Published rates rank differently once prompt caching, batch discounts and your own input-to-output ratio are applied. Run both against your numbers.
Grok 4.5 vs GPT-5
Two previous-generation frontier models with very different output prices.
OpenAI retires GPT-5 on 11 Dec 2026; the named replacement is GPT-5.6 Sol.
Monthly cost by workload shape
The same volume costs very different amounts depending on the shape of the traffic. No caching or batch applied.
| Workload | Grok 4.5 | GPT-5 | Difference | |
|---|---|---|---|---|
Support chat 200/day · 1500 in / 300 out | $29.22 | $29.68 | Grok 4.5saves $0.46 | Model it → |
RAG / search answers 20K/day · 3000 in / 400 out | $5,113.92 | $4,718.20 | GPT-5saves $395.72 | Model it → |
Agent / tool-use loop 8.0K/day · 2500 in / 600 out | $2,094.27 | $2,222.12 | Grok 4.5saves $127.85 | Model it → |
Batch summarization 100K/day · 5000 in / 500 out | $39.6K | $34.2K | GPT-5saves $5,327.00 | Model it → |
Code assistant 15K/day · 2000 in / 800 out | $4,018.08 | $4,794.30 | Grok 4.5saves $776.22 | Model it → |
Frequently asked questions
Which is cheaper, Grok 4.5 or GPT-5?
At a typical 3:1 input-to-output ratio, Grok 4.5 is cheaper: $3.00 blended per 1M tokens against $3.44. That flips once output exceeds 16% of your total tokens, because the two price input and output differently.
What is the price difference between Grok 4.5 and GPT-5?
Grok 4.5: $2.00 input, $6.00 output per 1M tokens. GPT-5: $1.25 input, $10.00 output. That is 1.6x on input and 1.7x on output.
Does prompt caching change the answer?
Grok 4.5 caches input at $0.300/1M and GPT-5 caches at $0.125/1M. For input-heavy workloads with repeated context, caching can matter more than the base rate difference, model both in the calculator rather than comparing rate cards.
This page compares published prices only. It makes no claim about which model performs better on any task; that depends entirely on your evaluations.
Rate cards decide nothing on their own.
Most teams run several models at once. Finitizer Tokenomics shows what each one actually costs you, read from your own bills and exports, which workloads could test a smaller model, what they would cost on an open-weight model, and, for models you serve yourself, the cost per token beside the API price. A routing decision starts from your spend rather than a price page.
See Finitizer Tokenomics