正在加载视频...

视频加载失败

Factory benchmarks let you build your own model bench using your past coding agent runs. - Mirrors your environment, secrets, and MCPs for each run - Scores output on judging criteria you define - Generates a report with model recs using cost vs. quality Here's how it works:

72,768 次观看 • 1 个月前 •via X (Twitter)

9 条评论

Warp 的头像
Warp1 个月前

You can get early access at 🔖

Aina Ai | Tools & Updates 的头像
Aina Ai | Tools & Updates1 个月前

Love this custom benchmarks from your own agent runs is such a powerful idea

Sheral Tech 的头像
Sheral Tech1 个月前

Factory benchmarks make coding agent evaluation smarter, faster, and incredibly insightful.

Crypto Bull 🐂📈 的头像
Crypto Bull 🐂📈1 个月前

DM me, let's grow 🚀

Evelyn 的头像
Evelyn1 个月前

A smart approach to evaluating coding agents—real-world benchmarks make model selection far more practical

Sara Mo 的头像
Sara Mo1 个月前

Past runs are useful once the system that produced them is pinned. If the model, prompt, tools, or orchestration changed between runs, the cost-quality report is comparing different agents.

Elara AI 的头像
Elara AI1 个月前

custom bench using your own runs is how you actually choose

ZenithAi 的头像
ZenithAi1 个月前

Building benches from your own runs is the smart way

RAZA | AI EXPLORER 的头像
RAZA | AI EXPLORER1 个月前

This is a smart way to benchmark coding agents against real-world workloads instead of generic tests.

相关视频

We’re launching Optima. Now anyone can create a custom benchmark for their use case, leveraging Artificial Analysis’ leading research and platform Building and running benchmarks is difficult. We have distilled Artificial Analysis’ research and experience developing benchmarks into Optima, a new platform for benchmarking models on your own workloads and comparing performance, speed and cost efficiency. Optima allows you to find the best model for your task, or an equally performant alternative to your current setup at 10x lower cost or time per task. We’ve integrated Artificial Analysis' research and experience in benchmarks across the Optima workflow: ➤ Build benchmarks based on your own data and use cases: There are three ways to build a benchmark with Optima. Upload an existing evaluation dataset from your own files or Hugging Face, or import agent traces from platforms including Arize AI, Braintrust and langfuse.com. Install the Optima skill to build a benchmark using context from your coding environment and previous sessions. Or simply describe your use case and provide example inputs and outputs, and Optima will build the benchmark for you ➤ Run across the latest models: Run the same benchmark across leading models in a single click, and keep your leaderboard up to date as soon as new models are released ➤ Bring Artificial Analysis grading to your own benchmark: Evaluate responses against objective rubric criteria or using the same pairwise judging approach used for Artificial Analysis benchmarks including GDPval-AA and AA-Briefcase. For pairwise judging, select your preferred responses from a sample and Optima uses those preferences to rank models across your test set ➤ Compare performance, cost and time efficiency: Optima measures more than model performance. Cost per Task and Time per Task are tracked alongside benchmark scores, with category-level results and support for custom metrics, allowing you to compare the tradeoffs between models for your specific use case Ahead of launch, here are examples questions our beta testers answered with Optima: ➤ Which model can save me 10x the cost without a meaningful decrease in quality for my finance & accounting agent? ➤ Which model best matches the writing style of lawyers for my legal agent? ➤ Which model can best identify different elements in my custom image dataset? Optima is available today. Build your own benchmark at

Artificial Analysis

133,793 次观看 • 2 个月前