MCP-Persona - Lark-Centric Cross-Server: leaderboard
Metric: Checkpoint accuracy (%): mean LLM-judged score (0, 0.5 or 1) of the sub-task checkpoints in a task, averaged over tasks, cross-server tasks centred on Lark; 173 human-verified personal-application tasks on 12 simulated MCP servers (Lark, Slack, WeCom, Notion, Obsidian, Rednote, Reddit, Instagram, email and search tools), up to 20 tool-calling rounds, GPT-4o judge; higher is better. Source: arxiv.org. Saturation forecast: Around 2034. 13 models tracked.
Top models
| # | Model | Score |
|---|---|---|
| 1 | Claude Sonnet 4.5 | 40.81 |
| 2 | Claude Opus 4.1 | 39.67 |
| 3 | GPT-5 | 37.67 |
| 4 | O3 | 34.64 |
| 5 | GPT-4o (2024-11-20) | 30.65 |
| 6 | O4 Mini | 30.43 |
| 7 | Qwen 3 235B A22B 2507 Instruct | 30.4 |
| 8 | Qwen3 Coder | 29.8 |
| 9 | DeepSeek V3 (0324) | 27.91 |
| 10 | Grok 4 | 26.78 |
| 11 | Gemini 2.5 Pro | 18.23 |
| 12 | Gemini 3 Pro | 14.6 |
Interactive version: theaggregate.ai/benchmark?slug=mcp-persona-lark-centric-cross-server · How It Works · Data refreshed daily, snapshot 2026-09-29.