The question I get most often from developers building on AI APIs is some version of "GPT-4o or Claude — which is cheaper?" And the honest answer is: the sticker prices are misleading. I've seen workloads where Claude 3.5 Sonnet ends up 24% cheaper than GPT-4o despite having higher listed rates, and others where GPT-4o is the clear winner. It comes down to one thing — how much of your input context repeats across requests.
Instead of arguing from pricing pages, here's a side-by-side calculation across five real workload types. Run your own numbers at the end.
| Model | Input (per 1M) | Output (per 1M) | Cache hit (input) | Context |
|---|---|---|---|---|
| GPT-4o (legacy) | $2.50 | $10.00 | $1.25 (50% off) | 128K |
| Claude 3.5 Sonnet (legacy) | $3.00 | $15.00 | $0.30 (90% off) | 200K |
GPT-4o is 17% cheaper on input and 33% cheaper on output at standard rates. But those standard rates rarely tell the whole story in production.
Setup: 500-token system prompt, 100-token average user message, 200-token response. No document context. 50,000 requests/month. Prompts vary enough that caching doesn't help much.
| Model | Input cost | Output cost | Monthly total |
|---|---|---|---|
| GPT-4o | $75.00 | $100.00 | $175.00 |
| Claude 3.5 Sonnet | $90.00 | $150.00 | $240.00 |
Winner: GPT-4o — 27% cheaper for this workload. No surprise given the sticker prices.
Setup: 3,000-token system prompt (stable), 2,000-token document context per request (stable within a session), 150-token user question, 400-token response. 30,000 requests/month. Caching applies to system prompt + document context.
| Model | Input cost (w/ caching) | Output cost | Monthly total |
|---|---|---|---|
| GPT-4o (50% cache) | $56.25 | $120.00 | $176.25 |
| Claude 3.5 Sonnet (90% cache) | $27.00 | $180.00 | $207.00 |
Winner: GPT-4o — still cheaper despite Claude's stronger caching, because the output cost difference ($10 vs $15/M) is significant at 400 tokens per response. However, if responses were shorter (say 150 tokens), the gap closes considerably.
Setup: 5,000-token document context (same document across many queries), 200-token analysis prompt, 100-token output (extracting structured fields). 20,000 requests/month. High cache hit rate on the document context.
| Model | Input cost (w/ caching) | Output cost | Monthly total |
|---|---|---|---|
| GPT-4o (50% cache) | $31.25 | $20.00 | $51.25 |
| Claude 3.5 Sonnet (90% cache) | $9.00 | $30.00 | $39.00 |
Winner: Claude 3.5 Sonnet — 24% cheaper. The 90% cache discount on the large document context more than offsets the higher output rate. This reversal is significant: the exact same headline "GPT-4o is cheaper" is wrong for this workload.
Setup: 800-token prompt, 2,500-token output per request (drafting articles, reports). 5,000 requests/month. Minimal repeated context.
| Model | Input cost | Output cost | Monthly total |
|---|---|---|---|
| GPT-4o | $10.00 | $125.00 | $135.00 |
| Claude 3.5 Sonnet | $12.00 | $187.50 | $199.50 |
Winner: GPT-4o — 32% cheaper. For output-heavy workloads with minimal context reuse, the $10 vs $15/M output gap dominates everything else.
| Workload type | Cheaper model | Why |
|---|---|---|
| Simple chatbot, Q&A | GPT-4o | Lower base rates, minimal caching advantage |
| RAG with large contexts | Depends on output length | Claude caching helps input; GPT-4o wins on output |
| Document analysis, short output | Claude 3.5 Sonnet | 90% cache discount dominates |
| Long-form content generation | GPT-4o | Output-heavy, $10 vs $15/M matters a lot |
| Classification/extraction | Neither — use mini models | Both too expensive for this workload |
Run the numbers for your specific workload in under a minute.
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