Toolkit for measuring Claude Code and Codex performance over time against a baseline using SWEbench-lite dataset. No API key required for Max or Pro subscribers. Useful for operations teams to evaluate and compare code-focused language models on real-world software engineering tasks.
git clone https://github.com/jimmc414/claudecode_gemini_and_codex_swebench.gitThe claudecode_gemini_and_codex_swebench skill is an essential toolkit designed for measuring the performance of Claude Code and Codex over time. Utilizing the SWEbench-lite dataset, this skill provides users with a robust framework to establish a performance baseline and track changes effectively. With no API key required for Max or Pro subscribers, this skill is accessible and straightforward to implement, making it a valuable addition to any AI automation toolkit. One of the key benefits of this skill is its ability to provide insights into the performance metrics of your AI models. By measuring performance against a baseline, developers and AI practitioners can identify trends, optimize workflows, and enhance the overall efficiency of their AI agents. Although the exact time savings are currently unknown, the ability to quickly assess model performance can lead to faster iterations and improved deployment times, ultimately saving resources in the long run. This skill is particularly beneficial for developers, product managers, and AI practitioners who are focused on optimizing AI workflows. By integrating this skill into their processes, teams can ensure that they are leveraging the full potential of their AI models, making informed decisions based on performance data. For example, a product manager might use this skill to evaluate the effectiveness of different AI models in a product feature, allowing for data-driven decisions that enhance user experience. With an intermediate implementation difficulty, users can expect to spend about 30 minutes setting up the claudecode_gemini_and_codex_swebench skill. This skill fits seamlessly into AI-first workflows, providing a structured approach to performance measurement that is essential for continuous improvement. By adopting this skill, teams can enhance their AI automation capabilities, leading to more effective and efficient solutions.
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/jimmc414/claudecode_gemini_and_codex_swebenchCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Evaluate the performance of [MODEL_NAME] using the SWEbench-lite dataset. Compare its accuracy, efficiency, and reliability against [BASELINE_MODEL] over a [TIME_PERIOD] period. Provide detailed metrics and insights for [COMPANY] in the [INDUSTRY] sector.
# Performance Evaluation: Claude Code vs. Codex ## Overview This evaluation compares the performance of Claude Code and Codex over a 3-month period using the SWEbench-lite dataset. The assessment focuses on accuracy, efficiency, and reliability in software engineering tasks. ## Key Metrics - **Accuracy**: - Claude Code: 87.5% - Codex: 82.3% - **Efficiency**: - Claude Code: 92.1% faster response time - Codex: 85.7% faster response time - **Reliability**: - Claude Code: 95.2% task completion rate - Codex: 90.4% task completion rate ## Insights Claude Code outperformed Codex in all key metrics. The most significant improvement was in response time, making it a better choice for time-sensitive tasks. Both models showed high reliability, but Claude Code's superior accuracy makes it the preferred option for critical software engineering tasks.
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