Which AI company will have the best coding model on Dec 31, 2026
Leader sits at 66% across 4 bound outcomes, runner-up at 21%. This is a winner-take-all market — the headline is the leader’s price, not an arithmetic mean.
Leader probability
Anthropic
Outcomes
4
winner-take-all
Runner-up
21¢
OpenAI
Spread
45pp
contested
24h volume
$1K
modest
Closes
Dec 31, 2026
115 days
Venue
Kalshi
4 bound
30-day trend
Bracket family
How the bracket ladder is priced.
Each row is one outcome on the venue. Sorted by 24h volume — the heaviest book is at the top.
Cluster 1
Which AI company will have the best coding model on Dec 31, 2026
Which AI company will have the best coding model on Dec 31, 2026?: OpenAI
KXCODINGMODEL-26DEC-OPEN
Which AI company will have the best coding model on Dec 31, 2026?: Anthropic
KXCODINGMODEL-26DEC-ANTH
Which AI company will have the best coding model on Dec 31, 2026?: xAI
KXCODINGMODEL-26DEC-XAI
Which AI company will have the best coding model on Dec 31, 2026?: Google
KXCODINGMODEL-26DEC-GOOG
Analysis
This probability reflects traders' assessment that Anthropic will be recognized as having the best coding model by year-end 2026. The 55% lead over OpenAI's 37% suggests meaningful confidence in Anthropic's trajectory, though the relatively narrow margin indicates genuine uncertainty. Market pricing will likely shift based on benchmark performance—coding competitions, GitHub Copilot adoption metrics, and public model releases throughout the second half of 2026. The lack of a single formal "winner" definition means traders are inferring consensus from developer adoption, enterprise deployment, and performance on standardized coding tests. Resolution will depend on which models demonstrate superior performance on tasks like algorithm generation, bug detection, and code completion accuracy. The 6-7 month timeframe allows for significant model updates from all competitors.
- ›Model release timing: Whether Anthropic, OpenAI, or Google releases a new coding-specialized model in H2 2026 that outperforms existing benchmarks
- ›Benchmark results: Performance scores on standardized coding evaluation suites (e.g., HumanEval, LeetCode-style problems) published before year-end
- ›Enterprise adoption metrics: Real-world usage data showing which models are selected by major developers and integrated into commercial tools
- ›Developer preference indicators: GitHub discussions, Stack Overflow signals, and industry surveys reflecting which models are considered superior by practitioners
- ›Definition and judging criteria: Ambiguity around what constitutes 'best'—whether measured by accuracy, speed, adoption, or independent expert assessment
What moved the line
- Sep 5Anthropic↑11pp58→69¢ · Kalshi
- Sep 5OpenAI↓9pp29→20¢ · Kalshi
- Sep 4Anthropic↑8pp50→58¢ · Kalshi
- Sep 4OpenAI↓7pp36→29¢ · Kalshi
- Sep 2OpenAI↑6pp25→31¢ · Kalshi
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These markets stopped trading. Last odds and any captured outcome are shown above — full settlement detail lives at the venue.
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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.
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