GPT-5.6 Luna vs Claude Haiku 4.5
The cheapest current model from each provider, for high-volume work.
Side by side
| Attribute | GPT-5.6 Luna | Claude Haiku 4.5 |
|---|---|---|
| Input, per 1M tokens | $0.20 | $1.00 |
| Output, per 1M tokens | $1.20 | $5.00 |
| Context window | 1,050,000 | 200,000 |
| Provider | OpenAI | Anthropic |
| Tokenizer | o200k_base | Anthropic tokenizer |
| Long-context tier | above 272,000 | none |
| Retires | no date announced | no date announced |
Which to choose
- On cost, GPT-5.6 Luna is 5x cheaper per million input tokens. On a workload that reads far more than it writes, that ratio is close to the whole difference in the bill.
- On capacity, GPT-5.6 Luna holds more in one request. That matters only if you are near the smaller ceiling — below it, the extra room changes nothing.
- On long prompts, at least one of these bills the entire request at an elevated rate past a threshold. A comparison made on short prompts can invert once you cross it.
What this page does not tell you. Which model produces better answers. That depends on your task, and this site has no benchmark data — everything above is drawn from published pricing and specifications. Compare quality by running your own prompts through both.
Common questions
- Is GPT-5.6 Luna or Claude Haiku 4.5 cheaper?
- GPT-5.6 Luna is cheaper on input — $0.20 per million against $1.00, a difference of 5x. Output rates differ separately, and a workload heavy on generated text can reverse which one costs less overall.
- Which has the larger context window?
- GPT-5.6 Luna, at 1,050,000 tokens against 200,000.
- Can I compare them on my own prompt?
- Yes. The calculator counts any text against every model at once, so you can see both figures for your actual workload rather than for a generic example.