davinci-002 vs GLM-5
Detailed pricing comparison and cost analysis.
Updated September 2026
Cost Simulator
| Feature | davinci-002 | GLM-5 |
|---|---|---|
| Provider | OpenAI | Zhipu AI |
| Input Price (1M) | $2.00 | $1.00 |
| Output Price (1M) | $2.00 | $3.20 |
| Context Window | 16,384 | 1,000,000 |
Verdict
davinci-002 costs $2.00 per 1M input tokens and $2.00 per 1M output tokens. GLM-5 costs $1.00 per 1M input tokens and $3.20 per 1M output tokens. GLM-5 is 50% cheaper on input tokens than davinci-002. For output tokens, davinci-002 is the more affordable option at $2.00/1M vs $3.20.
On context window, GLM-5 supports 1,000,000 tokens — meaning it can fit more conversation history, documents, or code in a single request. This matters for RAG pipelines, long document analysis, and agentic workflows where context builds up over many turns.
When to choose davinci-002
- ✓ Your workload is output-heavy — davinci-002 generates text cheaper
- ✓ You are already integrated with OpenAI
When to choose GLM-5
- ✓ You need the lowest input token cost ($ 1.00/1M)
- ✓ 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 davinci-002 cheaper than GLM-5? ▼
GLM-5 is cheaper on input tokens at $1.00/1M vs $2.00/1M for davinci-002 — a 50% saving.
What is the context window of davinci-002 vs GLM-5? ▼
davinci-002 has a 16,384-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: davinci-002 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.