PostTrainBench
Benchmark post-training performance of CLI agents on H100 GPU in 10 hours.
Pricing
Free tier
Flat rate
Adoption
→StableLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is PostTrainBench?
PostTrainBench evaluates the efficiency and effectiveness of CLI-based AI agents like Claude Code or Codex CLI when post-training base LLMs within a constrained time frame using a single H100 GPU. It is crucial for developers aiming to optimize their machine learning workflows under strict resource limitations.
Key differentiator
“PostTrainBench stands out as a specialized tool for evaluating the post-training performance of CLI-based AI agents under strict time and resource constraints, offering unique insights into efficiency and effectiveness on single H100 GPUs.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
API requires Python-specific patterns, TypeScript SDK is community-maintained
v0.1 to v0.2 migration required rewriting chain definitions
Designed specifically for evaluation on a single H100 GPU; lacks support for distributed computing
Evaluation within a fixed timeframe might not reflect real-world performance variability
Fit analysis
Who is it for?
✓ Best for
Teams needing to evaluate the efficiency and effectiveness of CLI-based AI agents in a constrained GPU environment.
Developers looking for detailed benchmarks on post-training performance under strict time and resource constraints.
✕ Not a fit for
Projects requiring real-time streaming or continuous training processes.
Budget-constrained projects where open-source solutions are not preferred.
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Next step
Get Started with PostTrainBench
Step-by-step setup guide with code examples and common gotchas.