JanusCoder-14B
💻Github Repo • 🤗Model Collections • 📜Technical Report
Introduction
We introduce JanusCoder and JanusCoderV, a suite of open-source foundational models designed to establish a unified visual-programmatic interface for code intelligence. This model suite is built upon open-source language models (such as Qwen3-8B and 14B) and multimodal models (such as Qwen2.5-VL and InternVL3.5-8B). The JanusCoder series is trained on JANUSCODE-800K—the largest multimodal code corpus to date, generated by an innovative synthesis toolkit, covering everything from standard charts to complex interactive Web UIs and code-driven animations. This enables the models to uniformly handle diverse visual-programmatic tasks, such as generating code from textual instructions, visual inputs, or a combination of both, rather than building specialized models for isolated tasks. JanusCoder excels at flexible content generation (like data visualizations and interactive front-ends) as well as precise, program-driven editing of visual effects and complex animation construction.
Model Downloads
| Model Name | Description | Download |
|---|---|---|
| JanusCoder-8B | 8B text model based on Qwen3-8B. | 🤗 Model |
| 👉 JanusCoder-14B | 14B text model based on Qwen3-14B. | 🤗 Model |
| JanusCoderV-7B | 7B multimodal model based on Qwen2.5-VL-7B. | 🤗 Model |
| JanusCoderV-8B | 8B multimodal model based on InternVL3.5-8B. | 🤗 Model |
Performance
We evaluate the JanusCoder model on various benchmarks that span code interlligence tasks on multiple PLs:
| Model | JanusCoder-14B | Qwen3-14B | Qwen2.5-Coder-32B-Instruct | LLaMA3-8B-Instruct | GPT-4o |
|---|---|---|---|---|---|
| PandasPlotBench (Task) | 86 | 78 | 82 | 69 | 85 |
| ArtifactsBench | 41.1 | 36.5 | 35.5 | 36.5 | 37.9 |
| DTVBench (Manim) | 8.41 | 6.63 | 9.61 | 4.92 | 10.60 |
| DTVBench (Wolfram) | 5.97 | 5.08 | 4.98 | 3.15 | 5.97 |
Quick Start
Transformers
The following provides demo code illustrating how to generate text using JanusCoder-14B.
Please use transformers >= 4.55.0 to ensure the model works normally.
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "internlm/JanusCoder-14B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name, device_map="auto", dtype="auto",
).eval()
messages = [
{"role": "user", "content": "Create a line plot that illustrates function y=x."}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt"
).to(model.device)
with torch.inference_mode():
generate_ids = model.generate(**inputs, max_new_tokens=200)
decoded_output = tokenizer.batch_decode(generate_ids, skip_special_tokens=True)
print(decoded_output[0])
Citation
🫶 If you are interested in our work or find the repository / checkpoints / benchmark / data helpful, please consider using the following citation format when referencing our papers:
@article{sun2025januscoder,
title={JanusCoder: Towards a Foundational Visual-Programmatic Interface for Code Intelligence},
author={Sun, Qiushi and Gong, Jingyang and Liu, Yang and Chen, Qiaosheng and Li, Lei and Chen, Kai and Guo, Qipeng and Kao, Ben and Yuan, Fei},
journal={arXiv preprint arXiv:2510.23538},
year={2025}
}
@article{sun2024survey,
title={A survey of neural code intelligence: Paradigms, advances and beyond},
author={Sun, Qiushi and Chen, Zhirui and Xu, Fangzhi and Cheng, Kanzhi and Ma, Chang and Yin, Zhangyue and Wang, Jianing and Han, Chengcheng and Zhu, Renyu and Yuan, Shuai and others},
journal={arXiv preprint arXiv:2403.14734},
year={2024}
}
@article{chen2025interactscience,
title={InteractScience: Programmatic and Visually-Grounded Evaluation of Interactive Scientific Demonstration Code Generation},
author={Chen, Qiaosheng and Liu, Yang and Li, Lei and Chen, Kai and Guo, Qipeng and Cheng, Gong and Yuan, Fei},
journal={arXiv preprint arXiv:2510.09724},
year={2025}
}
@article{sun2025codeevo,
title={CodeEvo: Interaction-Driven Synthesis of Code-centric Data through Hybrid and Iterative Feedback},
author={Sun, Qiushi and Gong, Jinyang and Li, Lei and Guo, Qipeng and Yuan, Fei},
journal={arXiv preprint arXiv:2507.22080},
year={2025}
}