FrontierCS 2 - Learned Transformer Layer Mapping: 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 Superseded attempts are not selected by score.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 2029. 14 models tracked.

Top models

#ModelScoreOverall rank
1Gemini 4 Argon (High)70#8 (Gemini 4 Argon)
2GPT-6 Astra (xHigh)70#6 (GPT-6 Astra)
3Claude Opus 5.5 (Max)69.62#5 (Claude Opus 5.5)
4GLM-5.3 Flash (Max)50.69#72 (GLM-5.3 Flash)
5DeepSeek V4.1 Flash (1017)49.71#27
6DeepSeek V4.1 Flash (Max)49.38#35 (DeepSeek V4.1 Flash)
7GLM-5.3 (Max)48.73#39 (GLM-5.3)
8Muse Wonder (Max)48.59#15 (Muse Wonder)
9Kimi K3 (High)48.36#33 (Kimi K3)
10Muse Spark 1.3 (High)30#29 (Muse Spark 1.3)
11GPT-6.1 Sol (xHigh)0#7 (GPT-6.1 Sol)

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

Interactive version: theaggregate.ai/benchmark?slug=frontiercs-2-learned-transformer-layer-mapping · How It Works · Data refreshed daily, snapshot 2026-10-26.