FrontierCS 2 - Latent Objective Recovery: 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 Superseded attempts are not selected by score.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: Estimated already saturated. 14 models tracked.

Top models

#ModelScoreOverall rank
1GPT-6 Astra (xHigh)99.49#6 (GPT-6 Astra)
2DeepSeek V4.1 Flash (Max)99.48#35 (DeepSeek V4.1 Flash)
3GPT-6.1 Sol (xHigh)99.45#7 (GPT-6.1 Sol)
4Claude Opus 5.5 (Max)99.41#5 (Claude Opus 5.5)
5Gemini 4 Argon (High)99.4#8 (Gemini 4 Argon)
6DeepSeek V4.1 Flash (1017)98.93#27
7Muse Wonder (Max)98.92#15 (Muse Wonder)
8Kimi K3 (High)98.84#33 (Kimi K3)
9GLM-5.3 Flash (Max)98.81#72 (GLM-5.3 Flash)
10Muse Spark 1.3 (High)98.29#29 (Muse Spark 1.3)
11GLM-5.3 (Max)97.34#39 (GLM-5.3)

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

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