AARRI-Bench (Claude Code) - Context: leaderboard
AARRI-Bench (Act As a Real Research Intern, 2026) is a set of 82 hand-crafted AI-research tasks graded by unit tests, testing whether an agent harness and model behave like a careful human researcher. This board is the percentage of the context tasks, which need judgment about the state of a field solved in full with the Claude Code harness; higher is better.
Metric: Task success rate (%) under the 0/1 reward on the context tasks: sensitivity to the state of a field (core contributions, data validity, genuine advances); Harbor containerized runs, Claude Code agent harness; a configuration's tasks without a valid run are left out of its denominator; higher is better. Source: arxiv.org. Saturation forecast: Around June 2027. 6 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | Claude Opus 4.7 | 55.9 | #79 |
| 2 | Claude Sonnet 4.6 | 50 | #161 |
| 3 | Qwen 3.6 Plus | 50 | #222 |
| 4 | MiniMax-M2.7 | 47.1 | #518 |
| 5 | GPT-5.3 Codex | 47.1 | #116 |
| 6 | Kimi K2.6 | 45.5 | #178 |
No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.
Interactive version: theaggregate.ai/benchmark?slug=aarri-bench-claude-code-context · How It Works · Data refreshed daily, snapshot 2026-11-13.