Last verified 2026-05-15 (left) · 2026-05-15 (right)

GPT-5.5 vs Claude Opus 4.7 — Pricing & Capability Comparison

GPT-5.5 charges $5.00 per million input tokens and $30.00 per million output tokens. Claude Opus 4.7 comes in at $5.00 / $25.00. Context windows span 1M vs 1M tokens respectively.

TL;DR — Quick Comparison

✓Claude Opus 4.7 is cheaper overall: $30.00 per 1M tokens (in+out) vs $35.00 for GPT-5.5 — saves $5.00 per 1M tokens
✓Input pricing: GPT-5.5 $5.00/1M vs Claude Opus 4.7 $5.00/1M
✓Output pricing: GPT-5.5 $30.00/1M vs Claude Opus 4.7 $25.00/1M
✓Context window: GPT-5.5 offers more (1M vs 1M)
✓Use our calculator below to estimate costs for your specific usage pattern

Input price (per 1M)

GPT-5.5

$5.00

Claude Opus 4.7

$5.00

GPT-5.5 leads here

Output price (per 1M)

GPT-5.5

$30.00

Claude Opus 4.7

$25.00

Claude Opus 4.7 leads here

Context window

GPT-5.5

1,000,000 tokens

Claude Opus 4.7

1,000,000 tokens

GPT-5.5 leads here

Cached input

GPT-5.5

$0.500

Claude Opus 4.7

$0.500

GPT-5.5 leads here

Which one should you choose?

Skip the spreadsheet if you just need the practical takeaway. Use these rules when deciding between GPT-5.5 and Claude Opus 4.7.

Choose GPT-5.5 if input tokens dominate your bill

GPT-5.5 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 Claude Opus 4.7 if you generate long answers

Claude Opus 4.7 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-5.5 if context size is the blocker

GPT-5.5 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	GPT-5.5	Claude Opus 4.7	GPT-5.5 cached	Claude Opus 4.7 cached
Balanced conversation 50% input · 50% output	$0.175	$0.150	$0.152	$0.128
Input-heavy workflow 80% input · 20% output	$0.100	$0.0900	$0.0640	$0.0540
Generation heavy 30% input · 70% output	$0.225	$0.190	$0.211	$0.177
Cached system prompt 90% cached input · 10% fresh output	$0.0750	$0.0700	$0.0345	$0.0295

Frequently asked questions

Which is cheaper: GPT-5.5 or Claude Opus 4.7?

GPT-5.5 is cheaper for input tokens at $5.00 per 1M tokens compared to $5.00. For output, Claude Opus 4.7 costs $25.00 per 1M tokens versus $30.00 for GPT-5.5.

What is the cost per 1M tokens for GPT-5.5?

GPT-5.5 pricing: $5.00 per 1M input tokens and $30.00 per 1M output tokens. Context window: 1,000,000 tokens.

What is the cost per 1M tokens for Claude Opus 4.7?

Claude Opus 4.7 pricing: $5.00 per 1M input tokens and $25.00 per 1M output tokens. Context window: 1,000,000 tokens.

How much does it cost per 1K tokens?

Per 1K tokens: GPT-5.5 costs $0.0050 input / $0.0300 output. Claude Opus 4.7 costs $0.0050 input / $0.0250 output. This is useful for calculating small-scale usage costs.

Which model supports a larger context window?

GPT-5.5 offers 1,000,000 tokens (1M) versus 1M for Claude Opus 4.7.

What is the estimated monthly cost for typical usage?

For a typical workload of 10M input + 2M output tokens per month: GPT-5.5 would cost approximately $110.00, while Claude Opus 4.7 would cost $100.00. Claude Opus 4.7 is more economical for this usage pattern.

Do these models support prompt caching?

GPT-5.5 supports prompt caching at $0.500 per 1M cached tokens, reducing costs for repeated context by up to 90%. Claude Opus 4.7 supports caching at $0.500 per 1M tokens, saving up to 90%.

Which model is best for my use case?

Choose GPT-5.5 for cost-sensitive applications with high input volume. Choose GPT-5.5 if you need 1M context for long documents or conversations. Consider prompt caching if you have repeated context. Use our token calculator to model your specific usage pattern.