Last verified 2026-03-12 (left) · 2026-03-19 (right)

DeepSeek Chat vs GPT-5.4 nano — Pricing & Capability Comparison

DeepSeek Chat charges $0.27 per million input tokens and $1.10 per million output tokens. GPT-5.4 nano comes in at $0.20 / $1.25. Context windows span 64K vs 400K tokens respectively.

TL;DR — Quick Comparison

✓DeepSeek Chat is cheaper overall: $1.37 per 1M tokens (in+out) vs $1.45 for GPT-5.4 nano — saves $0.08 per 1M tokens
✓Input pricing: DeepSeek Chat $0.27/1M vs GPT-5.4 nano $0.20/1M
✓Output pricing: DeepSeek Chat $1.10/1M vs GPT-5.4 nano $1.25/1M
✓Context window: GPT-5.4 nano offers more (400K vs 64K)
✓Use our calculator below to estimate costs for your specific usage pattern

Input price (per 1M)

DeepSeek Chat

$0.27

GPT-5.4 nano

$0.20

GPT-5.4 nano leads here

Output price (per 1M)

DeepSeek Chat

$1.10

GPT-5.4 nano

$1.25

DeepSeek Chat leads here

Context window

DeepSeek Chat

64,000 tokens

GPT-5.4 nano

400,000 tokens

GPT-5.4 nano leads here

Cached input

DeepSeek Chat

$0.070

GPT-5.4 nano

$0.020

GPT-5.4 nano leads here

Which one should you choose?

Skip the spreadsheet if you just need the practical takeaway. Use these rules when deciding between DeepSeek Chat and GPT-5.4 nano.

Choose GPT-5.4 nano if input tokens dominate your bill

GPT-5.4 nano has the lower input rate, which usually matters most for chat, RAG, classification, and long-prompt workflows where prompt volume stays much larger than generated output.

Choose DeepSeek Chat if you generate long answers

DeepSeek Chat is cheaper on output tokens, so it tends to win for report generation, coding assistance, reasoning traces, and any workflow where completions are long.

Choose GPT-5.4 nano if context size is the blocker

GPT-5.4 nano offers the larger published context window, which is more important than small pricing differences when you need to fit large files, long chats, or multi-document prompts into one request.

Cost comparison for 10K-token workloads

Side-by-side pricing for identical workloads (10,000 total tokens per request) across different distributions.

Scenario	DeepSeek Chat	GPT-5.4 nano	DeepSeek Chat cached	GPT-5.4 nano cached
Balanced conversation 50% input · 50% output	$0.0069	$0.0073	$0.0059	$0.0064
Input-heavy workflow 80% input · 20% output	$0.0044	$0.0041	$0.0028	$0.0027
Generation heavy 30% input · 70% output	$0.0085	$0.0094	$0.0079	$0.0088
Cached system prompt 90% cached input · 10% fresh output	$0.0035	$0.0030	$0.0017	$0.0014

Frequently asked questions

Which is cheaper: DeepSeek Chat or GPT-5.4 nano?

GPT-5.4 nano is cheaper for input tokens at $0.20 per 1M tokens compared to $0.27. For output, DeepSeek Chat costs $1.10 per 1M tokens versus $1.25 for GPT-5.4 nano.

What is the cost per 1M tokens for DeepSeek Chat?

DeepSeek Chat pricing: $0.27 per 1M input tokens and $1.10 per 1M output tokens. Context window: 64,000 tokens.

What is the cost per 1M tokens for GPT-5.4 nano?

GPT-5.4 nano pricing: $0.20 per 1M input tokens and $1.25 per 1M output tokens. Context window: 400,000 tokens.

How much does it cost per 1K tokens?

Per 1K tokens: DeepSeek Chat costs $0.0003 input / $0.0011 output. GPT-5.4 nano costs $0.0002 input / $0.0013 output. This is useful for calculating small-scale usage costs.

Which model supports a larger context window?

GPT-5.4 nano offers 400,000 tokens (400K) versus 64K for DeepSeek Chat.

What is the estimated monthly cost for typical usage?

For a typical workload of 10M input + 2M output tokens per month: DeepSeek Chat would cost approximately $4.90, while GPT-5.4 nano would cost $4.50. GPT-5.4 nano is more economical for this usage pattern.

Do these models support prompt caching?

DeepSeek Chat supports prompt caching at $0.070 per 1M cached tokens, reducing costs for repeated context by up to 74%. GPT-5.4 nano supports caching at $0.020 per 1M tokens, saving up to 90%.

Which model is best for my use case?

Choose GPT-5.4 nano for cost-sensitive applications with high input volume. Choose GPT-5.4 nano if you need 400K context for long documents or conversations. Consider prompt caching if you have repeated context. Use our token calculator to model your specific usage pattern.