GLM-5.2 vs Claude Opus 4.6
Detailed pricing comparison and cost analysis.
Updated September 2026
Cost Simulator
| Feature | GLM-5.2 | Claude Opus 4.6 |
|---|---|---|
| Provider | Zhipu AI | Anthropic |
| Input Price (1M) | $1.40 | $5.00 |
| Output Price (1M) | $4.40 | $25.00 |
| Context Window | 1,000,000 | 1,000,000 |
Verdict
GLM-5.2 costs $1.40 per 1M input tokens and $4.40 per 1M output tokens. Claude Opus 4.6 costs $5.00 per 1M input tokens and $25.00 per 1M output tokens. GLM-5.2 is 72% cheaper on input tokens than Claude Opus 4.6. For output tokens, GLM-5.2 is the more affordable option at $4.40/1M vs $25.00.
On context window, GLM-5.2 supports 1,000,000 tokens — meaning it can fit more conversation history, documents, or code in a single request. This matters for RAG pipelines, long document analysis, and agentic workflows where context builds up over many turns.
When to choose GLM-5.2
- ✓ You need the lowest input token cost ($ 1.40/1M)
- ✓ Your workload is output-heavy — GLM-5.2 generates text cheaper
- ✓ You are already integrated with Zhipu AI
When to choose Claude Opus 4.6
- ✓ You are already integrated with Anthropic
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 GLM-5.2 cheaper than Claude Opus 4.6? ▼
GLM-5.2 is cheaper on input tokens at $1.40/1M vs $5.00/1M for Claude Opus 4.6 — a 72% saving.
What is the context window of GLM-5.2 vs Claude Opus 4.6? ▼
GLM-5.2 has a 1,000,000-token context window. Claude Opus 4.6 has a 1,000,000-token context window. GLM-5.2 supports the larger context, suitable for longer documents and agentic workflows.
Which model is better: GLM-5.2 or Claude Opus 4.6? ▼
The best choice depends on your use case. For cost efficiency on input tokens, GLM-5.2 is the cheaper option. For maximum context length, GLM-5.2 supports 1,000,000 tokens. Use the comparison table above to find the right fit for your workload.