FrontierCS 2 - Sparse Video Attention Policy: 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 Superseded attempts are not selected by score.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 2033. 14 models tracked.

Top models

#ModelScoreOverall rank
1GPT-6 Astra (xHigh)48.59#6 (GPT-6 Astra)
2DeepSeek V4.1 Flash (1017)41.04#27
3GPT-6.1 Sol (xHigh)38.42#7 (GPT-6.1 Sol)
4Claude Opus 5.5 (Max)37.42#5 (Claude Opus 5.5)
5Kimi K3 (High)36.86#33 (Kimi K3)
6DeepSeek V4.1 Flash (Max)36.01#35 (DeepSeek V4.1 Flash)
7Gemini 4 Argon (High)34.98#8 (Gemini 4 Argon)
8Muse Spark 1.3 (High)34.88#29 (Muse Spark 1.3)
9GLM-5.3 Flash (Max)34.44#72 (GLM-5.3 Flash)
10Muse Wonder (Max)34.25#15 (Muse Wonder)
11GLM-5.3 (Max)15.35#39 (GLM-5.3)

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

Interactive version: theaggregate.ai/benchmark?slug=frontiercs-2-sparse-video-attention-policy · How It Works · Data refreshed daily, snapshot 2026-10-26.