babbage-002 vs GLM-5.3
Detailed pricing comparison and cost analysis.
Updated September 2026
Cost Simulator
| Feature | babbage-002 | GLM-5.3 |
|---|---|---|
| Provider | OpenAI | Zhipu AI |
| Input Price (1M) | $0.40 | $1.40 |
| Output Price (1M) | $0.40 | $4.40 |
| Context Window | 16,384 | 1,000,000 |
Verdict
babbage-002 costs $0.40 per 1M input tokens and $0.40 per 1M output tokens. GLM-5.3 costs $1.40 per 1M input tokens and $4.40 per 1M output tokens. babbage-002 is 71% cheaper on input tokens than GLM-5.3. For output tokens, babbage-002 is the more affordable option at $0.40/1M vs $4.40.
On context window, GLM-5.3 supports 1,000,000 tokens — meaning it can fit more conversation history, documents, or code in a single request. This matters for RAG pipelines, long document analysis, and agentic workflows where context builds up over many turns.
When to choose babbage-002
- ✓ You need the lowest input token cost ($ 0.40/1M)
- ✓ Your workload is output-heavy — babbage-002 generates text cheaper
- ✓ You are already integrated with OpenAI
When to choose GLM-5.3
- ✓ 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 babbage-002 cheaper than GLM-5.3? ▼
babbage-002 is cheaper on input tokens at $0.40/1M vs $1.40/1M for GLM-5.3 — a 71% saving.
What is the context window of babbage-002 vs GLM-5.3? ▼
babbage-002 has a 16,384-token context window. GLM-5.3 has a 1,000,000-token context window. GLM-5.3 supports the larger context, suitable for longer documents and agentic workflows.
Which model is better: babbage-002 or GLM-5.3? ▼
The best choice depends on your use case. For cost efficiency on input tokens, babbage-002 is the cheaper option. For maximum context length, GLM-5.3 supports 1,000,000 tokens. Use the comparison table above to find the right fit for your workload.