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open-thoughts/OpenThinkerAgent-32B

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<p align="center"> <img src="https://huggingface.co/datasets/open-thoughts/OpenThoughts1-Agent-SFT/resolve/main/ota-logo.png" width="50%"> </p>

<p align="center"> <a href="https://www.openthoughts.ai/blog/agent" style="margin-right: 24px;">Project</a> | <a href="https://github.com/open-thoughts/OpenThoughts-Agent" style="margin-right: 24px; margin-left: 24px;">Code</a> | <a href="https://huggingface.co/collections/open-thoughts/openthinker-agent" style="margin-left: 24px;">Collection</a> </p>

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):

ModelHarnessSWE-Bench-Verified-100OpenThoughts-TBLiteTerminal-Bench 2.0
Qwen/Qwen3-32BTerminus-226.713.77.5
OpenThinkerAgent-32BTerminus-255.741.326.2

Across the full seven-benchmark suite (best harness per benchmark), OpenThinkerAgent-32B is the strongest open-data model at the 32B scale:

BenchmarkAccuracy
SWE-Bench-Verified54.0
Terminal-Bench 2.026.2
Aider-Polyglot32.4
BFCL-Parity85.9
MedAgentBench47.8
GAIA-12723.6
FinanceAgent-Terminal44.0
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
  • —lrschedulertype: cosine, warmup_ratio 0.1
  • —globalbatchsize: 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}
}