GPT-6 Luna vs Claude Haiku 4.5
Both are their lab’s low-cost tier. GPT-6 Luna is much newer, holds five times the context and can switch reasoning off entirely; Haiku 4.5 is a proven, fast choice for live chat, but its knowledge is more than a year older and Anthropic only guarantees it until mid-October 2026.
Choose GPT-6 Luna if
- A long context — 1.05M tokens against Haiku’s 200K.
- Newer knowledge, to May 2026.
- High-volume extraction and routing.
Choose Claude Haiku 4.5 if
- Live chat and support, which Anthropic built it for.
- Sub-agents working under a Claude orchestrator.
- Sending PDFs directly.
Side by side
| GPT-6 Luna | Claude Haiku 4.5 | |
|---|---|---|
| Made by | OpenAI | Anthropic |
| Released | September 2026 | October 2025 |
| Input price, per 1M tokens | A$0.1514 | A$1.51 |
| Output price, per 1M tokens | A$0.7571 | A$7.57 |
| Cached input, per 1M tokens | A$0.0151 | A$0.1514 |
| Batch (about half price) | Yes | Yes |
| Context window | 1.05M tokens | 200K tokens |
| Longest answer | 128K tokens | 64K tokens |
| Reads | Text, Images | Text, Images, PDFs |
| Thinks before answering | Yes, adjustable | Yes, adjustable |
| Open weights | No | No |
Prices are Roar AI’s published rates, live from our price list. Specs are each lab’s own published figures. Prices are our published US dollar rates converted at A$1.47 to the dollar, our rate for October — the same rate your card is charged.
What a real job costs
Take 1,000 requests, each about 3,000 tokens in and 1,000 out — a long prompt and a page of answer.
GPT-6 Luna does the same job for 10× less. Models that think before answering spend extra output tokens doing it, so on hard prompts the real gap can be wider than the rates suggest.
Use both, switch any time
On Roar AI both are on the same key and the same monthly invoice, paid in your local currency. Trying the other one is a one-word change:
client.chat.completions.create(model="gpt-6-luna", …) client.chat.completions.create(model="claude-haiku-4-5", …)
Send the same prompts to both for a day and compare the answers on your own work — that settles it faster than any benchmark. Read the docs.
Try both
on one key
Send the same prompts to each and compare the answers on your own work.