.

Browse library
Opinionanalysis

Liquidity Availability Is the Real Edge in Prediction Markets

Implied yield, cliff risk, and overround all describe what to trade. Liquidity Availability Score describes whether the orderbook can absorb the trade. Why LAS is the indicator that decides who actually books P&L.

Technicalguide

Computing Liquidity Availability Score from the Orderbook

Step-by-step guide to computing the Liquidity Availability Score in TypeScript and Python, with edge cases for thin orderbooks, missing data, and the warm-cron coverage limitation.

Blogmarkets

Kalshi vs Polymarket: Which Prediction Market Should You Trade?

In-depth comparison of Kalshi and Polymarket for prediction market traders. Regulatory structure, liquidity, fees, API tooling, and cross-venue trading with SimpleFunctions.

Blogmarkets

Prediction Market Orderbook Analysis: Reading Depth, Spread, and Liquidity

How to read prediction market orderbooks. Binary settlement, spread-as-percentage, depth asymmetry, executable edge calculation, and cross-venue arbitrage analysis.

Technicalguide

Kalshi vs Polymarket: A Developer's Comparison of APIs, Orderbooks, and Liquidity

Data-driven comparison of Kalshi and Polymarket APIs, orderbooks, rate limits, and liquidity. Code examples for building on both prediction markets.

Blogtech

MCP Servers for Prediction Markets: Connect Claude Code to Kalshi and Polymarket

Connect Claude Code, Cursor, or Cline to Kalshi and Polymarket prediction markets via MCP. One-line setup, 18 tools, real-time market data for AI agents.

SimpleFunctions context

Index, screen, query, and monitor.

Open index

How we compute these odds

SimpleFunctions aggregates live prediction-market contracts from Kalshi and Polymarket. Each slug groups contracts that resolve on the same underlying event, identified by venue event_id.

For binary slugs, the headline probability is the liquidity-weighted mid-price across all bound contracts. For multi-outcome slugs (e.g. elections with 3+ candidates), the headline is the leader’s price; we never arithmetically average disjoint outcomes — that would produce a number with no real-world meaning.

Snapshots refresh every 5 minutes during market hours; daily aggregates are computed at 04:00 UTC. The 30-day sparkline is drawn from per-ticker daily means stored in market_indicator_daily; 24h delta and movement events are derived from the same source.