GLM-4.6: China's Coding Champion Rivals Claude Sonnet 4 with 200K Context and 30% Token Efficiency

On September 30, 2025, Zhipu AI (Z.ai) released GLM-4.6, a groundbreaking coding-focused language model that directly challenges Claude Sonnet 4’s dominance in programming tasks. GLM-4.6 achieves 82.8% on LiveCodeBench v6 and 68.0% on SWE-bench Verified, reaching near-parity with Anthropic’s flagship model while offering 200K token context window (up from 128K) and consuming 30% fewer tokens than its predecessor GLM-4.5. In a historic milestone for China’s AI sovereignty push, GLM-4.6 became the first production model deployed on domestic Cambricon chips using FP8+Int4 mixed quantization, delivering performance at a fraction of the cost—1/7th the price of Claude at just 20 yuan ($2.80) per month for the coding subscription plan. This release positions Zhipu AI as the leading domestic alternative to Western AI giants and marks a significant step toward China’s independent AI infrastructure.

What is GLM-4.6?

Evolution of the GLM Series

GLM Timeline:

• GLM-130B (2022): First open-source Chinese large language model
• ChatGLM (2023): Consumer-facing conversational AI
• GLM-4 (June 2024): Multimodal general-purpose model
• GLM-4.5 (July 2025): Reasoning-focused upgrade with hybrid thinking modes
• GLM-4.6 (September 2025): Coding-specialized iteration optimized for developers

GLM-4.6’s Focus: Unlike general-purpose models that attempt to excel at all tasks, GLM-4.6 is explicitly optimized for coding and agentic workflows, making targeted improvements in:

• Code generation and completion
• Bug detection and fixing
• Front-end development
• Tool building and API integration
• Data analysis and algorithm development

Architecture and Training

Model Specifications:

• Parameters: Not disclosed (likely 32B-355B range based on GLM-4.5 architecture)
• Context Window: 200,000 tokens (~150,000 words)
• Training Data Cutoff: June 2025
• Quantization Support: FP8, Int4, FP8+Int4 mixed precision

Key Innovations:

1. Extended Context: 56% increase from 128K to 200K tokens enables processing entire codebases
2. Token Efficiency: 30% reduction in average token consumption compared to GLM-4.5
3. Hybrid Reasoning: Inherited from GLM-4.5, supports “thinking mode” for complex problems and “non-thinking mode” for rapid responses
4. Domestic Chip Optimization: Custom kernel optimizations for Cambricon and Moore Threads GPUs

Benchmark Performance: Challenging Claude Sonnet 4

Coding Benchmarks

LiveCodeBench v6 (Code Writing and Debugging)

Scores:

• GLM-4.6: 82.8% (84.5% with tool use)
• GLM-4.5: 63.3%
• Claude Sonnet 4: 84.5%
• Claude Sonnet 4.5: 88.0%
• GPT-4 Turbo: 75.2%

Analysis: GLM-4.6 achieves near-parity with Claude Sonnet 4, closing the gap to just 1.7 percentage points. This represents a 30% improvement over GLM-4.5 and establishes GLM-4.6 as a legitimate competitor to Western models in real-world coding tasks.

SWE-bench Verified (Real-World Code Refactoring)

Scores:

• GLM-4.6: 68.0%
• GLM-4.5: 64.2%
• Claude Sonnet 4: 67.8%
• Claude Sonnet 4.5: 77.2%
• DeepSeek-V3: 70.5%

Analysis: GLM-4.6 narrowly surpasses Claude Sonnet 4 (68.0% vs 67.8%) in debugging and refactoring real-world GitHub repositories. However, it trails Claude Sonnet 4.5 and China’s own DeepSeek-V3, indicating room for improvement in complex codebase understanding.

Reasoning and Math

AIME 2025 (Advanced Math Reasoning)

Scores:

• GLM-4.6: 93.9% (98.6% with tool use)
• GLM-4.5: 85.4%
• Claude Sonnet 4: 87.0%
• GPT-4o: 90.0%

Analysis: GLM-4.6’s 98.6% with tool use exceeds all Western competitors, demonstrating superior integration of search, calculator, and code execution tools for mathematical problem-solving.

GPQA (Graduate-Level Science)

Scores:

• GLM-4.6: Ranks #15 globally
• Claude Sonnet 4.5: Ranks #3
• GPT-4o: Ranks #5

Analysis: GLM-4.6 shows weaker performance in specialized scientific reasoning, suggesting its optimizations favor coding over general knowledge domains.

Agent and Tool Use

BrowseComp (Web Navigation)

Scores:

• GLM-4.6: 45.1
• GLM-4.5: 26.4
• Claude Sonnet 4: 48.0

Analysis: 71% improvement over GLM-4.5 in browsing and information retrieval tasks, though Claude Sonnet 4 maintains a slight edge.

Terminal-Bench (CLI Operations)

Scores:

• GLM-4.6: 40.5% (Ranks #3 globally)
• Claude Sonnet 4.5: 52.0%

Analysis: Strong performance in terminal-based workflows, critical for DevOps and system administration use cases.

Key Features and Improvements

1. 200K Token Context Window

Why This Matters:

• Entire Repositories: GLM-4.6 can ingest and reason over codebases with 50,000+ lines of code
• Long-Form Code Review: Analyze multiple files simultaneously for architectural consistency
• Extended Conversations: Maintain context across lengthy debugging sessions without summarization loss

Comparison:

• GPT-4 Turbo: 128K tokens
• Claude Opus 4: 200K tokens
• Gemini 2.5 Pro: 1,000K tokens (but slower inference)

Real-World Example: A developer working on a React + Node.js project can load:

• Frontend components (20 files, ~15K lines)
• Backend API routes (15 files, ~10K lines)
• Tests and documentation (10K lines)
• Still have 100K+ tokens available for conversation

2. 30% Token Efficiency Gain

What This Means: GLM-4.6 generates the same functionality with 30% fewer tokens than GLM-4.5, translating to:

• Faster responses: Less generation time per task
• Lower costs: API calls consume fewer tokens
• Better UX: Reduced latency in interactive coding

Mechanism: Zhipu AI achieved this through:

• More aggressive model distillation
• Redundancy elimination in generated code
• Better prompt comprehension (fewer clarifying tokens needed)

Cost Impact: At Zhipu’s pricing:

• GLM-4.5 API call: 1,000 tokens average
• GLM-4.6 API call: 700 tokens average
• 30% cost savings for same task

3. Domestic Chip Deployment (FP8+Int4 Quantization)

Historic Milestone: GLM-4.6 is the first production model deployed on Cambricon chips using FP8+Int4 mixed quantization, a breakthrough for China’s AI sovereignty.

Technical Details:

FP8+Int4 Mixed Quantization:

• FP8 (8-bit floating point): Used for attention layers requiring precision
• Int4 (4-bit integer): Used for feed-forward layers tolerant to compression
• Result: 70% memory reduction vs FP16 with <1% accuracy loss

Why Cambricon Matters:

• US Export Controls: NVIDIA’s H100/A100 GPUs banned from China
• Domestic Alternative: Cambricon’s MLU (Machine Learning Unit) chips fill the gap
• Ecosystem Growth: GLM-4.6’s success proves viability of Chinese AI stack

Performance Metrics:

• Inference Speed: 50 tokens/second on Cambricon MLU-590 (comparable to H100)
• Cost Efficiency: 60% cheaper than NVIDIA-based deployment
• Scalability: Deployed across Zhipu’s production infrastructure

Moore Threads Support: In addition to Cambricon, GLM-4.6 runs on Moore Threads MTT S4000 GPUs in native FP8 precision, expanding deployment options for Chinese enterprises.

4. Refined Writing Style and Front-End Capabilities

Code Generation Quality:

• Better variable naming: More idiomatic and readable
• Improved comments: Explains complex logic without verbosity
• Framework adherence: Respects React, Vue, Angular best practices

Front-End Specialization:

• Component generation: Creates functional React/Vue components with proper state management
• CSS mastery: Generates responsive layouts with Flexbox/Grid
• Accessibility: Includes ARIA labels and semantic HTML

Example Comparison:

GLM-4.5 Output (verbose):

1
function calculateTotal(items) {
2
  let total = 0;
3
  for (let i = 0; i < items.length; i++) {
4
    total = total + items[i].price;
5
  }
6
  return total;
7
}

GLM-4.6 Output (concise and modern):

1
const calculateTotal = (items) => items.reduce((sum, item) => sum + item.price, 0);

5. Enhanced Agent and Tool Integration

Built-in Tool Support:

• Web search: Fetch real-time information from search engines
• Code execution: Run Python/JavaScript in sandboxed environment
• API calling: Integrate with REST APIs (GitHub, Stripe, AWS)
• File operations: Read/write files during task execution

Agentic Workflows: GLM-4.6 can autonomously:

1. Receive task description (e.g., “Build a user authentication system”)
2. Search documentation (e.g., query Express.js auth middleware)
3. Generate code across multiple files
4. Test implementation by executing code
5. Debug errors and iterate until tests pass

Use Case: Automated Bug Fixing

1
User: "API endpoint /users/:id returns 500 error when ID doesn't exist"
2

3
GLM-4.6:
4
1. Searches codebase for /users/:id route handler
5
2. Identifies missing null check after database query
6
3. Generates fix: Add 404 response if user not found
7
4. Writes test to prevent regression
8
5. Commits changes with descriptive message

Pricing and Availability

GLM Coding Plan (Consumer)

Pricing Tiers:

• Free: 10 queries per day, 128K context
• Basic: 20 yuan/month ($2.80) — 200 queries/day, 200K context
• Pro: 50 yuan/month ($7.00) — 1,000 queries/day, priority queue

Comparison to Claude:

• Claude Sonnet 4: $20/month (ChatGPT Pro)
• GLM-4.6: $2.80/month (Basic)
• Savings: 86% cheaper for comparable performance

Value Proposition: Zhipu AI markets GLM-4.6 as delivering “9/10 of Claude’s intelligence at 1/7 the price”, making it attractive for:

• Students and independent developers
• Startups with tight budgets
• Chinese users facing payment barriers with Western services

API Pricing (Developers)

Z.ai API Rates:

• Input: $0.15 per million tokens
• Output: $0.60 per million tokens

Comparison:

• Claude Sonnet 4 API: $3.00 input /$15.00 output per million tokens
• GLM-4.6 Savings: 95% cheaper for input, 96% cheaper for output

Example Cost Calculation: A developer building a coding assistant that processes 10M input tokens and generates 2M output tokens per month:

Claude Sonnet 4:

• Input: 10M × $3.00 =$30.00
• Output: 2M × $15.00 =$30.00
• Total: $60.00/month

GLM-4.6:

• Input: 10M × $0.15 =$1.50
• Output: 2M × $0.60 =$1.20
• Total: $2.70/month

Savings: $57.30/month (96% reduction)

Access Methods

1. Z.ai Chat Interface (chat.z.ai)

• Web-based chat for interactive coding
• Supports file uploads and code execution
• Audio changelogs (like Claude’s feature)

2. Z.ai API (bigmodel.cn)

• RESTful API for programmatic access
• Compatible with OpenAI SDK (drop-in replacement)
• Rate limits: 100 requests/minute (Pro), 10 req/min (Free)

3. Open-Source Weights (HuggingFace)

• Model weights available under Apache 2.0 license
• Local deployment via vLLM, SGLang, or Ollama
• Requires 80GB+ GPU VRAM (or quantized versions for consumer GPUs)

4. Self-Hosted on Domestic Chips

• Optimized Docker images for Cambricon MLU chips
• Moore Threads GPU support via vLLM
• Enterprise licensing available for on-premise deployment

Use Cases and Real-World Applications

1. Full-Stack Development

Scenario: Build a todo app with React frontend and Node.js backend

GLM-4.6 Workflow:

1. Generate React components with state management
2. Create Express.js API routes with validation
3. Write MongoDB schema and queries
4. Implement JWT authentication
5. Generate tests for all endpoints
6. Create Docker Compose setup for local dev

Time Savings:

• Traditional dev: 6-8 hours
• With GLM-4.6: 1-2 hours (mostly review and iteration)

2. Legacy Code Refactoring

Scenario: Migrate a 10,000-line jQuery codebase to modern React

GLM-4.6 Capabilities:

• Analyze jQuery code patterns
• Generate equivalent React components
• Preserve business logic while modernizing structure
• Update tests to reflect new architecture

Challenge: Requires multiple passes due to 200K context limit, but still 10x faster than manual rewrite.

3. Algorithm Optimization

Scenario: Improve performance of slow database queries in Python Django app

GLM-4.6 Approach:

1. Profile code to identify N+1 query issues
2. Suggest select_related() and prefetch_related() optimizations
3. Rewrite queries using Django ORM efficiently
4. Benchmark before/after performance

Result: Query time reduced from 2.5 seconds to 150ms in production.

4. DevOps Automation

Scenario: Write Terraform scripts to provision AWS infrastructure

GLM-4.6 Output:

• Generate .tf files for VPC, EC2, RDS, S3
• Configure security groups and IAM roles
• Create CI/CD pipeline with GitHub Actions
• Write documentation for deployment process

Advantage: GLM-4.6’s strong Terminal-Bench score (40.5%) makes it excellent for CLI-based tasks.

5. Educational Tool for Learning to Code

Scenario: Student learning data structures and algorithms

GLM-4.6 as Tutor:

• Explains concepts in simple language
• Generates example code with comments
• Creates practice problems with solutions
• Debugs student code and explains errors

Accessibility: At 20 yuan/month, GLM-4.6 is affordable for students in developing countries where Claude’s $20/month is prohibitive.

Limitations and Challenges

1. Still Trails Claude Sonnet 4.5

Gap in Advanced Tasks:

• SWE-bench Verified: GLM-4.6 (68.0%) vs Claude 4.5 (77.2%)
• LiveCodeBench: GLM-4.6 (82.8%) vs Claude 4.5 (88.0%)

Why This Matters: For cutting-edge software engineering (e.g., refactoring complex distributed systems), Claude Sonnet 4.5 remains superior.

2. Weaker in Non-Coding Domains

GPQA Ranking (#15): GLM-4.6’s coding optimizations come at the cost of general knowledge reasoning.

Not Ideal For:

• Academic research assistance
• Medical/legal document analysis
• Creative writing

Best For:

• Software development
• Data analysis
• Technical documentation

3. Limited International Availability

China-First Strategy:

• Website and documentation primarily in Chinese
• API payment requires Chinese bank account or Alipay
• Enterprise support prioritizes domestic customers

Workaround for International Users:

• Use open-source weights from HuggingFace
• Self-host with vLLM or Ollama
• Wait for potential international expansion

4. Domestic Chip Performance Gap

Cambricon vs NVIDIA: While GLM-4.6 runs on Cambricon MLU-590, it’s still slower than H100:

• H100: 70 tokens/second
• Cambricon MLU-590: 50 tokens/second
• 30% performance gap

Improving Over Time: China’s chip industry is rapidly advancing—expect this gap to narrow by 2026.

Implications for China’s AI Ecosystem

1. Reducing Dependence on Western AI

Strategic Importance: GLM-4.6’s success demonstrates China can build competitive AI models without access to cutting-edge Western chips or APIs.

Sovereignty Benefits:

• Data stays in China: No reliance on US cloud providers
• Censorship control: Model behavior aligned with Chinese regulations
• Economic independence: Revenue stays within domestic ecosystem

2. Accelerating Domestic Chip Adoption

Proof of Concept: GLM-4.6’s deployment on Cambricon proves Chinese chips can run production AI workloads.

Investment Signal: Expect increased funding for:

• Cambricon, Moore Threads, and other Chinese GPU makers
• Software optimization tools for domestic hardware
• Training infrastructure built on Chinese chips

3. Competitive Pressure on OpenAI and Anthropic

Pricing Disruption: GLM-4.6’s 96% lower API costs force Western competitors to reconsider pricing.

Feature Parity: Chinese models (GLM, DeepSeek, Qwen) are closing the performance gap faster than expected—6-12 month lag vs previous 2-3 year lag.

Market Dynamics: If China’s 1.4 billion people adopt domestic AI models, Western companies lose access to world’s largest market.

What’s Next for GLM?

Announced Features (Coming Q4 2025)

1. GLM-4.7 (Rumored)

• Further context expansion to 256K tokens
• Multimodal coding (understand UI screenshots and generate corresponding code)
• Better support for Rust, Go, and systems programming

2. GLM-4.6-Turbo

• 2x faster inference speed
• Optimized for short-form code generation
• Lower latency for IDE autocomplete

3. Fine-Tuning API

• Allow developers to fine-tune GLM-4.6 on proprietary codebases
• Learn company-specific coding conventions
• Improve accuracy for internal tools

Zhipu AI’s IPO Plans

Listing Timeline:

• Q4 2025: Complete pre-IPO funding (targeting $200M)
• Q1 2026: Submit prospectus to Shanghai Stock Exchange
• Q2 2026: Begin trading (target valuation: $5B)

Significance: Zhipu AI would become first “Big Model Six Tigers” to go public, ahead of Baichuan, MiniMax, Moonshot, and Stepfun.

Revenue Model:

• API subscriptions (40% of revenue)
• Enterprise licenses (35%)
• Consumer subscriptions (20%)
• Open-source support (5%)

Conclusion

Zhipu AI’s GLM-4.6 represents a watershed moment for China’s AI industry. By achieving near-parity with Claude Sonnet 4 in coding benchmarks while delivering a 200K context window, 30% token efficiency gains, and 1/7th the price, GLM-4.6 proves that Chinese AI companies can compete with—and in some cases surpass—Western giants.

The historic deployment on Cambricon domestic chips using FP8+Int4 mixed quantization addresses China’s most critical vulnerability: dependence on NVIDIA GPUs. As US export controls tighten, GLM-4.6’s success on Chinese hardware provides a roadmap for the entire industry.

Key Takeaways:

For Developers: GLM-4.6 is a legitimate alternative to Claude Sonnet 4 for coding tasks, offering comparable performance at a fraction of the cost. At $2.80/month for consumers and$0.15-0.60 per million tokens for API access, it’s the most cost-effective high-performance coding assistant available.

For Chinese AI Ecosystem: GLM-4.6 validates the domestic AI stack—from chips (Cambricon) to models (Zhipu) to applications (coding assistants). This end-to-end sovereignty reduces strategic risk and accelerates innovation.

For Global AI Landscape: The coding AI race is no longer US vs Europe—it’s US vs China. With GLM-4.6, DeepSeek-V3, and Qwen3 all achieving >80% on LiveCodeBench, Chinese models are forcing Western competitors to innovate faster and price more aggressively.

The question isn’t whether Chinese AI can compete—it’s how quickly Western companies will respond to the pricing and performance pressure GLM-4.6 represents.

Welcome to the era of competitive global AI. Welcome to GLM-4.6.

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