In the Haiku 5.5 vs GPT-6 Luna matchup, list price no longer separates the two. Stop asking which model is cheaper per token and ask which one sends fewer wrong answers to your staff, then run 100 of your own items through both before you switch anything.
Sorting 10,000 customer messages costs about $1.00 on Claude Haiku 5.5 and about $1.00 on GPT-6 Luna. That’s the whole price comparison in Haiku 5.5 vs GPT-6 Luna, and it’s why the buying question has moved. Both list at $0.10 per million input tokens and $0.50 per million output tokens for standard prompts. Anthropic published Haiku 5.5’s rates on October 7, and OpenAI’s pricing page shows Luna at the same figures.
Key points
- What does Haiku 5.5 vs GPT-6 Luna buy in a real job? About $1.00 for 10,000 messages. Assume each message runs 500 tokens in and 100 out, which makes 5 million tokens in and 1 million out.
- Where does the real cost sit? With whoever reads the output. Suppose 2% of those 10,000 messages land in a person’s queue and each takes three minutes.
- Can you trust the benchmark table? No, not as a verdict. Anthropic’s launch table has Haiku 5.5 ahead of Luna on every row where Luna appears.
- How do you test them before switching? Run 100 of your own items through both, plus a larger model, and count the corrections.
What does Haiku 5.5 vs GPT-6 Luna buy in a real job?
About $1.00 for 10,000 messages. Assume each message runs 500 tokens in and 100 out, which makes 5 million tokens in and 1 million out. At $0.10 and $0.50 per million, that’s $0.50 plus $0.50 on either model. Haiku 4.5, at $1.00 in and $5.00 out, would bill $10.00. Sonnet 5.5, at $2.00 and $10.00, would bill $20.00.
| Measure | Result |
|---|---|
| Model bill to sort 10,000 customer messages at list price | |
| Claude Haiku 5.5 | $1.00 |
| GPT-6 Luna | $1.00 |
| Claude Haiku 4.5 | $10.00 |
| Claude Sonnet 5.5 | $20.00 |
Those are list-price sums on our assumed message size. Retries aren’t in them. Long prompts change the sums too, since Haiku 5.5 rises to $0.50 and $2.50 above 100,000 tokens and Luna to $0.20 and $0.75. Anthropic says Haiku 5.5 runs about 75% cheaper than Haiku 4.5 on average, and that the figure already counts a new tokenizer that uses slightly more tokens per task.
Where does the real cost sit?
With whoever reads the output. Suppose 2% of those 10,000 messages land in a person’s queue and each takes three minutes. That’s 200 items and 10 hours of work, against a model bill of about $1. These are our illustrative numbers, not a measurement of any firm, but the shape holds.
A dollar of model and ten hours of staff time isn’t a close call.
It also cuts against the instinct to buy up. Sonnet 5.5 costs 20 times more on this job. On Anthropic’s own table it beats Haiku 5.5 on every row, including 1840 against 1620 on GDPval-AA, and a human would still sit on the exceptions. A dearer model shrinks the pile, and you pay 20 times for the privilege, so the pile’s size is what you need to measure.
Can you trust the benchmark table?
No, not as a verdict. Anthropic’s launch table has Haiku 5.5 ahead of Luna on every row where Luna appears. It scores 1620 against 1437 on GDPval-AA, 72.4% against 48.9% on an offline subset of OSWorld 2.1, and 39.2% against 16.4% on Terminal-Bench 4.0. A vendor chose those tests and published its own product’s results.
Anthropic also chose the customer quote, HubSpot’s 92.8% on its own CRM suite using simulated portals.
None of that is dishonest. It’s a vendor describing its product, and Anthropic is candid on one limit. It says Sonnet 5.5 and Opus 5.5 stay better for complex agentic coding, where Sonnet scores 70.6% on that same Terminal-Bench test. Haiku is pitched at narrow jobs like summaries, classification and sub-tasks, and that’s where we’d point it.
The sceptic says cheap models are cheap because they’re worse, and the savings vanish the first time a bad answer reaches a customer. That risk is real, which is why the test below counts fixes. Nobody outside the vendors has published a head-to-head on a small firm’s real data, so the table can’t settle it either way.
How do you test them before switching?
Run 100 of your own items through both, plus a larger model, and count the corrections. Pick one narrow, repetitive job, like sorting inbound messages or pulling fields from invoices. Include a few ugly items. Time each fix, then divide the total cost, with your minutes priced in, by the number of correct answers.
Write down a date to check prices again. Both vendors have changed them within weeks, so treat the figures here as correct on October 8 and likely to age. We covered the flagship tier in our look at GPT-6 Astra pricing, and Opus 5.5’s pricing piece shows how a setting can move a bill more than a list price. For the habit of testing on your own work, see our Kimi K3 test on 20 tasks. Our piece on the hidden time tax of AI explains why review time keeps surprising owners, and cost per successful task is the metric to carry out of this.
If independent tests on ordinary business data showed these models erring so rarely that review time shrinks to nothing, we’d say the model price matters again. Until then, the invoice for the model is the small number.
Cheap models still add up when agents loop, so read our guide to setting an agent token budget.
Frequently asked questions
Is Claude Haiku 5.5 cheaper than GPT-6 Luna?
No. Both list at $0.10 per million input tokens and $0.50 per million output tokens for standard prompts. Differences appear only at long context and in how many tokens each model uses per task.
What is Claude Haiku 5.5 good for?
Anthropic positions it for high-volume, cost-sensitive work such as summaries, classification, database queries and sub-tasks. It says larger models remain better for complex agentic coding.
Should a small business switch to a cheaper AI model?
No, not on price alone. Test 100 real items first and count the corrections, because review time usually costs more than the model.
How much does it cost to sort 10,000 customer messages?
About $1.00 on either Haiku 5.5 or GPT-6 Luna at list price, assuming 500 tokens in and 100 out per message. Review of the exceptions will likely cost more than that.
Claude is made by Anthropic, and we have not tested either model. Written and fact-checked on October 8, 2026. Sources are linked on the claims they support.
