Which model leads AlpacaEval2.0 (LC-winrate)?

Q: Which model leads AlpacaEval2.0 (LC-winrate)?

DeepSeek-R1 tops the board at 87.6

AlpacaEval2.0 (LC-winrate) Leaderboard

Pro Mode — raw scores, one benchmark under the lensofficial card harness

The field · 6 measured

24.787.6

6 models measured, most on the official card harness. DeepSeek-R1 tops the board at 87.6.

6 measured·Lifecycle Updated Oct 8, 2026Score basis

#◆Vendor$/M in$/M out

Leader — ignites whiteField — fill cools with scoreIndependently verified only0 of 6Bars show 24.7–87.6 · ◆ solid = first-party or better · outlined = arm's-length

01DeepSeek-R1+5 altsDeepSeek87.6$2.0/M$4.0/M$0.034

02DeepSeek-V3+4 altsDeepSeek70$0.24/M$0.90/M$0.008

03O1 Mini+4 altsOpenAI57.8———

04Claude 3.5 Sonnet+4 altsAnthropic52$3.0/M$15.0/M$0.173

05GPT-4o+4 altsOpenAI51.1$5.0/M$15.0/M$0.196

06DeepSeek-R1-ZeroDeepSeek24.7———

◆ solid = independently verified · aggregator attested · vendor attributed — outlined = cross-reference · source-attributed. Dimmed ◆ = confidence inferred. “+N alts” = alternate disclosures in the drawer with why-demoted. Prices per million tokens; $/point = input price ÷ score. Every row opens its model dossier.

Verification: 6 models scored · 0 independently verified · 3 vendor cross-reference · 3 vendor-reported · 0 with source disagreement. How these tiers are assigned