Results
Every model is evaluated through the same harness. RTL benchmarks run under the
simulator each official suite specifies; kernel benchmarks compare candidate
outputs against the reference implementation under matched inputs. Bold
marks the best result in each row and italic the second best.
Table with columns: Benchmark, Metric, iCoder-27B, Qwen3.6-27B, InCoder-32B, InCoder-32B-T, DeepSeek-V4-Pro, GLM-5.2, Kimi-K2.6, GPT-5.5, Claude-Opus-4.8, Hy3, Gemini-3.5-Flash| Benchmark | Metric | iCoder-27B | Qwen3.6-27B | InCoder-32B | InCoder-32B-T | DeepSeek-V4-Pro | GLM-5.2 | Kimi-K2.6 | GPT-5.5 | Claude-Opus-4.8 | Hy3 | Gemini-3.5-Flash |
|---|
| VerilogEval | Spec-to-RTL avg@4 | 86.3 | 70.1 | 62.5 | 65.9 | 69.9 | 66.0 | 72.4 | 90.1 | 82.7 | 83.8 | 89.1 |
| VerilogEval | Code-complete avg@4 | 86.0 | 70.8 | 58.2 | 54.2 | 79.8 | 74.8 | 78.5 | |
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "i-Coder/iCoder-27B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id, dtype="auto", device_map="auto"
)
messages = [{"role": "user", "content": "Write a 4-bit synchronous up counter with active-low reset in Verilog."}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=2048)
print(tokenizer.decode(out[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True))
Citation
If you find iCoder useful in your work, please cite the technical report:
@techreport{yang2026icoder,
title = {iCoder-27B: Recursive AI-Led Development of Frontier Industrial Coding Model},
author = {Cheng Yang and Jiayang Lyu and Shangyuan Liu and Guibin Zhang and
Jiong Lin and Xinlei Yu and Junchi Yan and Shuicheng Yan and
Weinan E and Linfeng Zhang and Linfeng Zhang and Qibing Ren},
year = {2026},
month = aug,
type = {Technical Report},
url = {https://huggingface.co/i-Coder/iCoder-27B}
}
License
Apache-2.0, inherited from the base model, Qwen3.6-27B.