Anthropic's Claude family has quietly become the go-to for enterprise AI workloads. The pricing structure is different enough from OpenAI's that teams migrating between them frequently make budget mistakes.
Here's a full breakdown of what everything costs in 2025 and where the real savings are.
Current Claude Pricing
| Model | Input per 1M | Output per 1M | Context |
|---|---|---|---|
| Claude Opus 4.7 | $15.00 | $75.00 | 200K |
| Claude Sonnet 4.6 | $3.00 | $15.00 | 200K |
| Claude Haiku 4.5 | $0.80 | $4.00 | 200K |
All Claude models support 200K token context windows — significantly larger than GPT-4o's 128K. This matters for document processing and long-context tasks.
Extended Context Caching: The 90% Discount
Claude's prompt caching is the most aggressive in the industry:
| Cache operation | Cost |
|---|---|
| Cache write | 25% premium on input price |
| Cache read | 90% discount on input price |
| Cache TTL | 5 minutes |
For a 10,000-token system prompt at Sonnet prices ($3.00/M input):
- Without caching: $0.03 per request
- With caching (read): $0.0003 per request — 100x cheaper
Teams with large, stable system prompts (legal docs, product catalogs, knowledge bases) should design their entire Claude integration around prompt caching. The ROI is extreme.
Claude vs GPT-4o: Real Cost Comparison
For a typical customer support use case (800 input / 200 output tokens per request):
| Model | Cost per request | Cost per 100K requests |
|---|---|---|
| GPT-4o | $0.004 | $400 |
| Claude Sonnet 4.6 | $0.0054 | $540 |
| GPT-4o mini | $0.00024 | $24 |
| Claude Haiku 4.5 | $0.00144 | $144 |
GPT-4o mini is the cheapest for high-volume simple tasks. Claude Haiku is 6x more expensive than mini but significantly more capable on complex instructions.
When to Choose Claude Over GPT
Claude tends to outperform GPT-4o on:
- Long document analysis (200K vs 128K context, better retention at the end of long contexts)
- Following complex instructions (lower refusal rate, more literal compliance)
- Code with nuance (Claude Sonnet frequently beats GPT-4o on complex refactoring)
- Writing style and voice matching (for brand-voice content generation)
GPT-4o tends to win on:
- Speed (GPT-4o mini is faster than Haiku on simple tasks)
- Tool calling and function execution (more mature ecosystem)
- Image understanding (GPT-4o vision is more robust for structured image data)
Practical Claude Cost Examples
Building a RAG chatbot on 50,000 requests/day:
Without caching (2,000 token system prompt + 500 token retrieval + 100 token query → 300 token answer):
- Input: 2,600 tokens × $3.00/M = $0.0078/request → $390/day
- Output: 300 tokens × $15.00/M = $0.0045/request → $225/day
- Total: $615/day
With prompt caching on the system prompt (1,984 cached, 616 fresh input):
- Cached input: 1,984 × $0.30/M = $0.000595/request
- Fresh input: 616 × $3.00/M = $0.00185/request
- Output unchanged
- Total: ~$350/day — 43% savings
Use the Claude Token Cost Calculator to model your specific workload.