GPT-3.5 Turbo vs GPT-5
Detailed pricing comparison and cost analysis.
Updated August 2026
Cost Simulator
| Feature | GPT-3.5 Turbo | GPT-5 |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Input Price (1M) | $0.50 | $1.25 |
| Output Price (1M) | $1.50 | $10.00 |
| Context Window | 16,385 | 400,000 |
Verdict
GPT-3.5 Turbo costs $0.50 per 1M input tokens and $1.50 per 1M output tokens. GPT-5 costs $1.25 per 1M input tokens and $10.00 per 1M output tokens. GPT-3.5 Turbo is 60% cheaper on input tokens than GPT-5. For output tokens, GPT-3.5 Turbo is the more affordable option at $1.50/1M vs $10.00.
On context window, GPT-5 supports 400,000 tokens — meaning it can fit more conversation history, documents, or code in a single request. This matters for RAG pipelines, long document analysis, and agentic workflows where context builds up over many turns.
When to choose GPT-3.5 Turbo
- ✓ You need the lowest input token cost ($ 0.50/1M)
- ✓ Your workload is output-heavy — GPT-3.5 Turbo generates text cheaper
- ✓ You are already integrated with OpenAI
When to choose GPT-5
- ✓ 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 GPT-3.5 Turbo cheaper than GPT-5? ▼
GPT-3.5 Turbo is cheaper on input tokens at $0.50/1M vs $1.25/1M for GPT-5 — a 60% saving.
What is the context window of GPT-3.5 Turbo vs GPT-5? ▼
GPT-3.5 Turbo has a 16,385-token context window. GPT-5 has a 400,000-token context window. GPT-5 supports the larger context, suitable for longer documents and agentic workflows.
Which model is better: GPT-3.5 Turbo or GPT-5? ▼
The best choice depends on your use case. For cost efficiency on input tokens, GPT-3.5 Turbo is the cheaper option. For maximum context length, GPT-5 supports 400,000 tokens. Use the comparison table above to find the right fit for your workload.