Claude Haiku 5.5 vs GPT-6 Luna: Which Cheap AI Model Should You Use?

Claude Haiku 5.5 and GPT-6 Luna charge the same $0.10 per million input tokens, yet one long document can cost five times more on Haiku. For most short, high-volume tasks, Haiku 5.5 is still our pick, based on current independent benchmarks. GPT-6 Luna takes over once your prompts grow past 100,000 tokens, or when the lowest cost per completed task matters more than raw quality.

That answer surprises people, because the two price cards match to the cent. If you're picking a model for a support assistant, a document summarizer, or an AI feature inside your app, the matching card hides what actually sets yourClaude Haiku 5.5 vs GPT-6 Luna at a glance bill.

Two things decide it: where each model's price tier changes, and how many tokens each model spends to finish a job. Below, both are broken down using Anthropic's and OpenAI's own documentation and independent results from Artificial Analysis. Pricing and benchmark figures are as of October 2026.

The two models share the same short-prompt price, output limit, and input types, and split on long-prompt pricing and measured intelligence.

Claude Haiku 5.5 vs GPT-6 Luna specs, with prices per 1 million tokens. Context windows are as each vendor lists them; Artificial Analysis lists both models at 1 million tokens. Pricing as of October 2026, from Anthropic and OpenAI.

What are Claude Haiku 5.5 and GPT-6 Luna?

Claude Haiku 5.5 is Anthropic's low-cost small model, released October 7, 2026, for high-volume work like summaries, classification, and subagent tasks. OpenAI describes GPT-6 Luna as its most efficient model for focused, high-volume tasks. Both support adjustable reasoning effort and about 1 million tokens of context.

Anthropic pitches Haiku 5.5 as a helper model. A larger model such as Opus 5.5 or Sonnet 5.5 plans the work, and Haiku handles the many small jobs underneath it. Anthropic also calls it its fastest model at standard speed, which makes it a fit for live customer support and browser automation.

OpenAI aims GPT-6 Luna at the same kind of work. In practice, GPT-6 Luna vs Claude Haiku 5.5 is a contest for the same job: running thousands of small requests without blowing up your budget.

Haiku 5.5 vs Luna pricing: identical until your prompt passes 100K tokens

Claude Haiku 5.5 and GPT-6 Luna cost exactly the same per token until a prompt passes 100,000 tokens. Between 100,000 and 272,000 tokens, Haiku 5.5 costs five times as much as Luna for the same request.

1. Where each price tier starts

Haiku 5.5 has two price rows. Anthropic charges the higher row for the request once the prompt exceeds 100,000 tokens:

• Up to 100,000 prompt tokens: $0.10 input, $0.50 output, $0.01 cache reads, and $0.125 cache writes per million tokens
• Over 100,000 prompt tokens: $0.50 input, $2.50 output, $0.05 cache reads, and $0.625 cache writes per million tokens

GPT-6 Luna starts at the same $0.10 and $0.50. Its step comes much later and is gentler. Once a request passes 272,000 input tokens, OpenAI charges two times the input and cache rates and 1.5 times the output rate for the full request, which works out to $0.20 input and $0.75 output.

Anthropic says about 90% of requests to its previous Haiku model stayed under 100,000 tokens. If your workload looks like that, the threshold rarely matters. If you feed the model long contracts, transcripts, or codebases, it decides your bill.

2. What a typical request costs on each model

The gap only appears in the middle band. Here is what four realistic requests cost on each model, using list prices with no caching:

Cost per request for Claude Haiku 5.5 vs GPT-6 Luna, calculated from list prices as of October 2026. Excludes thinking tokens, caching, retries, tool fees, Batch or Flex discounts, and regional premiums.

Run the contract-summary job 10,000 times a month and the difference becomes real money: about $800 on Haiku 5.5 against $160 on Luna. The edge of the tier is the sharpest part. A 99,000-token prompt with 2,000 tokens of output costs about $0.011 on Haiku 5.5, while a 101,000-token prompt costs about $0.056.

Pricing tip: Long system prompts, tool lists, and chat history all count toward Haiku's 100,000-token line. Keep them lean, or a short question can land in the expensive tier.

3. Why the same price can mean a bigger bill

Price per token is not price per job. A model that writes three times as many tokens to finish a task costs three times as much, whatever its rate card says.

That is exactly what Artificial Analysis measured at max effort. Haiku 5.5 used about 162,000 output tokens per task, about 129,000 of them hidden reasoning tokens, against about 50,000 for GPT-6 Luna. As a result, Haiku cost $0.21 per task and Luna cost $0.07, even though both charge $0.50 per million output tokens.

Two smaller factors add to the gap. Anthropic notes that Haiku 5.5 uses an updated tokenizer that produces slightly more tokens for the same work than Haiku 4.5 did. And token counts never match across vendors, so the same paragraph can be a different number of billable tokens on each model. Count your real prompts on both before you budget.

Haiku 5.5 vs GPT-6 Luna benchmarks: vendor claims vs independent tests

Haiku 5.5 scores above GPT-6 Luna on every benchmark where Anthropic's launch table reports both models. Artificial Analysis also gives Haiku the higher aggregate score, though Luna wins or ties on several individual evaluations.

1. What Anthropic reports

Anthropic printed GPT-6 Luna in its own launch table, which is unusual and tells you which rival it is targeting. These numbers come from Anthropic's evaluation setup, so read them as vendor-reported results.

Vendor-reported benchmarks from Anthropic's Claude Haiku 5.5 launch announcement, October 2026. Elo scores are relative ratings, not percentages.

The biggest gaps sit in computer use and command-line work, where Haiku 5.5 scores well above Luna. The coding gap on FrontierCode is much narrower at four points.

2. What Artificial Analysis measured

Artificial Analysis, an independent benchmarking firm, puts Haiku 5.5 at 43 on its Intelligence Index against 38 for GPT-6 Luna, with both at max effort. That is a real lead, but a five-point one, and the per-test results are more mixed than the launch table suggests.

Independent results from Artificial Analysis, Intelligence Index v4.3.2, accessed October 8, 2026.

3. Where GPT-6 Luna comes out ahead

GPT-6 Luna wins clearly on one test and holds level on several others. On AutomationBench-AA, which measures multi-step workflows across business software, Luna scores 53% against Haiku's 35%.

Luna also edges ahead on GDP.pdf, a test of reasoning over professional documents, and ties Haiku on SciCode and long-context reasoning. If your app automates chains of actions across tools like CRMs and spreadsheets, test Luna first. Scores for a model this new also tend to move as evaluators re-run their suites, so recheck before you commit.

Also read our GPT-6 Luna benchmarks guide for a deeper look at how the scores hold up across independent evaluations.

How effort settings change the Haiku 5.5 vs Luna comparison

At high effort, Claude Haiku 5.5 matches GPT-6 Luna's max-effort score for one cent more per task. That single setting does more to change the outcome than any benchmark row.

Effort controls how long a model thinks before it answers. More thinking usually means better answers, more hidden tokens, and a higher bill. Haiku 5.5 is the first Haiku model with this dial, and it ships set to medium.

Claude Haiku 5.5 effort settings compared with GPT-6 Luna at max effort. Measured by Artificial Analysis on its benchmark tasks. Snapshot accessed October 8, 2026; Artificial Analysis updates these figures as it re-runs tests.

Three patterns stand out in this table:

• The default trails Luna: Haiku 5.5 at medium scores 34, four points below Luna at max, though it is also the cheapest option at $0.05 per task.
• High is the sweet spot: Haiku 5.5 at high ties Luna's score, costs about the same, and starts answering roughly four times sooner.
• Max is expensive for what it adds: going from xhigh to max buys two points for nearly double the cost per task.

Comparing both models at max effort hides this trade-off. If you run Haiku 5.5, our view is to start at high and move up only for tasks where the extra quality shows up in your own results.

Which is faster, Claude Haiku 5.5 or GPT-6 Luna?

Claude Haiku 5.5 writes text faster than GPT-6 Luna at every effort level Artificial Analysis tested. At max effort, though, Haiku thinks so long that its first answer arrives roughly three to four times later than Luna's.

Speed claims about these two models often seem to contradict each other because they measure different things:

• Output speed: how quickly words stream once the model starts answering. Haiku 5.5 runs at roughly 155 to 245 tokens per second depending on effort, against about 128 for Luna at max.
• Time to first token: how long you wait before anything appears, which includes thinking time. At max effort, Haiku 5.5 waited several minutes on Artificial Analysis's tasks, roughly three to four times as long as Luna at max.

Those waits come from long, difficult benchmark tasks, so measure latency on your own requests before you decide. The relative pattern is still useful. At medium or high effort, Haiku 5.5 starts sooner and streams faster than Luna at max. At max effort, Luna gets to an answer first.

Customer reports point the same way at practical settings. Asana told Anthropic that Haiku 5.5 cut task-completion latency by more than 30% compared with the model it uses today, and Box reported about half the latency of Haiku 4.5. Both are vendor-supplied testimonials rather than independent tests.

Speed tip: For live chat, voice, or support, run Haiku 5.5 at medium or high effort. Max effort trades response time for a few points of quality that users of a live product rarely notice.

Which model makes fewer things up?

Claude Haiku 5.5 is the more reliable of the two on factual questions, according to Artificial Analysis. On its AA-Omniscience Index, Haiku 5.5 scores 11 and GPT-6 Luna scores 1, both at max effort.

The index rewards correct answers, subtracts points for confident wrong answers, and does not penalize a model for saying it doesn't know. A score near zero means a model gets roughly as many questions wrong as right when it chooses to answer. Haiku's higher score means it performs better on this measure of factual reliability.

Neither score is high in absolute terms, so don't rely on either model to recall facts unaided. Give it the source material, such as a help article or a product sheet, and ask it to answer from that.

Should you use Claude Haiku 5.5 or GPT-6 Luna?

Choose by prompt length first and task type second. Claude Haiku 5.5 wins most short, high-volume jobs, and GPT-6 Luna wins long-document work and multi-step business automation.

Which model to pick by workload, based on list pricing and benchmark results as of October 2026.

You don't have to pick only one. Some teams send short requests to one model and long ones to the other. That split only pays off if a large share of your traffic sits above 100,000 tokens, though. If most of your requests are short, a single model keeps your setup and your billing simpler.

What this means when you're building an app

If you're building an app instead of writing code, the model choice shows up as your monthly running cost per feature. For most short AI features, Haiku 5.5 and GPT-6 Luna cost the same, so quality and speed should decide.

Two examples make the difference concrete:

• A support assistant answering 20,000 short questions a month, at about 5,000 tokens in and 1,000 out each, costs roughly $20 a month on either model, before thinking tokens, caching, and retries.
• A contract-review feature reading 2,000 documents of about 150,000 tokens each costs roughly $160 a month on Haiku 5.5 and $32 on GPT-6 Luna.

The first feature should run on whichever model gives better answers in your tests. The second is where the 100,000-token line earns attention.

Emergent, the AI app builder that publishes this guide, lets you add features like these by describing them in plain English. Its Universal LLM Key gives your app access to Claude, GPT, and Gemini models through one credential, with usage drawn from your Emergent credits. You can switch models without opening separate accounts with Anthropic and OpenAI. Model lineups change as new versions launch, so confirm the current list in your workspace before you build.

Pick by prompt length, then by task

In the Claude Haiku 5.5 vs GPT-6 Luna matchup, Haiku 5.5 is the better default for short, high-volume work. It scores higher on independent tests and starts answering sooner at practical effort settings. Until a prompt passes 100,000 tokens, you pay exactly what Luna costs.

GPT-6 Luna earns its place on long documents, where its price stays flat to 272,000 tokens, and on multi-step business automation. At max effort it also finishes tasks for about a third of Haiku's cost, because it uses far fewer output tokens, including reasoning.

Your next step is a small test. Run 20 to 50 real requests from your app through Haiku 5.5 at high effort and Luna at its default, then compare answer quality and cost per finished task. The same habit applies to any AI feature you build: choose the model that fits the job, not the one with the best launch table. On Emergent, you can pick from Claude, GPT, and Gemini models for your app and switch as your needs change.

Start Building on Emergent.