babbage-002 vs GPT-5 nano
Detailed pricing comparison and cost analysis.
Updated September 2026
Cost Simulator
| Feature | babbage-002 | GPT-5 nano |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Input Price (1M) | $0.40 | $0.05 |
| Output Price (1M) | $0.40 | $0.40 |
| Context Window | 16,384 | 400,000 |
Verdict
babbage-002 costs $0.40 per 1M input tokens and $0.40 per 1M output tokens. GPT-5 nano costs $0.05 per 1M input tokens and $0.40 per 1M output tokens. GPT-5 nano is 88% cheaper on input tokens than babbage-002. For output tokens, babbage-002 is the more affordable option at $0.40/1M vs $0.40.
On context window, GPT-5 nano supports 400,000 tokens — meaning it can fit more conversation history, documents, or code in a single request. This matters for RAG pipelines, long document analysis, and agentic workflows where context builds up over many turns.
When to choose babbage-002
- ✓ You are already integrated with OpenAI
When to choose GPT-5 nano
- ✓ You need the lowest input token cost ($ 0.05/1M)
- ✓ You need a larger context window (400,000 tokens)
- ✓ You are already integrated with OpenAI
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 GPT-5 nano? ▼
GPT-5 nano is cheaper on input tokens at $0.05/1M vs $0.40/1M for babbage-002 — a 88% saving.
What is the context window of babbage-002 vs GPT-5 nano? ▼
babbage-002 has a 16,384-token context window. GPT-5 nano has a 400,000-token context window. GPT-5 nano supports the larger context, suitable for longer documents and agentic workflows.
Which model is better: babbage-002 or GPT-5 nano? ▼
The best choice depends on your use case. For cost efficiency on input tokens, GPT-5 nano is the cheaper option. For maximum context length, GPT-5 nano supports 400,000 tokens. Use the comparison table above to find the right fit for your workload.