OctoCodingBench - MiniMax's open-source Coding Agent benchmark suite

What is OctoCodingBench?

OctoCodingBench is an open-source benchmark suite from MiniMax that assesses the instruction-following capabilities of Coding Agents. By simulating real-world software development scenarios, OctoCodingBench evaluates an Agent's ability to strictly adhere to rules and complete tasks across multiple dimensions, including system constraints, user instructions, project specifications, skill invocation, and historical memory. Unlike traditional benchmarks that focus solely on results, OctoCodingBench prioritizes process compliance, meticulously measuring Agent performance under complex constraints through Check-level Accuracy (CSR) and Instance-level Success Rate (ISR). The benchmark suite contains 72 instances covering various development scenarios, driving the evolution of Coding Agents from simply "being able to write code" to "being able to collaborate in a standardized manner."

The main functions of OctoCodingBench

• Multi-dimensional instruction compliance assessmentThe system comprehensively evaluates whether the Agent can strictly follow the rules to complete the task from multiple dimensions, including system constraints (such as language style and tool usage), user instructions (multi-turn interactions), project specifications (such as code style and testing process), skill invocation, and historical memory.
• Separate task completion and rule complianceThe Agent's performance is measured by two metrics: Check-level accuracy (CSR) and Instance-level success rate (ISR), which respectively measure the Agent's performance under single constraints and all constraints, revealing the Agent's process compliance.
• Real-world development scenario simulationIt provides 72 carefully designed instances, each containing natural language user queries, system prompts, project documentation, skill documentation, etc., simulating a real software development task environment.
• Conflict Detection and Resolution Capability TestDesign scenarios that include conflicting instructions and evaluate the Agent's ability to prioritize and resolve conflicts when faced with conflicting instructions.
• Supports multiple development frameworksIt provides configurations and Docker environments for various development frameworks (such as Claude Code, Kilo, and Droid) to ensure that evaluations are conducted in a real production environment.
• Binary list scoringEach evaluation item is objectively judged (pass/fail), ensuring the transparency and repeatability of the evaluation results.

The technical principles of OctoCodingBench

• Multi-source instruction setOctoCodingBench categorizes instruction sources into seven types: system prompts, system alerts, user queries, project documentation (such as CLAUDE.md and AGENTS.md), skill documentation, historical records, and tool call guidelines. Each category has different levels of authority and constraints.
• Structured assessment checklistEach evaluation instance comes with a detailed structured evaluation checklist containing multiple binary decisionable check-items. The check-items cover various aspects from language style to code implementation.
• Docker environment simulationTo ensure consistency in the evaluation environment, OctoCodingBench provides 34 different Docker images, each simulating a real development environment. This development environment includes project code, dependency libraries, testing tools, etc., ensuring that the Agent runs under conditions consistent with the actual development environment.
• LLM-as-Judge scoring mechanismLarge Language Models (LLMs) are used as a scoring tool to evaluate the agent's behavioral trajectories item by item. Based on a predefined evaluation checklist, the LLM determines whether the agent meets each constraint and provides a pass or fail result.
• Data collection and trajectory analysisDuring the evaluation process, the system collects the agent's complete interaction trajectory, including system prompts, user queries, agent responses, and tool calls. This trajectory data is used for subsequent scoring analysis.
• Statistics and AnalysisThe overall performance of the agent is quantitatively analyzed by calculating two metrics: CSR and ISR. CSR measures the agent's accuracy on a single constraint, while ISR measures the agent's success rate when all constraints are satisfied simultaneously.

OctoCodingBench project address

• HuggingFace model libraryhttps://huggingface.co/datasets/MiniMaxAI/OctoCodingBench

Application scenarios of OctoCodingBench

• Agent Development and TrainingBy standardizing evaluation, we optimize the training process of the Coding Agent to ensure excellent performance in following instructions and process compliance, not just generating correct code.
• Software Engineering and Development: Evaluate whether the Coding Agent follows project standards (such as code style and testing procedures) to improve code quality and ensure team collaboration efficiency.
• Academic Research and EvaluationIt provides standardized benchmarks to compare the performance of different Coding Agent models in terms of instruction compliance and process compliance, and provides data support for research directions.
• Education and TrainingIt helps developers and students understand the behavior patterns of Coding Agents, learn how to design effective instructions, and improve their ability to apply them in practical development.