Is Claude 4.7 Opus or GPT-4.1 cheaper?

For a typical request (10,000 input + 2,000 output tokens), GPT-4.1 is cheaper — about 88% less, or roughly $26400 saved per 100,000 requests. Claude 4.7 Opus runs $15/$75 per 1M input/output tokens; GPT-4.1 runs $2/$8.

Which has the larger context window?

GPT-4.1, at 1,047,576 tokens versus 200,000.

How accurate are these token counts?

Claude 4.7 Opus: Approximated with cl100k_base — drift typically <2% on English and code. GPT-4.1: Exact tokenization via the canonical OpenAI vocab (o200k_base). The dollar math itself is exact once the token count is known.

Claude 4.7 Opus vs GPT-4.1: pricing & cost comparison

On input tokens, GPT-4.1 is the cheaper of the two — 87% less per million ($15 vs $2). On output, GPT-4.1 is 89% cheaper ($75 vs $8) — and since output is usually the dominant cost driver, that gap matters more than it looks.

Side by side

	Claude 4.7 Opus	GPT-4.1
Input / 1M tokens	$15	$2
Output / 1M tokens	$75	$8
Context window	200,000	1,047,576
Token-count accuracy	±2%	exact
Cost — 10,000 input + 2,000 output tokens	$0.3	$0.036

What a real request costs

Take a representative turn — 10,000 input + 2,000 output tokens. Claude 4.7 Opus comes to $0.3, GPT-4.1 to $0.036. Across 100,000 requests that's a $26400 swing in favour of GPT-4.1. To run the numbers on your actual prompt, paste it into the calculator and toggle Compare across all models.

Which should you pick?

These are different vendors, so a switch means a different API and a slightly different tokenizer — budget a small calibration buffer. GPT-4.1 give exact counts; the others land within a few percent. See the full breakdown on the dedicated pages for Claude 4.7 Opus and GPT-4.1.

FAQ

Is Claude 4.7 Opus or GPT-4.1 cheaper?: For a typical request (10,000 input + 2,000 output tokens), GPT-4.1 is cheaper — about 88% less, or roughly $26400 saved per 100,000 requests. Claude 4.7 Opus runs $15/$75 per 1M input/output tokens; GPT-4.1 runs $2/$8.
Which has the larger context window?: GPT-4.1, at 1,047,576 tokens versus 200,000.
How accurate are these token counts?: Claude 4.7 Opus: Approximated with cl100k_base — drift typically <2% on English and code. GPT-4.1: Exact tokenization via the canonical OpenAI vocab (o200k_base). The dollar math itself is exact once the token count is known.