OpenThinkerAgent-32B/README.md

base_model, datasets, library_name, license, model-index, pipeline_tag, tags

base_model	datasets	library_name	license	model-index	pipeline_tag	tags
Qwen/Qwen3-32B	open-thoughts/OpenThoughts-Agent-SFT-100K	transformers	apache-2.0	name results OpenThinkerAgent-32B	text-generation	agents terminal code software-engineering

Project | Code | Collection

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.

• Homepage: https://www.openthoughts.ai/blog/agent
• Repository: https://github.com/open-thoughts/OpenThoughts-Agent

Performance

Evaluated in the terminus-2 harness (pass@1, mean over 3 stochastic re-runs):

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:

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

• 🌐 OpenThoughts-Agent project page
• 💻 OpenThoughts-Agent GitHub repository
• 📚 OpenThinker-Agent collection
• 🧠 Training dataset: OpenThoughts-Agent-SFT-100K

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}
}