Modern developers face growing challenges in code generation, debugging, and large-scale codebase maintenance. Traditional tools cannot efficiently handle long-context reasoning or integrate with complex workflows. AI coding models such as GLM-4.6 and Qwen3-Coder-480B-A35B-Instruct are built to address these gaps. This article compares their architectures, benchmarks, and inference efficiency to show how each model solves real-world coding problems—from rapid prototyping to deep repository analysis—and guides developers in choosing the right model and setup for their specific coding tasks.
What Coding Problems People Use AI Models to Solve？
AI coding models mainly help developers generate and operate code. They either create new files and modules from natural-language instructions or read existing repositories to modify, refactor, or call external data and APIs. The first type accelerates prototyping and agent-style automation; the second improves understanding and reuse of large, complex codebases.
Type
Instruction-Based Generation / Agent
Repository-Based Reasoning / Data Calling
Input
Natural-language request such as “build this feature”
Project code, repo files, APIs, data sources
Focus
Creates new content (modules, files, interfaces)
Understands existing code and expands it
Automation
High automation (agent-style workflows)
Complex analysis with context integration
Typical Uses
Rapid prototyping, UI generation, setup scripts
Refactoring, large-repo updates, data-driven features
Very comprehensive; lists every file, template, and test with detailed purpose and functions.
Focused on main components only; omits minor templates and extra files.
Structure
Hierarchical and exhaustive, ending with architectural patterns and design principles.
Concise and modular, grouping files by functionality (auth, blog, tests).
Depth of Understanding
Demonstrates deep repository comprehension and long-context reasoning.
Shows efficient summarization and information condensation.
Readability
Dense and long; better suited for expert readers or technical documentation.
Easier to read; suitable for beginners or quick-reference summaries.
Use-Case Fit
Ideal for evaluating code-understanding and reasoning depth in large-context models.
Ideal for testing summarization quality and clarity under constrained outputs.
Strength Highlighted
Long-context tracking, structural reasoning, and comprehensive coverage.
Precision, brevity, and clarity in summarizing key logic.
Best Demonstrates
Repository analysis and detailed explanation capabilities.
Summarization and concise technical writing abilities.
GLM 4.6 VS Qwen3-Coder-480B-A35B-Instruct: Architecture
GLM-4.6 is a 355B-parameter MoE model with 32B active parameters and a 200 K-token context window.
Total parameters: ~ 355 billion, active parameters ~ 32 billion.
Model architecture: Mixture-of-Experts (MoE) inherited from GLM-4.x series.
Context window: native 200,000 tokens, max output ~128K tokens.
Key enhancements over its predecessor (GLM-4.5) include longer context length, improved coding performance, better tool integration.
Qwen3-Coder-480B-A35B is a 480B-parameter MoE model with 35B active parameters and supports up to 1 M tokens context.
Total parameters: ~ 480 billion; active parameters ~ 35 billion.
Context window: native support for ~256 K tokens, scalable via extrapolation to ~1 million tokens.
Architecture: Mixture-of-Experts with many experts (e.g., 160 experts with 8 active) according to model card.
Purpose-built for agentic coding tasks (multi-turn code generation, tool invocation).
GLM-4.6 is optimized for coding performance and tool integration, making it well-suited for fast coding, debugging, and multi-tool collaboration. In contrast, Qwen3-Coder-480B-A35B-Instruct is better suited for large-scale codebase understanding, long-document reasoning, and cross-file refactoring tasks that demand ultra-long context and complex logical processing.
GLM 4.6 VS Qwen3-Coder-480B-A35B-Instruct: Benchmark
Benchmark
GLM-4.6
Qwen3-Coder-480B-A35B-Instruct
SWE-bench Verified
68.0 %
69.6 % (OpenHands 500 turns)
Terminal-Bench
40.5 %
37.5 %
LiveCodeBench v6
84.5 % (with tools)
–
HLE
30.4 % (with tools)
–
Aider-Polyglot
–
61.8 %
SWE-bench Multilingual
–
54.7 %
WebArena / Mind2Web
~45–50 % (range)
49.9 / 55.8 %
GLM-4.6 performs slightly lower on SWE-bench but leads on LiveCodeBench and tool-integrated benchmarks, showing maturity in assisted coding workflows.
Qwen3-Coder-480B achieves higher consistency across multilingual and multi-turn agentic tasks, implying better robustness in complex, long-session coding.
Both are close in pure code correctness, but GLM-4.6 wins in real-time responsiveness; Qwen3-Coder wins in sustained reasoning.
GLM 4.6 VS Qwen3-Coder-480B-A35B-Instruct: Effeciency
GLM-4.6 outputs more and runs faster, but costs more overall; Qwen3-Coder-480B is slower yet cheaper per run, with lower reasoning cost.
1. Output Volume
GLM-4.6: 86 million output tokens
Qwen3-Coder-480B: 9.7 million output tokens
GLM-4.6 produces about nine times more output tokens.
2. Generation Speed
GLM-4.6: 82 tokens per second
Qwen3-Coder-480B: 41 tokens per second
GLM-4.6 generates responses roughly twice as fast.
GLM-4.6 “talks more” during reasoning; Qwen3 is more concise and cost-efficient.
5. Hardware Requirements
Model
Active Params
Recommended Setup
Efficiency Profile
GLM-4.6
32B
8× A100 80 GB or 4× H100 48 GB
Low VRAM, fast inference
Qwen3-Coder-480B
35B
8–16× H100 80 GB
High VRAM, optimized for long-context runs
GLM-4.6: Highest output, fastest inference, but also the most expensive and reasoning-heavy.
Qwen3-Coder-480B: Lower speed and output, yet more cost-efficient with reduced reasoning overhead. GLM-4.6 fits interactive, high-speed coding tasks; Qwen3-Coder suits long-context or large-scale batch inference.
How to Access GLM 4.6 or Qwen3-Coder-480B-A35B-Instruct for your Code Job?
The official website currently uses a monthly subscription model. If you just want to use it practically rather than paying for unused time, you can try Novita AI, which offers both lower prices and highly stable support services.
Novita AI offers Qwen3-Coder APIs with a 262K context window at $0.29 per input and $1.2 per output. It also provides GLM-4.6V APIs with a 208K context window at $0.60 per input and $2.20 per output, supporting structured outputs and function calling.
By using Novita AI’s service, you can bypass the regional restrictions of Claude Code. Novita also provides SLA guarantees with 99% service stability, making it especially suitable for high-frequency scenarios such as code generation and automated testing. Novita AI also provides access guides for Trae and Qwen Code, which can be found in the following articles.
The First: Get API Key(Using GLM-4.6 as Example)
Step 1: Log in to your account and click on the Model Library button.
Open Command Prompt and set the following environment variables:
set ANTHROPIC_BASE_URL=https://api.novita.ai/anthropic
set ANTHROPIC_AUTH_TOKEN=<Novita API Key>
set ANTHROPIC_MODEL=moonshotai/glm-4.6
set ANTHROPIC_SMALL_FAST_MODEL=moonshotai/glm-4.6
Replace <Novita API Key> with your actual API key obtained from the Novita AI platform. These variables remain active for the current session and must be reset if you close the Command Prompt.
For Mac and Linux
Open Terminal and export the following environment variables:
export ANTHROPIC_BASE_URL="https://api.novita.ai/anthropic"
export ANTHROPIC_AUTH_TOKEN="<Novita API Key>"
export ANTHROPIC_MODEL="moonshotai/glm-4.6"
export ANTHROPIC_SMALL_FAST_MODEL="moonshotai/glm-4.6"
Starting Claude Code
With installation and configuration complete, you can now start Claude Code in your project directory. Navigate to your desired project location using the cd command:
Launch the Trae app. Click the Toggle AI Side Bar in the top-right corner to open the AI Side Bar. Then, go to AI Management and select Models.
Step 2: Add a Custom Model and Choose Novita as Provider
Click the Add Model button to create a custom model entry. In the add-model dialog, select Provider = Novita from the dropdown menu.
Step 3: Select or Enter the Model
From the Model dropdown, pick your desired model (DeepSeek-R1-0528, Kimi K2 DeepSeek-V3-0324, or MiniMax-M1-80k,GLM 4.6). If the exact model isn’t listed, simply type the model ID that you noted from the Novita library. Ensure you choose the correct variant of the model you want to use.
For solving coding tasks, GLM-4.6 excels in fast, interactive development, automated debugging, and tool-based code generation. Its higher speed and responsiveness make it ideal for developers who iterate quickly. Qwen3-Coder-480B-A35B-Instruct focuses on large-repository reasoning, long-context understanding, and structured refactoring, enabling it to handle complex, cross-file code tasks. Together, they demonstrate how AI can accelerate software development—GLM-4.6 prioritizing speed and precision, and Qwen3-Coder emphasizing scale and reasoning depth.
Frequently Asked Questions
How does GLM-4.6 help solve real coding tasks?
GLM-4.6 can generate, debug, and refactor code interactively using natural language. It is optimized for short-to-medium code contexts, helping developers rapidly test, fix, and ship features within IDEs like Cursor or Claude Code.
When is Qwen3-Coder-480B-A35B-Instruct a better choice?
Use Qwen3-Coder-480B-A35B-Instruct for large-scale or repository-level coding problems. Its extended 1M-token context allows deep reasoning across multiple files, ideal for analyzing architecture, tracing dependencies, or refactoring complex systems.
Which model performs coding tasks faster?
GLM-4.6 generates about 82 tokens per second, roughly twice the speed of Qwen3-Coder-480B-A35B-Instruct, making it better for iterative and time-sensitive development workflows.
Novita AI is an AI cloud platform that offers developers an easy way to deploy AI models using our simple API, while also providing an affordable and reliable GPU cloud for building and scaling.
