FrontierCS 2 - Dynamic GPU Allocation: 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 Superseded attempts are not selected by score.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 2035. 14 models tracked.

Top models

#ModelScoreOverall rank
1Gemini 4 Argon (High)47.29#8 (Gemini 4 Argon)
2GPT-6 Astra (xHigh)47.17#6 (GPT-6 Astra)
3Claude Opus 5.5 (Max)47.09#5 (Claude Opus 5.5)
4GPT-6.1 Sol (xHigh)46.82#7 (GPT-6.1 Sol)
5GLM-5.3 (Max)46.54#39 (GLM-5.3)
6Kimi K3 (High)46.45#33 (Kimi K3)
7GLM-5.3 Flash (Max)46.33#72 (GLM-5.3 Flash)
8DeepSeek V4.1 Flash (Max)46.19#35 (DeepSeek V4.1 Flash)
9Muse Spark 1.3 (High)44.89#29 (Muse Spark 1.3)
10Muse Wonder (Max)44.48#15 (Muse Wonder)
11DeepSeek V4.1 Flash (1017)44.2#27

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

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