The Dawn of True Open-Source Agents
For years, the gap between open-source models and proprietary giants like Claude and GPT seemed insurmountable, especially regarding complex reasoning and agentic workflows. That changed in late December 2025. Z.ai released GLM-4.7, a massive 355B-parameter Mixture-of-Experts (MoE) model that doesn't just generate code—it thinks before it types.
As a Developer Advocate, I have tested dozens of “coding” models. GLM-4.7 is different. It is specifically engineered for agentic coding, meaning it excels at maintaining context across multi-turn architecture discussions and debugging sessions. In this post, we will look at why this model is topping the SWE-bench leaderboards and how you can implement a reasoning loop using Python.
Under the Hood: 355B Parameters of Reasoning
GLM-4.7 utilizes a sophisticated MoE architecture. While the total parameter count is 355 billion, the active parameter count during inference is significantly lower, allowing for efficient deployment on high-end consumer hardware or enterprise clusters. The standout feature, however, is its native Thinking Capability.
The 'Thinking' Process
Unlike previous iterations that rushed to provide a solution, GLM-4.7 is trained to output a reasoning trace encapsulated in specific tokens before generating the actual code. This allows the model to plan its approach, consider edge cases, and self-correct logic errors before committing to a syntax. This behavior mimics the “System 2” thinking process seen in proprietary models like GPT-5, making it incredibly effective for complex refactoring tasks.
Practical Example: Building a Refactoring Agent
Let's get practical. Below is a Python example using the transformers library. In a real-world scenario, you would likely serve this model using vLLM or TGI due to its size, but this script demonstrates how to structure a prompt to leverage the model's specific agentic tokens for a refactoring task.
We will create a simple script that asks GLM-4.7 to analyze a legacy function and plan a refactor before writing code.
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Configuration for the 355B MoE Model
# Note: This requires significant VRAM (e.g., A100s/H100s) or quantization.
model_id = "Z-ai/GLM-4.7-355B-MoE-Instruct"
def generate_refactor_plan(legacy_code):
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True
)
# The system prompt ensures we trigger the 'Thinking' mode
system_prompt = (
"You are an expert software engineer. "
"Before writing any code, you must output your reasoning process "
"enclosed in tags. Analyze the complexity and potential bugs."
)
user_message = f"Refactor this Python function for better readability and performance:\n\n{legacy_code}"
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_message}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=2048,
temperature=0.2, # Lower temperature for code precision
do_sample=True
)
response = tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True)
return response
if __name__ == "__main__":
# A purposefully inefficient piece of code
bad_code = """
def do_stuff(x):
l = []
for i in range(len(x)):
if x[i] % 2 == 0:
l.append(x[i] * 2)
return l
"""
print("Sending code to GLM-4.7 Agent...\n")
result = generate_refactor_plan(bad_code)
print("--- Agent Response ---")
print(result) Parsing the Output
When you run the code above, GLM-4.7 will produce an output that separates the <thought> process from the implementation. It might look like this:
1. The user wants to refactor a function `do_stuff`.
2. Current implementation uses a manual loop with `range(len(x))` which is non-Pythonic.
3. It filters even numbers and doubles them.
4. This is a classic map/filter pattern or list comprehension.
5. Plan: Rename function to `double_evens`, use list comprehension for speed, add type hinting.
def double_evens(numbers: list[int]) -> list[int]:
"""
Takes a list of integers, filters for even numbers,
and returns a new list with those values doubled.
"""
return [n * 2 for n in numbers if n % 2 == 0]Conclusion
GLM-4.7 represents a pivotal moment for open-source AI. By combining state-of-the-art reasoning with the accessibility of open weights, Z.ai has given developers the tools to build autonomous coding agents that rival proprietary solutions. If you are building dev tools or autonomous agents, this is the model to watch in 2026.
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