by iammrduncan · Agent Tool · ★ 38
typesafe-ai-benchmark LLM-native structured output vs. TypeSafe Jev: latency, cost, and judgment quality. Hackers in the Loop How does a fast general-purpose LLM compare with a purpose-built judgment model on the same application tasks? This benchmark runs Qwen 3.8 27B on Cerebras and TypeSafe Jev side by side across seven synthetic workloads, with a separate local Needle 3 evaluation on the same contracts. It records validated outputs, mistakes, request latency, token usage and estimated cost.
| Stars | 38 |
| Forks | 5 |
| Language | TypeScript |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 63.1799742186279/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-19 |
| Created | 2026-09-16 |
| Platforms | node |
| Est. Tokens | ~16k |
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typesafe-ai-benchmark is This is a LLM Gateway that mimics typesafe ai structured output. Like an imposter Jev.. It is categorized as a Agent Tool with 38 GitHub stars.
typesafe-ai-benchmark is primarily written in TypeScript.
You can find installation instructions and usage details in the typesafe-ai-benchmark GitHub repository at github.com/iammrduncan/typesafe-ai-benchmark. The project has 38 stars and 5 forks, indicating an active community.
typesafe-ai-benchmark is released under the MIT license, making it free to use and modify according to the license terms.