AI benchmarks and leaderboards are different things
Learn the difference between a benchmark, an evaluation run and a leaderboard, and use each to answer the right question.
LLM Stats
PostTrainBench
PostTrainBench evaluates a model's ability to autonomously post-train base models. Given pretrain-only base models, the agent must complete the full pipeline of data synthesis, training, evaluation, and iteration within a time budget, scored across downstream benchmarks such as AIME2025, BFCL, GPQA Main, GSM8K, and HumanEval.
Evaluation context and evidence
- Source
- LLM Stats
- Publisher
- Not recorded
- Benchmark release
- Not recorded
- Collected
- 2026-08-17
- Modality
- text
These are source-reported observations, not independent reruns by this site. Check the task version, evaluation protocol, unit and reporting date before comparing models. Results from different setups are not a universal ranking.
Original sources
Reported results 6
Shown in source observation order, not ranked. Dates retain the precision provided by the source.
| Model | Value | Unit | Protocol | Reported date and basis | Source |
|---|---|---|---|---|---|
| GLM-5.3 | 0.398 | Not recorded | Not recorded | 2026-08-14 · model_announcement | LLM Stats |
| MiniMax M3 | 0.371 | Not recorded | Not recorded | 2026-06-01 · model_announcement | LLM Stats |
| Kimi K3 | 0.366 | Not recorded | Not recorded | 2026-07-16 · model_announcement | LLM Stats |
| GLM-5.2 | 0.343 | Not recorded | Not recorded | 2026-06-16 · model_announcement | LLM Stats |
| Seed 2.1 Turbo | 0.183 | Not recorded | Not recorded | 2026-06-24 · model_announcement | LLM Stats |
| Seed 2.1 Pro | 0.165 | Not recorded | Not recorded | 2026-06-24 · model_announcement | LLM Stats |