FrontierCS 2 - Differentiable Population Statistics: leaderboard

FrontierCS 2 selected formal runs on 48 research engineering tasks, with each source-native coding harness. Paper v1 Table 3 fixes the effort settings; the subsequently added DeepSeek 1017 row leaves effort unspecified. Superseded attempts are not selected by score. Native bounded task scores are distinct from human-reference margins and from pass/fail verdicts. Explicit failed-run zeroes remain zero; unreported scores remain missing. Metric: Task score (0–100).

Metric: Task score (0–100). Source: frontier-cs.org. Saturation forecast: Around 2034. 14 models tracked.

Top models

#ModelScoreOverall rank
1Claude Opus 5.5 (Max)7.16#5 (Claude Opus 5.5)
2Muse Wonder (Max)5.02#15 (Muse Wonder)
3Gemini 4 Argon (High)4.2#8 (Gemini 4 Argon)
4DeepSeek V4.1 Flash (Max)4.16#35 (DeepSeek V4.1 Flash)
5GLM-5.3 (Max)3.96#39 (GLM-5.3)
6DeepSeek V4.1 Flash (1017)3.85#27
7GLM-5.3 Flash (Max)3.81#72 (GLM-5.3 Flash)
8GPT-6 Astra (xHigh)3.65#6 (GPT-6 Astra)
9Kimi K3 (High)3.55#33 (Kimi K3)
10GPT-6.1 Sol (xHigh)3.08#7 (GPT-6.1 Sol)
11Muse Spark 1.3 (High)2.88#29 (Muse Spark 1.3)

No result here: #9 Claude Fable 5.1, #10 Muse Wonder Full.

Interactive version: theaggregate.ai/benchmark?slug=frontiercs-2-differentiable-population-statistics · How It Works · Data refreshed daily, snapshot 2026-10-26.