GLM-5 vs DeepSeek V4-Pro
Detailed pricing comparison and cost analysis.
Updated September 2026
Cost Simulator
| Feature | GLM-5 | DeepSeek V4-Pro |
|---|---|---|
| Provider | Zhipu AI | DeepSeek |
| Input Price (1M) | $1.00 | $1.32 |
| Output Price (1M) | $3.20 | $3.96 |
| Context Window | 1,000,000 | 1,000,000 |
Verdict
GLM-5 costs $1.00 per 1M input tokens and $3.20 per 1M output tokens. DeepSeek V4-Pro costs $1.32 per 1M input tokens and $3.96 per 1M output tokens. GLM-5 is 24% cheaper on input tokens than DeepSeek V4-Pro. For output tokens, GLM-5 is the more affordable option at $3.20/1M vs $3.96.
On context window, GLM-5 supports 1,000,000 tokens — meaning it can fit more conversation history, documents, or code in a single request. This matters for RAG pipelines, long document analysis, and agentic workflows where context builds up over many turns.
When to choose GLM-5
- ✓ You need the lowest input token cost ($ 1.00/1M)
- ✓ Your workload is output-heavy — GLM-5 generates text cheaper
- ✓ You are already integrated with Zhipu AI
When to choose DeepSeek V4-Pro
- ✓ You are already integrated with DeepSeek
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 cheaper than DeepSeek V4-Pro? ▼
GLM-5 is cheaper on input tokens at $1.00/1M vs $1.32/1M for DeepSeek V4-Pro — a 24% saving.
What is the context window of GLM-5 vs DeepSeek V4-Pro? ▼
GLM-5 has a 1,000,000-token context window. DeepSeek V4-Pro 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: GLM-5 or DeepSeek V4-Pro? ▼
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.