Sign inGet started
Dataset · Hugging Face

MiniMaxAI/OctoCodingBench

OctoCodingBench: Instruction-Following Benchmark for Coding Agents English | 中文 🌟 Overview OctoCodingBench benchmarks scaffold-aware instruction following in repository-grounded agentic coding. Why OctoCodingBench? Existing benchmarks (SWE-bench, etc.) focus on task completion — whether the agent produces correct code. However, they miss a critical dimension: does the agent follow the rules while solving the task? In real-world agentic coding… See the full description on the dataset page: https://huggingface.co/datasets/MiniMaxAI/OctoCodingBench.

Author / organizationMiniMaxAI
License (as declared)mit
Downloads486 (as reported by Hugging Face)
Likes413
Created9 January 2026
Last updated at source13 January 2026
codeagentbenchmarkevaluation
Embed this entry
<iframe src="https://silytics.si/embed/item/676f6316-02ca-4eb1-9d91-3b002a3c67b9" width="420" height="190" style="border:0" loading="lazy" title="Silytics entry"></iframe>

Free, no tracking. More about feeds and embeds.

Recent changes

  • 16 Oct 2026 Description: OctoCodingBench: Instruction-Following Benchmark for Coding Agents English | 中文 🌟 Overview OctoCodingBench benchmarks scaffold-aware instruction following in repository-grounded agentic coding. Why OctoCodingBench? Existing benchmarks (SWE → OctoCodingBench: Instruction-Following Benchmark for Coding Agents English | 中文 🌟 Overview OctoCodingBench benchmarks scaffold-aware instruction following in repository-grounded agentic coding. Why OctoCodingBench? Existing benchmarks (SWE

Changes detected by Silytics between refreshes of the source.

More from MiniMaxAI

Silytics shows public metadata from Hugging Face and links to the original. Information may be incomplete or out of date. Last refreshed 16 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.