GLM-4.7 - Your New Coding Partner with 358B Parameters

🤔 Curiosity: What Makes a Great Coding Agent?

Building AI-powered games at NC SOFT and COM2US, I’ve always wondered: Can an AI model truly understand the context of a complex codebase and act autonomously to fix bugs, implement features, or refactor code?

The answer is increasingly “yes” - and GLM-4.7 from Z.ai is the latest contender pushing the boundaries of what’s possible with agentic coding.

The question: How does a 358B parameter MoE model achieve state-of-the-art performance on coding benchmarks while maintaining practical inference speeds?

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📚 Retrieve: Understanding GLM-4.7

What is GLM-4.7?

GLM-4.7 is Z.ai’s latest large language model, designed specifically for agentic coding tasks. It’s a 358B parameter Mixture-of-Experts (MoE) model that supports:

• Text Generation with multilingual support (English, Chinese)
• Tool calling via OpenAI-compatible APIs
• Interleaved thinking for complex reasoning

GLM-4.7 benchmark comparisons across reasoning, coding, and agent tasks

Key Features

Feature	Description	Impact
Core Coding	Multilingual agentic coding and terminal tasks	73.8% on SWE-bench (+5.8% vs GLM-4.6)
Vibe Coding	Improved UI quality for web/slide generation	Cleaner, modern designs
Tool Using	Enhanced function calling and web browsing	87.4% on τ²-Bench
Complex Reasoning	Mathematical and logical reasoning	42.8% on HLE (+12.4%)

Benchmark Results

GLM-4.7 competes head-to-head with the latest models from OpenAI, Anthropic, Google, and others:

Benchmark	GLM-4.7	GLM-4.6	GPT-5-High	GPT-5.1-High	Claude Sonnet 4.5	Gemini 3.0 Pro
MMLU-Pro	84.3	83.2	87.5	87.0	88.2	90.1
GPQA-Diamond	85.7	81.0	85.7	88.1	83.4	91.9
HLE (w/ Tools)	42.8	30.4	35.2	42.7	32.0	45.8
AIME 2025	95.7	93.9	94.6	94.0	87.0	95.0
HMMT Feb. 2025	97.1	89.2	88.3	96.3	79.2	97.5
LiveCodeBench-v6	84.9	82.8	87.0	87.0	64.0	90.7
SWE-bench Verified	73.8	68.0	74.9	76.3	77.2	76.2
SWE-bench Multilingual	66.7	53.8	55.3	-	68.0	-
Terminal Bench 2.0	41.0	24.5	35.2	47.6	42.8	54.2
τ²-Bench	87.4	75.2	82.4	82.7	87.2	90.7

Key Insight: GLM-4.7 excels in mathematical reasoning (AIME, HMMT) and agentic tasks (SWE-bench Multilingual), while Gemini 3.0 Pro leads in general knowledge benchmarks.

Interleaved Thinking Architecture

What sets GLM-4.7 apart is its thinking architecture:

Interleaved, Preserved, and Turn-level Thinking modes

flowchart TB
    subgraph Input["User Request"]
        A[Complex Coding Task]
    end

    subgraph Thinking["🧠 Thinking Modes"]
        B["Interleaved Thinking<br/>Think before every action"]
        C["Preserved Thinking<br/>Retain context across turns"]
        D["Turn-level Thinking<br/>Enable/disable per turn"]
    end

    subgraph Output["Model Response"]
        E[Action/Tool Call]
        F[Code Generation]
        G[Explanation]
    end

    A --> B
    B --> C
    C --> D
    D --> E
    D --> F
    D --> G

    style B fill:#ff6b6b,stroke:#c92a2a,color:#fff
    style C fill:#4ecdc4,stroke:#0a9396,color:#fff
    style D fill:#ffe66d,stroke:#f4a261,color:#000

Three Thinking Modes:

1. Interleaved Thinking: The model thinks before every response and tool call, improving instruction following
2. Preserved Thinking: Automatically retains thinking blocks across multi-turn conversations - critical for long-horizon tasks
3. Turn-level Thinking: Per-turn control - disable for lightweight requests, enable for complex tasks

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💡 Innovation: Using GLM-4.7 in Production

Getting Started with vLLM

# Install vLLM nightly
pip install -U vllm --pre --index-url https://pypi.org/simple \
    --extra-index-url https://wheels.vllm.ai/nightly

# Serve the model
vllm serve zai-org/GLM-4.7-FP8 \
    --tensor-parallel-size 4 \
    --speculative-config.method mtp \
    --speculative-config.num_speculative_tokens 1 \
    --tool-call-parser glm47 \
    --reasoning-parser glm45 \
    --enable-auto-tool-choice \
    --served-model-name glm-4.7-fp8

Using with Transformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL_PATH = "zai-org/GLM-4.7"

# Initialize tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_PATH,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

# Create messages with thinking enabled
messages = [{"role": "user", "content": "Implement a binary search tree in Python"}]

# Tokenize with chat template
inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

# Generate response
generated_ids = model.generate(
    **inputs,
    max_new_tokens=8192,
    do_sample=True,
    temperature=1.0,
    top_p=0.95,
)

output = tokenizer.decode(generated_ids[0][inputs.input_ids.shape[1]:])
print(output)

Preserved Thinking for Agent Tasks

For agentic tasks, enable Preserved Thinking mode:

# SGLang configuration for preserved thinking
chat_template_kwargs = {
    "enable_thinking": True,
    "clear_thinking": False  # Keep thinking across turns
}

Integration with Coding Agents

GLM-4.7 works excellently with popular coding agent frameworks:

graph LR
    A[GLM-4.7] --> B[Claude Code]
    A --> C[Kilo Code]
    A --> D[Cline]
    A --> E[Roo Code]

    B --> F[Agentic<br/>Development]
    C --> F
    D --> F
    E --> F

    style A fill:#0077b6,stroke:#03045e,color:#fff
    style F fill:#4ecdc4,stroke:#0a9396,color:#fff

Evaluation Parameters

Default Settings (Most Tasks):

• temperature: 1.0
• top-p: 0.95
• max_new_tokens: 131072

Coding Tasks (SWE-bench, Terminal Bench):

• temperature: 0.7
• top-p: 1.0
• max_new_tokens: 16384

τ²-Bench (Agent Tasks):

• temperature: 0
• max_new_tokens: 16384

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🎯 Key Takeaways

Insight	Implication	Production Consideration
MoE Architecture	358B params with efficient routing	Requires multi-GPU setup (TP=4 minimum)
Preserved Thinking	Better context retention for agents	Enable for long-horizon tasks
Multilingual Coding	66.7% on SWE-bench Multilingual	Great for polyglot codebases
MIT License	Fully open for commercial use	No licensing concerns

What I’d Try First

1. Deploy with vLLM: Use the FP8 quantized version for faster inference
2. Enable Preserved Thinking: Essential for multi-turn agentic tasks
3. Integrate with existing agent frameworks: Claude Code, Cline work well
4. Benchmark on your codebase: Real-world performance may vary

When to Use GLM-4.7

Good fit:

• Complex debugging across multiple files
• Code refactoring with context awareness
• Multilingual codebase maintenance
• Long-horizon agentic development tasks

Consider alternatives:

• Simple code completion (smaller models are faster)
• Real-time IDE suggestions (latency-sensitive)
• Tasks without tool use (simpler models suffice)

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🤔 New Questions This Raises

1. Fine-tuning potential: Can we adapt GLM-4.7 for game-specific coding patterns?
2. Agent orchestration: How does it compare to multi-agent setups like Claude Code + Sonnet?
3. Context window utilization: With 131K tokens, how efficiently does it use long contexts?
4. Production costs: What’s the inference cost vs. API-based alternatives?

Next experiment: Benchmark GLM-4.7 against Claude Opus 4.5 on game development tasks - shader code generation, gameplay balancing scripts, and procedural content generation.

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References

Official Resources:

• GLM-4.7 on Hugging Face
• Z.ai Technical Blog
• GLM-4.7 API Platform
• GLM-4.7 Chat Interface
• Z.ai Discord Community

Research Papers:

• GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models (arXiv:2508.06471)
• Thinking Mode Documentation
• Claude Opus 4.5 System Card (τ²-Bench fixes)

Code & Implementation:

• Official GitHub Repository
• vLLM Nightly Docker
• SGLang Docker Image

Model Variants:

• GLM-4.7-FP8 (Quantized)
• GLM-4.7 Collection

Inference Providers:

• Novita AI
• Cerebras
• Z.ai API

Related Benchmarks:

• SWE-bench
• GPQA Dataset
• MMLU-Pro Dataset
• HLE Dataset

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GLM-4.7 represents a significant step forward in open-source coding agents. With its MIT license and strong benchmark performance, it’s a compelling option for production AI-assisted development workflows.