Published rates rank differently once prompt caching, batch discounts and your own input-to-output ratio are applied. Run both against your numbers.
Claude Haiku 4.5 vs GPT-5 nano
Cheap-tier models for classification and routing.
OpenAI retires GPT-5 nano on 11 Dec 2026; the named replacement is GPT-5.6 Luna.
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 | Claude Haiku 4.5 | GPT-5 nano | Difference | |
|---|---|---|---|---|
Support chat 200/day · 1500 in / 300 out | $18.26 | $1.19 | GPT-5 nanosaves $17.08 | Model it → |
RAG / search answers 20K/day · 3000 in / 400 out | $3,044.00 | $188.73 | GPT-5 nanosaves $2,855.27 | Model it → |
Agent / tool-use loop 8.0K/day · 2500 in / 600 out | $1,339.36 | $88.88 | GPT-5 nanosaves $1,250.48 | Model it → |
Batch summarization 100K/day · 5000 in / 500 out | $22.8K | $1,369.80 | GPT-5 nanosaves $21.5K | Model it → |
Code assistant 15K/day · 2000 in / 800 out | $2,739.60 | $191.77 | GPT-5 nanosaves $2,547.83 | Model it → |
Frequently asked questions
Which is cheaper, Claude Haiku 4.5 or GPT-5 nano?
At a typical 3:1 input-to-output ratio, GPT-5 nano is cheaper: $0.138 blended per 1M tokens against $2.00. GPT-5 nano is cheaper at every input and output mix.
What is the price difference between Claude Haiku 4.5 and GPT-5 nano?
Claude Haiku 4.5: $1.00 input, $5.00 output per 1M tokens. GPT-5 nano: $0.050 input, $0.400 output. That is 20.0x on input and 12.5x on output.
Does prompt caching change the answer?
Claude Haiku 4.5 caches input at $0.100/1M and GPT-5 nano caches at $0.00500/1M. 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