Multi-Model Response Comparator
Compare answers from multiple AI models for the same prompt, then summarize tradeoffs across quality, style, and likely use cases.
When to use
- - choosing between models for a workflow
- benchmarking prompt behavior
- checking whether a stronger model is worth the cost
- generating second opinions on important outputs
Recommended runtime
This skill works with OpenAI-compatible runtimes and has been tested on Crazyrouter.
Required output format
Always structure the final comparison with these sections:
- 1. Task summary
- Models compared
- Strengths by model
- Weaknesses by model
- Best model by use case
- Cost/latency sensitivity note
- Final recommendation
Suggested workflow
- 1. pick 2-4 models
- run the same prompt on each model
- compare structure, depth, correctness, tone, and likely latency/cost
- score or describe tradeoffs using the comparison rubric
- produce a recommendation by use case, not just one universal winner
Comparison rules
- - Use the same prompt and same success criteria for all models.
- Do not claim exact cost or latency unless the user provides them.
- If metrics are inferred, label them as likely or expected.
- Separate writing quality from factual reliability.
- For coding tasks, prioritize correctness, edge cases, and implementation completeness.
Example prompts
- - Compare GPT, Claude, and Gemini on this support email draft.
- Run this coding prompt across three models and summarize which one is most production-ready.
- Compare low-cost vs premium models for a blog outline task.
References
Read these when preparing the final comparison:
Crazyrouter example
CODEBLOCK0
Recommended artifacts
- - catalog.json
- provenance.json
- market-manifest.json
- evals/evals.json
多模型响应比较器
针对同一提示词，比较多个AI模型给出的答案，然后总结其在质量、风格及潜在用例方面的权衡。
适用场景
- - 为工作流程选择模型
- 对提示词行为进行基准测试
- 检查更强模型是否物有所值
- 对重要输出寻求第二意见
推荐运行环境
该技能兼容OpenAI兼容的运行环境，并已在Crazyrouter上完成测试。
必需输出格式
最终比较结果必须包含以下部分：
- 1. 任务摘要
- 比较的模型
- 各模型优势
- 各模型劣势
- 按用例划分的最佳模型
- 成本/延迟敏感性说明
- 最终推荐
建议工作流程
- 1. 选择2-4个模型
- 对每个模型运行相同的提示词
- 比较结构、深度、正确性、语气以及可能的延迟/成本
- 使用比较评分标准对权衡进行评分或描述
- 按用例给出推荐，而非仅选出一个通用优胜者
比较规则
- - 对所有模型使用相同的提示词和相同的成功标准。
- 除非用户提供，否则不声称精确的成本或延迟数据。
- 如果指标为推断得出，需标注为可能或预期。
- 将写作质量与事实可靠性分开评估。
- 对于编码任务，优先考虑正确性、边界情况和实现完整性。
示例提示词
- - 比较GPT、Claude和Gemini对此支持邮件草稿的处理。
- 对三个模型运行此编码提示词，并总结哪个最接近生产就绪状态。
- 比较低成本模型与高级模型在博客大纲任务中的表现。
参考资料
准备最终比较时请阅读以下资料：
- - references/comparison-rubric.md
- references/example-prompts.md
Crazyrouter示例
python
from openai import OpenAI
client = OpenAI(
apikey=YOURAPI_KEY,
base_url=https://crazyrouter.com/v1
)
推荐制品
- - catalog.json
- provenance.json
- market-manifest.json
- evals/evals.json
