GPT-6 Luna vs Claude Haiku 5.5: Which Cheap Model Wins

OpenAI launched GPT-6 Luna on September 23, 2026. Anthropic followed with Claude Haiku 5.5 on October 7. Both are the small, fast tier of a new family, and both list at $0.10 per million input tokens and $0.50 per million output tokens. Both discount cached input to $0.01 per million, and both halve the bill for batch work that can wait up to a day.

A note on sources before we go further. I am working from launch coverage and pricing trackers that cite the vendors, not from the vendor pricing pages themselves, and trackers have disagreed with each other this month on names and dates. Treat every number here as a planning figure, and check OpenAI's and Anthropic's pricing pages before you commit a budget.

With the prices matched, a founder is left with a question that spreadsheets do not answer. Which one do you put behind the feature that processes ten million tokens a day? I have watched teams pick on a single benchmark chart, then rip the model out six weeks later when a real prompt behaved differently. The price tie actually helps, because it removes the lazy tiebreaker and forces you to look at the things that decide whether the feature works.

If you have not read our breakdown of where the small Claude model fits, start with Claude Haiku 5.5 pricing and subagent routing. This article is the comparison that one points toward.

Here is the comparison as reported, side by side in plain text.

• Standard price: $0.10 input and $0.50 output per million tokens on both.
• Cached input: $0.01 per million on both.
• Batch: 50 percent off on both, with results inside 24 hours.
• Context window: about 1 million tokens on both, with up to 128K tokens of output.
• Long-context surcharge: this is where they split. Haiku 5.5 reportedly jumps to $0.50 input and $2.50 output once a prompt passes 100K tokens. Luna reportedly holds the standard rate until 272K input tokens, then bills the whole request at $0.20 input and $0.75 output.
• Priority tier: Luna has a fast mode at about double the standard rate. Check whether Anthropic offers an equivalent before you rely on one.
• Reasoning control: Haiku 5.5 takes an effort setting from low to max and thinks adaptively by default. Luna's controls differ, so read its docs for how much hidden reasoning you will pay for.

The long-context rows matter most for a specific kind of product. If your prompts stay under 100K tokens, the surcharge never shows up and the two are interchangeable on price. If you stuff whole contracts, codebases or chat histories into the prompt, the gap between a 100K and a 272K threshold is a real cost difference. A single 150K-token request on Haiku 5.5 would cost five times the base input rate for the entire call, while the same request on Luna stays at the base rate.

Also note what Luna replaced. The previous Luna generation was listed at $0.20 input and $1.20 output, so OpenAI cut the price of its small tier roughly in half for output. When one vendor halves a price, the other usually follows within a quarter. Do not assume today's tie will hold into next year, which is why we keep saying to build for pricing volatility, as we covered in our piece on AI API price changes.

Two models at the same list price can produce very different bills. The reason is that you pay for tokens, and the tokens a model spends vary by task.

Output length

A model that answers in 80 tokens costs less than one that answers in 200, even at the same per-token rate. Classification and extraction prompts with strict output formats make this gap small. Open-ended summaries make it large. Run your real prompts through both and count output tokens.

Hidden reasoning

Both models can think before they answer, and thinking tokens bill as output. At $0.50 per million output tokens this is cheap, but a feature that fires a few hundred million calls a month can still see its bill double because one model reasons longer by default. Set the reasoning level deliberately on both and test again.

Retries and repairs

The cost that does not appear in the token column is the wrong answer. If one model returns malformed JSON in 3 percent of calls and the other in 0.5 percent, you pay for the retries, the validation code and the occasional escalation to a human. At a half cent per thousand tokens the model cost is a rounding error. The repair path is the real expense.

So measure cost per correct answer. Take 200 real inputs from your product, run both models, grade the outputs against what you would have accepted, and divide total spend by the number of passing answers. It takes an afternoon, and it replaces every argument about which model is smarter. Our guide to evaluating LLM quality covers how to build that grading set without fooling yourself.

Here is how I would route common startup workloads today. These are starting points to test, not verdicts, and your data will overrule me.

High-volume classification and tagging

Either model works. Pick the one with the lower error rate on your labels, and use the batch tier if the results are not shown to a user in real time. Half price on a nightly job is free money.

Support summaries and ticket triage

Tone and faithfulness matter more than speed. Test both on your worst tickets, the angry multi-issue ones, not the clean examples. The model that invents fewer details wins, even if it costs a few percent more in output tokens.

Agent subtasks and tool calling

This is where I would test hardest. Small models differ in how reliably they pick the right tool and fill its arguments. A bad call in a loop compounds, because the next step builds on the mistake. If your agent runs five steps, a 2 percent per-step failure rate is nearly a 10 percent failure rate on the whole run.

Long-document work

If prompts regularly pass 100K tokens, the surcharge rule favors Luna on the reported numbers. Better still, stop sending 150K tokens. Retrieval that sends the 8K tokens that matter is cheaper and usually more accurate than any long-context discount.

Anything a customer reads directly

Do not choose by price. Put the cheap model in front of a sample of real users, or at least of real prompts, and read the outputs yourself. A small model that is wrong with confidence costs you more in trust than it saves in tokens.

For the broader pattern of sending easy requests to the cheap tier and hard ones to a larger model, see our guide to model routing and LLM cost optimization. The cheap tier is only half of a good setup.

Picking a model feels like a one-way door. It should not be. The decision gets expensive only when your code treats one vendor's API as the shape of your product.

Here is what locks teams in, in order of how often I see it:

• Prompts tuned to one model. A prompt polished for Haiku 5.5 over three weeks will behave differently on Luna. You will need to re-tune, and you will need your evaluation set to know when you are done.
• Vendor-specific features. Effort settings, structured output flags, prompt caching syntax and tool-call formats all differ. Each one you use directly in application code is a line you rewrite later.
• Scattered API calls. If twelve files import a vendor SDK, a model swap is a twelve-file change. If one module wraps the call, it is a one-file change.
• No logs. Without stored inputs and outputs, you cannot replay traffic through a second model to compare. Log them from day one, with the privacy rules your product needs.

The fix is boring. Put one thin wrapper between your app and the model. Keep prompts in files, not strings buried in handlers. Keep an evaluation set in the repo. Then a model change becomes a config change plus a test run, and you can take whichever vendor cuts its price next. A gateway or proxy layer can help once you run more than one model in production, and the comparison in our post on Helicone, Portkey and LiteLLM covers the options.

You do not need a research team. You need a day and a spreadsheet.

1. Collect 200 real inputs. Pull them from production logs or from beta users. Include the ugly ones: empty fields, mixed languages, very long messages, adversarial phrasing. Remove personal data before it leaves your systems.
2. Write the pass rule first. Decide what a correct answer is before you see any output. For extraction, it is exact field matches. For summaries, it is a short checklist: no invented facts, all key items present, right tone.
3. Run both models with the same prompt, then with a prompt tuned for each. The first run tells you about raw capability. The second tells you what you will get after a week of real work. Record input tokens, output tokens, latency and pass or fail.
4. Compute cost per passing answer and p95 latency. Pick the winner on those two numbers. If they are within a few percent of each other, pick the one with the longer-context rule that suits your prompts, and keep the other warm as a fallback.

Repeat the run whenever either vendor ships a new version. Small models are changing every few months, and a result from August is stale by Christmas. If the bake-off is scripted, the rerun costs you a few dollars of tokens and ten minutes.

One more rule. Do the bake-off on the feature that drives your bill, not on the one that is most fun to build. Most products have one or two calls that account for 80 percent of token spend. Find them in your usage dashboard first.

On the numbers reported so far, Luna and Haiku 5.5 are a coin flip for short prompts, and Luna has the better long-context pricing. That is a narrow edge, and it only matters if you actually send long prompts. For most startup features, the deciding factors are error rate on your data, tool-calling reliability and how cleanly you can swap the model later.

My default advice for a founder with a live product and a rising AI bill:

• Run the bake-off on your top two token-spending calls.
• Put the winner on the cheap tier and a larger model behind a fallback rule for hard cases.
• Turn on caching and batch for anything that allows it, since those cut more than switching vendors does.
• Wrap the model call so the next price cut is a config change.
• Check the vendor pricing pages monthly. The figures in this article will age.

If you are still deciding what to build, treat the cheap tier as good news. A feature that cost real money to run a year ago now costs a few dollars a month at moderate volume, and that changes which ideas are worth trying. If you want a second opinion on where AI belongs in your product and what it will cost to run, book a free strategy call.