Executive Summary
🏆 GLM-4.5
- • Quality Index: 66/70
- • Best Price: $0.91/M tokens (Deepinfra)
- • Context: Up to 131K tokens
- • Hybrid reasoning: Thinking + Non-thinking modes
- • Commercial license: MIT (fully open)
🚀 Qwen3-235B-A22B-Instruct-2507-FP8
- • Quality Index: 69/70 (Reasoning mode)
- • Best Price: $0.25/M tokens (Deepinfra)
- • Context: Up to 262K tokens
- • Massive scale: 235B total, 22B active params
- • FP8 optimized: Excellent efficiency
Bottom Line: Qwen3-235B edges ahead with higher quality scores and better pricing, while GLM-4.5 offers more consistent performance across providers and easier deployment.
Model Architecture & Specifications
GLM-4.5
Qwen3-235B-A22B-Instruct-2507-FP8
🔍 Architecture Deep Dive
Both models use Mixture of Experts (MoE) architecture, but with different approaches. GLM-4.5 focuses on intelligent agent capabilities with hybrid reasoning, while Qwen3-235B emphasizes massive scale with efficient FP8 quantization. The Qwen model's 128 experts vs GLM's more compact design represents different philosophies: breadth vs depth of specialization.
Performance Benchmarks
Quality Index Comparison
GLM-4.5
Qwen3-235B (Reasoning)
Detailed Performance Metrics
| Benchmark | GLM-4.5 | Qwen3-235B-2507 | Winner |
|---|---|---|---|
| MMLU-Pro | ~75-80 | 83.0 | 🏆 Qwen3 |
| AIME25 (Math) | ~45-55 | 70.3 | 🏆 Qwen3 |
| LiveCodeBench | ~40-50 | 51.8 | 🏆 Qwen3 |
| Arena-Hard v2 | ~65-70 | 79.2 | 🏆 Qwen3 |
| Agent Tasks (BFCL-v3) | ~65-70 | 70.9 | 🏆 Qwen3 |
📊 Performance Analysis
Qwen3-235B-A22B-Instruct-2507-FP8 demonstrates superior performance across most benchmarks, particularly excelling in mathematical reasoning (AIME25: 70.3), coding tasks (LiveCodeBench: 51.8), and conversational AI (Arena-Hard: 79.2). GLM-4.5 remains competitive but shows its strength in consistent, reliable performance rather than peak scores.
Pricing Analysis
GLM-4.5 Pricing
Qwen3-235B-2507 Pricing
💰 Cost Efficiency Analysis
Qwen3-235B offers exceptional value, delivering higher quality at significantly lower costs. The FP8 optimization enables aggressive pricing without performance compromise.
Speed & Latency Performance
GLM-4.5 Speed Metrics
Qwen3-235B Speed Metrics
⚡ Speed Analysis
GLM-4.5 offers more consistent speeds across providers (43-115 tok/sec), while Qwen3-235B shows extreme variance. Cerebras delivers unprecedented speed with Qwen3 (1600+ tok/sec), but most providers offer moderate speeds. For consistent, reliable performance, GLM-4.5 has the edge.
Use Cases & Recommendations
🎯 GLM-4.5 Best For
Intelligent Agents
Hybrid reasoning modes excel in complex decision-making and tool usage scenarios.
Production Applications
Consistent performance across providers reduces deployment complexity.
Enterprise Integration
MIT license and stable APIs make it ideal for commercial products.
Conversational AI
Reliable quality for customer service and assistant applications.
🎯 Qwen3-235B Best For
Research & Development
Superior benchmarks make it ideal for pushing performance boundaries.
Cost-Sensitive Applications
Exceptional value proposition with 72% cost savings over alternatives.
Long-Context Tasks
262K context length handles extensive documents and conversations.
Mathematical & Coding
Exceptional performance in STEM fields and programming tasks.
🏆 Final Verdict
Overall Winner
Qwen3-235B-A22B-Instruct-2507-FP8
Superior performance + cost efficiency
Reliability Winner
GLM-4.5
Consistent across providers
Value Winner
Qwen3-235B (Deepinfra)
$0.25/M tokens
Conclusion
Both GLM-4.5 and Qwen3-235B-A22B-Instruct-2507-FP8 represent the cutting edge of open-source language models, but they serve different needs in the AI ecosystem.
Qwen3-235B-A22B-Instruct-2507-FP8 emerges as the technical winner, delivering superior performance across most benchmarks while maintaining exceptional cost efficiency. Its massive 235B parameter count with 22B active parameters, combined with FP8 optimization, creates a compelling package for developers prioritizing performance and budget optimization.
GLM-4.5 shines in production reliability and ease of deployment. Its hybrid reasoning capabilities and consistent performance across providers make it an excellent choice for enterprise applications where predictability matters more than peak performance.
Quick Decision Matrix
Choose GLM-4.5 if you need:
- ✅ Consistent, reliable performance
- ✅ Agent-based applications
- ✅ Easy deployment across providers
- ✅ Proven production stability
Choose Qwen3-235B if you need:
- ✅ Maximum performance per dollar
- ✅ Superior benchmark scores
- ✅ Long context handling (262K)
- ✅ Cutting-edge capabilities
As the LLM landscape continues to evolve rapidly, both models represent significant achievements in open-source AI. Your choice should ultimately depend on your specific use case, budget constraints, and performance requirements. For most new projects, Qwen3-235B's combination of superior performance and cost efficiency makes it the recommended choice, while GLM-4.5 remains ideal for production environments requiring maximum reliability.
Related Resources
Qwen3-235B Resources
💡 Pro Tip: Use our LLM comparison tool to explore real-time pricing and performance data for these and hundreds of other models.