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Qwen/Qwen2.5-Coder-32B-Instruct
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🧑💻 NanoRollout Code
🤗 Mocha-Coder-32B Model
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Mocha-Coder-32B is a strong open-data coding agent built on top of Qwen2.5-Coder-32B-Instruct. It is trained entirely through distillation on a 300K+ trajectory mixture sampled with our lightweight agent-rollout infrastructure, NanoRollout, with no reinforcement learning. The full training signal comes from frontier open-source teacher models (Qwen3-Coder-480B-A35B, Kimi-K2.5, Qwen3-Coder-Next, DeepSeek-V3.2) generating trajectories across multiple agent harnesses (OpenHands, mini-swe-agent, Terminus-2 JSON) on SWE-Rebench, SWE-Smith, and SETA.
The result is a simple but strong baseline coding agent: at the ≤32B scale, Mocha-Coder-32B is the state-of-the-art among open-data models and is competitive with much larger open-source models on agentic SWE benchmarks.
ZeonLap/Mocha-trajectories).| Model | Max Iteration | SWE-Bench Verified (Pass@1) |
|---|---|---|
| Qwen3-Coder-480B-A35B-Instruct | 100 | 67.0 |
| Mocha-Coder-32B | 100 | 62.6 |
| SWE-Master-32B-RL | 150 | 61.4 |
| Kimi-Dev-72B | Agentless, TTS@40 | 60.4 |
| CoderForge-Preview-32B | 100 | 59.4 |
| GLM-4.7-Flash | 100 | 59.2 |
| daVinci-Dev-72B | 100 | 58.5 |
| daVinci-Dev-32B | 100 | 56.1 |
| SERA-32B | 100 | 54.2 |
| Qwen3-Coder-30B-A3B-Instruct | 100 | 51.6 |
| Qwen2.5-Coder-32B-Instruct (Base) | 100 | 6.2 |
| Model | Max Iteration | SWE-Bench Pro (Pass@1) |
|---|---|---|
| Qwen3-Coder-480B-A35B-Instruct | 250 | 38.7 |
| Mocha-Coder-32B | 250 | 35.3 |
| Gemini-3-flash | 250 | 34.6 |
| Kimi-K2-Instruct | 250 | 27.7 |
| DeepSeek-V3.2 | 250 | 15.6 |
| Qwen2.5-Coder-32B-Instruct (Base) | 250 | 0.0 |
| Model | Terminal-Bench 2.0 |
|---|---|
| Qwen3-Coder-480B-A35B-Instruct | 23.9 |
| Mocha-Coder-32B | 23.6 |
| Qwen3-Coder-30B-A3B-Instruct | 13.5 |
| Qwen2.5-Coder-32B-Instruct (Base) | 3.4 |
Mocha-Coder-32B is trained on a 300K+ trajectory distillation mixture, drawn from previously released distillation sets (120K) and trajectories newly generated with NanoRollout (~180K).
| Dataset | Teacher Model | Harness | # Trajectories (K) | Source |
|---|---|---|---|---|
| SWE-Rebench | Qwen3-Coder-480B-A35B | OpenHands | 32.2 | Nebius |
| SWE-Smith | Qwen3-Coder-480B-A35B | OpenHands | 89.5 | CoderForge |
| SWE-Rebench | Kimi-K2.5 | mini-swe-agent | 83.6 | NanoRollout |
| SWE-Rebench | Qwen3-Coder-Next | mini-swe-agent | 11.5 | NanoRollout |
| SWE-Smith | Qwen3-Coder-480B-A35B | mini-swe-agent | 12.8 | NanoRollout |
| SWE-Smith | Qwen3-Coder-Next | mini-swe-agent | 9.1 | NanoRollout |
| SETA | Kimi-K2.5 / DeepSeek-V3.2 | Terminus-2 JSON | 14.0 | NanoRollout |
The full mixture is released at ZeonLap/Mocha-trajectories.
Mocha-Coder-32B is trained as an agent and is most useful when paired with a coding-agent harness. We have validated it with:
Point each harness's model endpoint at the vLLM server above. For SWE-Bench Verified we report numbers at a 100-iteration budget; for SWE-Bench Pro at 250 iterations.
Mocha-Coder-32B (model weights, training trajectories, and code) is released under the MIT License (see LICENSE) for research, educational, and commercial use.
If you use Mocha-Coder-32B or NanoRollout in your research, please cite NanoRollout:
bibtex
@misc{nanorollout, title = {NanoRollout: A Lightweight Infra for Digital Agent Rollout at Scale}, author = {Wang, Junli and Cheng, Zhoujun and Zhang, Yuxuan and Hao, Shibo and Tang, Yao and Hu, Zhiting and Ammanabrolu, Prithviraj and Zhang, Hao}, year = {2026}, howpublished = {\url{https://github.com/cocoa-org/NanoRollout}},}@misc{nanorollout, title = {NanoRollout: A Lightweight Infra for Digital Agent Rollout at Scale}, author = {Wang, Junli and Cheng, Zhoujun and Zhang, Yuxuan and Hao, Shibo and Tang, Yao and Hu, Zhiting and Ammanabrolu, Prithviraj and Zhang, Hao}, year = {2026}, howpublished = {\url{https://github.com/cocoa-org/NanoRollout}},}