Last verified 2026-02-11 (left) · 2025-09-22 (right)

Claude Sonnet 4.5 vs GPT-5 Chat — Pricing & Capability Comparison

Claude Sonnet 4.5 charges $3.00 per million input tokens and $15.00 per million output tokens. GPT-5 Chat comes in at $1.25 / $10.00. Context windows span 200K vs 200K tokens respectively.

TL;DR — Quick Comparison

✓GPT-5 Chat is cheaper overall: $11.25 per 1M tokens (in+out) vs $18.00 for Claude Sonnet 4.5 — saves $6.75 per 1M tokens
✓Input pricing: Claude Sonnet 4.5 $3.00/1M vs GPT-5 Chat $1.25/1M
✓Output pricing: Claude Sonnet 4.5 $15.00/1M vs GPT-5 Chat $10.00/1M
✓Context window: Claude Sonnet 4.5 offers more (200K vs 200K)
✓Use our calculator below to estimate costs for your specific usage pattern

Input price (per 1M)

Claude Sonnet 4.5

$3.00

GPT-5 Chat

$1.25

GPT-5 Chat leads here

Output price (per 1M)

Claude Sonnet 4.5

$15.00

GPT-5 Chat

$10.00

GPT-5 Chat leads here

Context window

Claude Sonnet 4.5

200,000 tokens

GPT-5 Chat

200,000 tokens

Claude Sonnet 4.5 leads here

Cached input

Claude Sonnet 4.5

$0.300

GPT-5 Chat

$0.125

GPT-5 Chat leads here

Which one should you choose?

Skip the spreadsheet if you just need the practical takeaway. Use these rules when deciding between Claude Sonnet 4.5 and GPT-5 Chat.

Choose GPT-5 Chat if input tokens dominate your bill

GPT-5 Chat 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 GPT-5 Chat if you generate long answers

GPT-5 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 Claude Sonnet 4.5 if context size is the blocker

Claude Sonnet 4.5 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	Claude Sonnet 4.5	GPT-5 Chat	Claude Sonnet 4.5 cached	GPT-5 Chat cached
Balanced conversation 50% input · 50% output	$0.0900	$0.0563	$0.0765	$0.0506
Input-heavy workflow 80% input · 20% output	$0.0540	$0.0300	$0.0324	$0.0210
Generation heavy 30% input · 70% output	$0.114	$0.0738	$0.106	$0.0704
Cached system prompt 90% cached input · 10% fresh output	$0.0420	$0.0212	$0.0177	$0.0111

Frequently asked questions

Which is cheaper: Claude Sonnet 4.5 or GPT-5 Chat?

GPT-5 Chat is cheaper for input tokens at $1.25 per 1M tokens compared to $3.00. For output, GPT-5 Chat costs $10.00 per 1M tokens versus $15.00 for Claude Sonnet 4.5.

What is the cost per 1M tokens for Claude Sonnet 4.5?

Claude Sonnet 4.5 pricing: $3.00 per 1M input tokens and $15.00 per 1M output tokens. Context window: 200,000 tokens.

What is the cost per 1M tokens for GPT-5 Chat?

GPT-5 Chat pricing: $1.25 per 1M input tokens and $10.00 per 1M output tokens. Context window: 200,000 tokens.

How much does it cost per 1K tokens?

Per 1K tokens: Claude Sonnet 4.5 costs $0.0030 input / $0.0150 output. GPT-5 Chat costs $0.0013 input / $0.0100 output. This is useful for calculating small-scale usage costs.

Which model supports a larger context window?

Claude Sonnet 4.5 offers 200,000 tokens (200K) versus 200K for GPT-5 Chat.

What is the estimated monthly cost for typical usage?

For a typical workload of 10M input + 2M output tokens per month: Claude Sonnet 4.5 would cost approximately $60.00, while GPT-5 Chat would cost $32.50. GPT-5 Chat is more economical for this usage pattern.

Do these models support prompt caching?

Claude Sonnet 4.5 supports prompt caching at $0.300 per 1M cached tokens, reducing costs for repeated context by up to 90%. GPT-5 Chat supports caching at $0.125 per 1M tokens, saving up to 90%.

Which model is best for my use case?

Choose GPT-5 Chat for cost-sensitive applications with high input volume. Choose Claude Sonnet 4.5 if you need 200K context for long documents or conversations. Consider prompt caching if you have repeated context. Use our token calculator to model your specific usage pattern.