GPT-5.2 vs DeepSeek R1
Detailed pricing comparison and cost analysis.
Updated April 2026
Cost Simulator
| Feature | GPT-5.2 | DeepSeek R1 |
|---|---|---|
| Provider | OpenAI | DeepSeek |
| Input Price (1M) | $1.75 | $0.28 |
| Output Price (1M) | $14.00 | $0.42 |
| Context Window | 128,000 | 64,000 |
Verdict
GPT-5.2 costs $1.75 per 1M input tokens and $14.00 per 1M output tokens. DeepSeek R1 costs $0.28 per 1M input tokens and $0.42 per 1M output tokens. DeepSeek R1 is 84% cheaper on input tokens than GPT-5.2. For output tokens, DeepSeek R1 is the more affordable option at $0.42/1M vs $14.00.
On context window, GPT-5.2 supports 128,000 tokens — meaning it can fit more conversation history, documents, or code in a single request. This matters for RAG pipelines, long document analysis, and agentic workflows where context builds up over many turns.
When to choose GPT-5.2
- ✓ You need a larger context window (128,000 tokens)
- ✓ You are already integrated with OpenAI
When to choose DeepSeek R1
- ✓ You need the lowest input token cost ($ 0.28/1M)
- ✓ Your workload is output-heavy — DeepSeek R1 generates text cheaper
- ✓ 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 GPT-5.2 cheaper than DeepSeek R1? ▼
DeepSeek R1 is cheaper on input tokens at $0.28/1M vs $1.75/1M for GPT-5.2 — a 84% saving.
What is the context window of GPT-5.2 vs DeepSeek R1? ▼
GPT-5.2 has a 128,000-token context window. DeepSeek R1 has a 64,000-token context window. GPT-5.2 supports the larger context, suitable for longer documents and agentic workflows.
Which model is better: GPT-5.2 or DeepSeek R1? ▼
The best choice depends on your use case. For cost efficiency on input tokens, DeepSeek R1 is the cheaper option. For maximum context length, GPT-5.2 supports 128,000 tokens. Use the comparison table above to find the right fit for your workload.