Zhipu AI GLM 4.6 vs Claude Sonnet 4.5: Open-Source Coding Model

Quick Comparison

GLM-4.6 (September 30, 2025) and Claude Sonnet 4.5 (September 29, 2025) represent cutting-edge coding AI models released within days of each other. While Claude Sonnet 4.5 leads with 77.2% on SWE-bench Verified, GLM-4.6 achieves competitive performance at a fraction of the cost through its open-source MIT license.

GLM-4.6 Overview

Zhipu AI released GLM-4.6 on September 30, 2025, as an open-source coding model with MIT license. The 357B-parameter Mixture of Experts (MoE) architecture achieves competitive performance with significantly lower inference costs through sparse activation. For a comprehensive comparison of Chinese AI models including GLM 4.5, Kimi K2, and Qwen 3 Coder, see our detailed analysis.

Architecture & Capabilities

GLM-4.6 features a 200K input context window and 128K maximum output tokens, doubling the output capacity of most competitors. The model uses BF16/F32 tensors and demonstrates particular strength in multi-turn agentic tasks and long-context reasoning.

Open Source Advantage

The MIT license provides unrestricted commercial use, modification rights, and zero API lock-in. Organizations can self-host for data privacy, fine-tune for domain-specific tasks, or use through cost-effective APIs like Z.ai at $0.36 per million tokens.

Claude Sonnet 4.5 Overview

Anthropic released Claude Sonnet 4.5 on September 29, 2025, achieving industry-leading 77.2% on SWE-bench Verified. The model demonstrates exceptional reasoning, extended thinking capabilities, and proven production reliability across enterprises.

Performance Achievements

Claude Code 2.0 Integration

Claude Sonnet 4.5 powers Claude Code 2.0 with automatic checkpoints, subagents for parallel workflows, and hooks for pre-commit validation. The platform provides proven enterprise reliability with SOC 2 Type II compliance and extensive API ecosystem.

Performance Benchmarks

Both models excel at coding tasks, but with different strengths. Claude Sonnet 4.5 leads on standardized benchmarks, while GLM-4.6 achieves competitive real-world performance at significantly lower cost.

CC-Bench Extended Results

On CC-Bench extended multi-turn tasks (run by human evaluators in isolated Docker environments), GLM-4.6 achieved a 48.6% win rate against Claude Sonnet 4. This near-parity performance demonstrates GLM-4.6's competitiveness in real-world coding scenarios.

Pricing Comparison

Cost differences between GLM-4.6 and Claude Sonnet 4.5 are substantial. GLM-4.6's open-source nature enables 90%+ cost savings through APIs like Z.ai, or zero marginal cost through self-hosting.

Deployment Options

GLM-4.6's open-source nature provides flexible deployment options, while Claude Sonnet 4.5 offers a robust API-only approach with proven reliability.

GLM-4.6 Deployment

import Anthropic from '@anthropic-ai/sdk';

const client = new Anthropic({
  baseURL: 'https://open.zhipu.ai/api/v1',
  apiKey: process.env.ZHIPU_API_KEY
});

const response = await client.messages.create({
  model: 'glm-4.6',
  max_tokens: 4096,
  messages: [{
    role: 'user',
    content: 'Write a React component'
  }]
});

# Install vLLM
pip install vllm

# Serve GLM-4.6
vllm serve THUDM/glm-4.6 \
  --dtype bfloat16 \
  --tensor-parallel-size 4 \
  --max-model-len 200000

Claude Sonnet 4.5 Deployment

import Anthropic from '@anthropic-ai/sdk';

const client = new Anthropic({
  apiKey: process.env.ANTHROPIC_API_KEY
});

const response = await client.messages.create({
  model: 'claude-sonnet-4.5-20250929',
  max_tokens: 4096,
  messages: [{
    role: 'user',
    content: 'Write a React component'
  }]
});

Use Case Recommendations

Conclusion

GLM-4.6 and Claude Sonnet 4.5 represent two excellent approaches to AI coding assistance: open-source affordability versus proprietary performance. The choice depends on your specific priorities around cost, control, and capabilities.