LUNAR - Agentic Memory: leaderboard

Personalized response quality using memory compressed into atomic facts before retrieval, averaging GPT-5.1-judged personalization coverage and depth across daily-life domains such as clothing, food, housing and mobility (LUNAR, arXiv 2608.05246v2, Table 4). 1-5 scale; higher is better; 19 models.

Metric: Personalization score (1-5; mean of coverage and depth, GPT-5.1 judge). Source: arxiv.org. Saturation forecast: Around 2029. 19 models tracked.

Top models

#ModelScoreOverall rank
1Kimi K2.62.97#113
2Gemini 3 Flash (Preview)2.9#90
3GLM-5.12.9#109
4DeepSeek V4 Flash2.81#155
5MiniMax-M2.72.75#270
6GPT-4.1 Mini2.69#363
7DeepSeek V4 Pro2.66#112
8Qwen 3 30B A3B 2507 Instruct2.56#492
9Ling-2.6-1T2.54#307
10GPT-4o Mini2.48#598
11GLM-4.7 Flash2.4#543
12Ling-2.6-flash2.4#533
13Qwen 3 14B (Non-reasoning)2.36#542 (Qwen 3 14B)
14Qwen 3 8B (Non-reasoning)2.24#678 (Qwen 3 8B)
15Qwen 3 32B (Non-reasoning)2.2#449 (Qwen 3 32B)

No result here: #3 Claude Opus 5.5, #6 GPT-6 Astra, #7 GPT-6.1 Sol.

Interactive version: theaggregate.ai/benchmark?slug=lunar-agentic-memory · How It Works · Data refreshed daily, snapshot 2026-11-05.