tinybirdco/llm-benchmark
We assessed the ability of popular LLMs to generate accurate and efficient SQL from natural language prompts. Using a 200 million record dataset from the GH Archive uploaded to Tinybird, we asked the LLMs to generate SQL based on 50 prompts.
86
Stars
9
Forks
37.8
Radar Score
Stable
Momentum
Scoring Breakdown
Impact23.1
Velocity35.2
Health65.0
Growth Metrics
7-Day Stars
+0
30-Day Stars
+0
Acceleration
x
Language
TypeScript
Why it ranks
Steady growth — consistently collecting stars and contributors.
LLM FrameworksTop 66% globally
Popular Alternatives & Competitors
Top projects in the same category (LLM Frameworks) — compare side by side.
🛡️ Enterprise Readiness & Commercial Safety
Organic Growth
95%
High Confidence
Growth pattern looks organic.
License
Unlicensed / Custom
Review license terms carefully before commercial adoption.
Health Grade
A
Based on maintenance, issue response, and release cadence.
Radar Score
37.8
Composite of Impact, Velocity, and Health.
Disclaimer: This is an automated heuristic assessment based on public GitHub signals. Always perform your own security audit, dependency review, and legal compliance check before production deployment.
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