SlackBench - MCP High - Agentic Search: leaderboard
Agents answer questions across realistic Slack workplace reenactments and associated evolving code repositories. Mean category pass score (%). SlackBench140tasks:58AgenticSearch,48Hallucination,14Summarization,20CircumstanceInference; three runs,20min/query; GPT-5.4 judge. Summarization earns checklist partial credit, other categories binary pass; Overall is the unweighted category mean. Harness, MCP/CLI access and reasoning effort remain separate in row identities. Native first-party model APIs.
Metric: Mean category pass score (%). Source: arxiv.org. Saturation forecast: Rough model projection: around 2026. 5 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | Claude Opus 4.7 (Benchmark Sweep Run: source=SlackBench; harness=Claude Code; access=MCP; effort=high) | 58.62 | |
| 2 | GPT-5.5 (Benchmark Sweep Run: source=SlackBench; harness=Codex CLI; access=MCP; effort=high) | 58.62 | |
| 3 | Gemini 3.1 Pro (Preview) (Benchmark Sweep Run: source=SlackBench; harness=Gemini CLI; access=MCP; effort=high) | 58.62 | |
| 4 | Claude Sonnet 4.6 (Benchmark Sweep Run: source=SlackBench; harness=Claude Code; access=MCP; effort=high) | 55.17 | |
| 5 | GPT-5.4 Mini (Benchmark Sweep Run: source=SlackBench; harness=Codex CLI; access=MCP; effort=high) | 41.38 |
Interactive version: theaggregate.ai/benchmark?slug=slackbench-mcp-high-agentic-search · How It Works · Data refreshed daily, snapshot 2026-11-06.