chinese-llm-benchmark vs PostTrainBench
chinese-llm-benchmark is much bigger: 6.5k stars against 611. Over the days we have tracked them chinese-llm-benchmark moved +2.3% and PostTrainBench +26.2%, so PostTrainBench is growing faster right now.
PostTrainBench leads on context depth and model freedom. chinese-llm-benchmark does not take any axis by a clear margin.
Stars and commit dates come from our own daily tracking. The six axes are read off each project's documentation by our review pipeline, so they describe what a project says about itself, not what we measured in its code.
Where they stand today
Chinese-first, large-scale benchmark that covers hundreds of commercial and open-source models and ships a >2M-entry defect database for model diagnosis.
- Stars
- 6.5k
- Tracked growth
- +2.3%
- Maturity
- ●●●●●
- Last commit
- 4d ago
- Language
- mixed
- License
- none declared
- Cost to run
- Requires Nonelinear API key (cloud service)
Measures autonomous CLI agents' ability to post-train base LLMs within a 10‑hour H100 budget, evaluating agent-driven R&D rather than only inference.
- Stars
- 611
- Tracked growth
- +26.2%
- Maturity
- ●●●●●
- Last commit
- 29d ago
- Language
- Python
- License
- MIT
- Cost to run
- Your API keys + H100 GPU (cluster or rented)
Six axes, head to head
Each axis runs 0 to 5. The label under a score is what that project's own docs claim, not a category average.
| Axis | chinese-llm-benchmark | PostTrainBench |
|---|---|---|
Context depth How much of your codebase it sees before it answers: the open diff, the diff plus related files, or the whole repository. | ●●●●● Diff only | ●●●●● Whole-repo access |
Noise control How it keeps output volume down — severity thresholds, deduplication, incremental runs over new commits only. | ●●●●● Basic scoring filters | ●●●●● Judge + rules |
Customization How far it bends to your team: custom rules, prompts, style guides, per-path config. | ●●●●● Config & custom data | ●●●●● Configurable CLI options |
Privacy Whether your code stays on your own infrastructure: fully local, self-hostable, or cloud API only. | ●●●●● Cloud API (your key) | ●●●●● APIs with your key |
Model freedom Whether you can point it at any provider, or it is wired to one. | ●●●●● Gateway to many models | ●●●●● BYO key + local models |
Setup ease What it takes to get a first useful run out of it. | ●●●●● API key required | ●●●●● Container + cluster setup |
Which one to pick
Pick chinese-llm-benchmark if…
Chinese-focused — pick this when you need the most extensive, regularly updated multi-domain Chinese LLM benchmark and a huge defect corpus for analysis.
Pick PostTrainBench if…
Autonomy-focused — use this to benchmark end-to-end agent-driven post-training of base models on an H100 with built-in evaluation tasks and reward‑hacking mitigations.
- Context depth: Whole-repo access (5/5 against 1/5)
- Model freedom: BYO key + local models (5/5 against 3/5)
What people want from each one
jeinlee1991/chinese-llm-benchmark
aisa-group/PostTrainBench
Questions people ask
Is chinese-llm-benchmark better than PostTrainBench?
PostTrainBench leads on context depth and model freedom. chinese-llm-benchmark does not take any axis by a clear margin. chinese-llm-benchmark is worth picking when chinese-focused — pick this when you need the most extensive, regularly updated multi-domain Chinese LLM benchmark and a huge defect corpus for analysis.
Which of chinese-llm-benchmark and PostTrainBench keeps my code private?
chinese-llm-benchmark: Cloud API (your key) (3/5). PostTrainBench: APIs with your key (3/5).
What does each one cost to run?
chinese-llm-benchmark: Requires Nonelinear API key (cloud service). PostTrainBench: Your API keys + H100 GPU (cluster or rented).
Full profiles: jeinlee1991/chinese-llm-benchmark and aisa-group/PostTrainBench. Everything else in Evals & benchmarks.