OpenThinkerAgent-32B
OpenThoughts-Agent is an open-source effort to curate the best datasets for training agents. Our release includes datasets, models and our research codebase.
OpenThinkerAgent-32B is post-trained from Qwen/Qwen3-32B with full-parameter SFT on the 100,000-example OpenThoughts-Agent-SFT-100K dataset (Top-4 task sources, GLM-4.7-AWQ teacher in the terminus-2 harness, ≥5-turn trace filter). It is the flagship OpenThinkerAgent-32B, the strongest open-data 32B model on the average of seven agentic benchmarks.
Evaluated in the terminus-2 harness (pass@1, mean over 3 stochastic re-runs):
Table with columns: Model, Harness, SWE-Bench-Verified-100, OpenThoughts-TBLite, Terminal-Bench 2.0| Model | Harness | SWE-Bench-Verified-100 | OpenThoughts-TBLite | Terminal-Bench 2.0 |
|---|
| Qwen/Qwen3-32B | Terminus-2 | 26.7 | 13.7 | 7.5 |
| OpenThinkerAgent-32B | Terminus-2 | 55.7 | 41.3 | 26.2 |
Across the full seven-benchmark suite (best harness per benchmark), OpenThinkerAgent-32B is the strongest open-data model at the 32B scale:
Table with columns: Benchmark, Accuracy| Benchmark | Accuracy |
|---|
| SWE-Bench-Verified | 54.0 |
| Terminal-Bench 2.0 | 26.2 |
| Aider-Polyglot | 32.4 |
| BFCL-Parity | 85.9 |
| MedAgentBench | 47.8 |
| GAIA-127 | 23.6 |
| FinanceAgent-Terminal | 44.0 |
| Average (7) | 44.8 |
Data
The model is trained on OpenThoughts-Agent-SFT-100K: (task, agent-trajectory) pairs from the Top-4 task sources (SWE-Smith, StackExchange-SuperUser, StackExchange-Tezos with synthetic augmentation, IssueTasks). Trajectories are generated by GLM-4.7-AWQ in the terminus-2 harness and filtered to traces with at least 5 model turns.
Training hyperparameters
- learning_rate: 4e-05
- lr_scheduler_type: cosine, warmup_ratio 0.1
- global_batch_size: 96
- num_epochs: 5
- cutoff_len: 32768
- precision: bf16, DeepSpeed ZeRO-3
Links
Citation
@misc{openthoughts-agent,
author = {Team, OpenThoughts-Agent},
title = {{OpenThoughts-Agent: Data Recipes for Agentic Models}},
howpublished = {https://www.openthoughts.ai/blog/agent},
year = {2026}
}