Claude Sonnet 4.5 vs GLM-5
Detailed pricing comparison and cost analysis.
Updated September 2026
Cost Simulator
| Feature | Claude Sonnet 4.5 | GLM-5 |
|---|---|---|
| Provider | Anthropic | Zhipu AI |
| Input Price (1M) | $3.00 | $1.00 |
| Output Price (1M) | $15.00 | $3.20 |
| Context Window | 200,000 | 1,000,000 |
Verdict
Claude Sonnet 4.5 costs $3.00 per 1M input tokens and $15.00 per 1M output tokens. GLM-5 costs $1.00 per 1M input tokens and $3.20 per 1M output tokens. GLM-5 is 67% cheaper on input tokens than Claude Sonnet 4.5. For output tokens, GLM-5 is the more affordable option at $3.20/1M vs $15.00.
On context window, GLM-5 supports 1,000,000 tokens — meaning it can fit more conversation history, documents, or code in a single request. This matters for RAG pipelines, long document analysis, and agentic workflows where context builds up over many turns.
When to choose Claude Sonnet 4.5
- ✓ You are already integrated with Anthropic
When to choose GLM-5
- ✓ You need the lowest input token cost ($ 1.00/1M)
- ✓ Your workload is output-heavy — GLM-5 generates text cheaper
- ✓ You need a larger context window (1,000,000 tokens)
- ✓ You are already integrated with Zhipu AI
Use the calculator above to simulate your specific workload and find the exact break-even point. For most applications, the cheapest model is the one that minimises your total monthly bill given your input-to-output token ratio.
Frequently Asked Questions
Is Claude Sonnet 4.5 cheaper than GLM-5? ▼
GLM-5 is cheaper on input tokens at $1.00/1M vs $3.00/1M for Claude Sonnet 4.5 — a 67% saving.
What is the context window of Claude Sonnet 4.5 vs GLM-5? ▼
Claude Sonnet 4.5 has a 200,000-token context window. GLM-5 has a 1,000,000-token context window. GLM-5 supports the larger context, suitable for longer documents and agentic workflows.
Which model is better: Claude Sonnet 4.5 or GLM-5? ▼
The best choice depends on your use case. For cost efficiency on input tokens, GLM-5 is the cheaper option. For maximum context length, GLM-5 supports 1,000,000 tokens. Use the comparison table above to find the right fit for your workload.