GPT-4o vs Claude Opus 4.7: Per-Token Cost Comparison

The 6x Input / 7.5x Output Gap

For a 5,000-token prompt with a 1,000-token completion, the per-call cost looks like this:

Model	Input cost	Output cost	Total
GPT-4o	$0.0125	$0.01	$0.0225
Claude Opus 4.7	$0.075	$0.075	$0.150

That is 6.7x more expensive per call. At 100,000 calls a month — a modest production volume for a B2B SaaS chat product — the GPT-4o bill is $2,250 vs. $15,000 for Claude Opus.

When Opus pays for itself

Three scenarios consistently justify the premium:

1. Long-form drafting where readers spend 5+ minutes on the output. Opus's lower hallucination rate and superior coherence over 2K+ token responses cuts the human-edit time roughly in half. If your editor costs $80/hour, recovering 6 minutes per article funds Opus for that article.
2. Tool-use agents where one wrong tool call cascades into 5 wrong calls. Opus's higher tool-call accuracy compounds when the agent has to chain 3+ steps.
3. Coding and refactoring at the architecture level — diff generation, multi-file changes, library migration. The combination of context length and reasoning depth shows up in lower regression rates.

When GPT-4o is the right answer

For chat UI, classification, summarization at scale, structured-data extraction, and most RAG retrieval-then-summarize flows, GPT-4o matches Opus on the metrics users actually notice while costing 1/7th. Combine GPT-4o with prompt caching and you can drop the input portion of the bill another 50%.

Decision shortcut

Run both for a week on real production traffic, sample 100 outputs, and have a human rate them on a 1-5 quality scale. If Opus's mean quality is more than 0.5 points higher than GPT-4o's on the metric you care about, the 7x cost is usually worth it. Below 0.3 points, ship GPT-4o.