I've been running all three frontier models in production across my AI Business Platform, KCAI Desktop, and Memory Forge. Here's what I've actually observed — not benchmark scores, but real task performance.
Coding Performance
For complex multi-file refactoring, the ranking is clear. GPT-5.5 handles context across 50+ files with its 256K window. GLM-5.1's FP8 variant is 2x faster but slightly less accurate on edge cases. Claude 4 excels at security analysis.
| Model | Context | Coding Speed | Accuracy | Best For |
|---|---|---|---|---|
| GPT-5.5 | 256K | Fast | Excellent | Large refactors, multi-file |
| GLM-5.1 FP8 | 128K | Very Fast | Very Good | Production speed, cost |
| Claude 4 Opus | 200K | Medium | Excellent | Security review, safety |
| Claude 4 Sonnet | 200K | Fast | Good | Daily coding tasks |
Agentic Tool Use
All three support function calling, but they differ in reliability. GLM-5.1's native function calling is the most consistent for multi-step agent workflows. GPT-5.5's adaptive reasoning sometimes overthinks simple tool calls. Claude 4 is the safest but slowest for agentic chains.
✦ TipFor agent orchestration with 15+ agents, GLM-5.1 FP8 gives the best speed-to-reliability ratio. Use GPT-5.5 for complex planning, Claude 4 for safety-critical decisions.
Cost Analysis
Running these models in production adds up. Here's my monthly spend breakdown across the AI Business Platform:
- ▸GPT-5.5: ~$200/month — 50K requests, mostly agent dispatch
- ▸GLM-5.1 FP8: ~$80/month — 100K requests, high-volume tasks
- ▸Claude 4 Sonnet: ~$120/month — 30K requests, code review
- ▸Local (llama.cpp): ~$15/month electricity — unlimited inference
The Verdict
There's no single winner. The optimal setup uses all three for different tasks. GPT-5.5 for complex reasoning, GLM-5.1 FP8 for high-volume production, Claude 4 for safety-critical code, and local models for privacy-sensitive work.