FrontierCS 2 - Polar-Factor Approximation: 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 Superseded attempts are not selected by score.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 October 2027. 14 models tracked.

Top models

#ModelScoreOverall rank
1Gemini 4 Argon (High)71.7#8 (Gemini 4 Argon)
2Claude Opus 5.5 (Max)67.81#5 (Claude Opus 5.5)
3GLM-5.3 (Max)64.82#39 (GLM-5.3)
4GPT-6 Astra (xHigh)63.55#6 (GPT-6 Astra)
5DeepSeek V4.1 Flash (1017)62.81#27
6DeepSeek V4.1 Flash (Max)61.58#35 (DeepSeek V4.1 Flash)
7Kimi K3 (High)54.23#33 (Kimi K3)
8GPT-6.1 Sol (xHigh)53.34#7 (GPT-6.1 Sol)
9Muse Wonder (Max)52.73#15 (Muse Wonder)
10Muse Spark 1.3 (High)52.13#29 (Muse Spark 1.3)
11GLM-5.3 Flash (Max)0#72 (GLM-5.3 Flash)

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

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