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

Two cheap models. One identical price tag. And a catch buried in the fine print.

That’s Claude haiku 5.5 vs GPT 6 luna in three sentences. Anthropic launched Haiku 5.5 on October 8, 2026, and it set the price to match GPT’s Luna exactly: $0.10 per million input tokens and $0.50 per million output tokens. So, if you’re shopping for the best lightweight AI models for API work, it looks like a coin flip.

It isn’t. In the anthropic haiku 5.5 vs GPT 6 luna matchup, Haiku wins most of the benchmarks and Luna wins a lot of the invoices. Which one matters more depends on what you’re building. This guide covers haiku 5.5 vs GPT 6 luna pricing and speed, plus the stuff the launch posts skip, like the price jump at 100K tokens and the reasoning tokens you pay for but never see.

One note on sources. These models are days and weeks old. The numbers below come from Anthropic’s launch materials, Artificial Analysis runs and a handful of independent comparison sites, all checked around October 8 and 9, 2026. Where they disagree, I’ll say so.

Executive Summary: Claude Haiku 5.5 vs. GPT-6 Luna at a Glance

Both models do the same kind of job. They handle the repetitive, well-defined work that gets expensive at scale: pulling fields out of documents, summarizing, sorting support tickets, answering database questions, and doing the small tasks a bigger agent hand off.

Here’s the short version.

Features	Claude Haiku 5.5	GPT-6 Luna
Released	October 8, 2026	September 23, 2026
Base price (input / output per 1M tokens)	$0.10 / $0.50	$0.10 / $0.50
Price jumps at	Above 100K prompt tokens	Above 272K input tokens
Price after the jump	$0.50 / $2.50	$0.20 / $0.75
Cache read price	$0.01 per 1M (up to 100K)	$0.01 per 1M
Context window	Around 1M tokens (one tracker lists 200K, so check the docs)	About 1.05M tokens
Adjustable thinking effort	Low, Medium, High, Max	Yes, with its own effort labels
Best at	Computer use, agentic coding, knowledge work, fewer made-up answers	Cheap long prompts, low token use, simple high-volume jobs

Notice the price row. They match until a prompt crosses 100,000 tokens. After that, Haiku gets five times more expensive per token. Luna doesn’t flinch until 272,000.

So, who’s it for? Pick Haiku if your tasks are tricky, if the model has to click around software or write code, or if wrong answers cost you money. Pick Luna if your prompts are long, your tasks are simple and your bill is the main thing you watch. Mixed workloads? Keep reading, because the answer might be both.

Benchmark Comparison: Intelligence, Computer Use & Agentic Coding

Let’s start with where Haiku looks strong. It looks very strong.

Computer use. On OSWorld 2.1, which tests whether a model can operate real desktop software, Haiku 5.5 scored 72.4% on the offline subset. Luna scored 48.9%. That’s a big gap. If you’re building an agent that opens apps, fills in forms or moves files around, this is the number to care about. It’s the headline reason people say haiku 5.5 vs GPT 6 luna computer use isn’t a close race.

Agentic coding. On Terminal-Bench 4.0, which has a model work through command-line tasks, Haiku scored 39.2% against Luna’s 16.4%. That’s more than double. For agentic coding haiku 5.5 vs luna, Haiku is the clear pick. Another comparison site also lists Haiku ahead on FrontierCode 1.1.

Knowledge work. On GDPval-AA v2.1, which measures real office-style tasks, Haiku 5.5 scored an Elo of 1620. Luna got 1437. For context, last-generation Haiku 4.5 sat at 735, and the bigger Sonnet 5.5 hit 1840. So Haiku jumped a long way and landed about a third of the way from Luna to Sonnet. That’s worth knowing if you’re wondering whether you can skip the pricier model.

General intelligence. On the Artificial Analysis Intelligence Index, Haiku 5.5 scored 43 at max effort and 38 at high. Luna scored 38 at max. So Haiku at high effort roughly ties Luna at max. You only pull ahead if you pay for the top setting.

Hallucinations. One independent review says Haiku makes things up far less often than Luna, while Luna actually knows more raw facts. Those are two different things. A model that knows less but admits it is often safer for business use than one that knows more and bluffs. If you’re doing customer-facing answers or anything that touches money, that matters.

Vision. Haiku 5.5 handles images, which counts for document scans, screenshots and charts. I couldn’t find a trustworthy head-to-head vision score against Luna, so I won’t invent one. Test it on your own files.

What I couldn’t confirm. Humanity’s Last Exam and AA-LCR (a long-context reasoning test) come up in a lot of comparisons. I couldn’t find clean, sourced numbers for both models on either one. If a blog hands you exact figures without saying where they came from, be careful.

And one honest warning. Most of these results come from Anthropic’s launch materials or from tests run with specific settings. Anthropic itself says to validate the gap on your own tasks. Good advice. Run fifty of your real prompts through both models before you commit.

Cost & Token Tiering Breakdown: The 100K Token Pricing Cliff

This is the part that trips people up.

On paper, haiku 5.5 vs GPT 6 luna input pricing is a tie. In practice, there’s a haiku 5 5 100k token price step. Once a single request goes past 100,000 tokens, Haiku charges $0.50 per million input tokens and $2.50 per million output. That’s five times the base rate, and it applies to the whole request, not just the part over the line.

Luna has the same kind of step, but it’s gentler and it comes later. Past 272,000 input tokens, Luna goes to $0.20 input and $0.75 output. That’s double on input and 1.5 times on output, not five times.

Here’s the full picture.

Prompt size	Haiku 5.5 (in / out)	GPT-6 Luna (in / out)
Up to 100K	$0.10 / $0.50	$0.10 / $0.50
100K to 272K	$0.50 / $2.50	$0.10 / $0.50
Over 272K	$0.50 / $2.50	$0.20 / $0.75

Now let’s put real requests through it.

Request	Haiku 5.5	GPT-6 Luna
10K input, 2K output	$0.002	$0.002
150K input, 10K output	$0.100	$0.020
400K input, 10K output	$0.225	$0.0875

At 10K tokens in, you can’t tell them apart. At 150K, Haiku costs five times as much. At 400K, it’s about 2.6 times as much. Run a million of those 150K requests and you’re looking at $100,000 versus $20,000. Same task, same sticker price on the pricing page.

Agent loops make it worse. This is the sneaky one. An agent keeps adding to its conversation every turn: tool results, file contents, its own notes. One analysis found a 20-turn agent run crosses Haiku’s 100K line around turns 16. Everything before that is cheap. The last few turns cost five times as much. If you’ve budgeted off the headline price, your bill will surprise you.

Caching helps both, with a twist. Both models charge $0.01 per million tokens to read from cache, which is a huge discount on repeated context like system prompts or a big reference document. On prompt caching haiku 5.5 vs GPT luna, the base write prices match at $0.125 per million. But Haiku’s cache writes jumps to $0.625 above 100K, while Luna’s only goes to $0.25 above 272K. Also, Haiku’s cache lasts five minutes, so if your traffic is bursty, you may keep paying to rewrite it. Check how long Luna’s lasts in GPT’s docs before you plan around it. That’s the real GPT 6 luna api caching costs question.

Want to sanity check your own workload? This tiny script does it:

def haiku_cost(input_tokens, output_tokens): # Whole request moves to the higher rate above 100K input tokens if input_tokens > 100_000: return input_tokens * 0.50 / 1e6 + output_tokens * 2.50 / 1e6 return input_tokens * 0.10 / 1e6 + output_tokens * 0.50 / 1e6   def luna_cost(input_tokens, output_tokens): if input_tokens > 272_000: return input_tokens * 0.20 / 1e6 + output_tokens * 0.75 / 1e6 return input_tokens * 0.10 / 1e6 + output_tokens * 0.50 / 1e6   print(haiku_cost(150_000, 10_000))  # 0.10 print(luna_cost(150_000, 10_000))   # 0.02

If you’re hunting for the cheapest fast LLM API 2026 has to offer, the honest answer is Luna for long prompts and a tie for short ones. Haiku only wins on cost if its better accuracy means fewer retries, or if you can keep every request under 100K.

So, can you keep requests short? Often, yes. Chunk big documents, trim old turns out of agent memory, summarize as you go. It’s extra engineering, but it keeps Haiku at its best price.

Speed & Latency: Output Tokens per Second & Time-to-First-Token

Speed is where the haiku 5.5 vs GPT 6 luna pricing and speed story gets interesting, because Haiku isn’t just the smarter one here.

One gateway, AIHubMix, measured these numbers:

	Haiku 5.5	GPT-6 Luna
Haiku 5.5 output tokens per second	155	96.7
Time to first token	3.5 seconds	4.6 seconds

So, Haiku streams about 60% faster once it starts talking, and it starts about a second sooner. For a chat bot or a live assistant, that’s noticeable. A reply that finishes in four seconds feels very different from one that takes six.

Now the caveats. This is one provider’s measurement. Speed changes by hour, by region and by how busy the servers are. Both models are brand new, and early traffic is unpredictable. And a 3.5-second wait before the first word is slow for a “fast” model. Neither of these is going to feel instant.

Why so slow to start? Mostly because these models can think before they answer. Which brings us to the next part.

If your app is built around fastest low cost llm for streaming, test both under your own load for a day or two. Look at the 95th percentile, not the average. Users remember the slow responses, not the typical ones.

Reasoning Effort Settings: Balancing Accuracy vs. Total Task Cost

This is the section most comparisons skip, and it’s the one that decides your real bill.

Both models can “think” before answering. That thinking costs tokens, and you pay for them even though you never see them. Haiku gives you dials for it through its Claude haiku 5.5 adjustable effort settings: Low, Medium, High and Max. That’s the haiku 5 5 thinking effort latency tradeoff in one switch. More thinking means better answers, but slower and pricier ones.

And here’s the problem. Haiku thinks a lot.

In Artificial Analysis’s testing, Haiku 5.5 at medium effort wrote about 33,000 output tokens per task. Luna at medium wrote about 11,000. That’s three times as many. Haiku cost around $0.05 per task against $0.02 for Luna, and scored 34 against 30 on the intelligence index. So you paid two and a half times as much for a four-point bump.

That’s the haiku 5 5 hidden reasoning token cost in action. The per-token price looked identical. The per-task price didn’t. Luna was cheaper per task at every matching effort label in those runs. At the lowest setting, one tracker put Luna around $0.0045 per task versus $0.02 for Haiku.

Does that mean Luna wins? Not quite. Look at what you get for the money.

• Haiku at High roughly matches Luna at Max on the intelligence index, but costs more.
• Haiku at Low or Medium is a fast, cheap model that’s a bit smarter than Luna at the same setting.
• Haiku at Max is the only setting where it clearly pulls ahead, and it’s expensive.

So, the smart move is to match effort to the job. Don’t leave it on Max. Use Low for things like tagging, routing and field extraction, where the answer is obvious and the model barely needs to think. Use Medium for summaries and drafting. Save High or Max for the hard stuff: multi-step coding, long reasoning chains, or computer-use agents where one mistake derails everything.

A practical way to do it: send every request to Haiku at Low first. If it’s unsure or fails a check, retry at High. You’ll spend almost nothing on the easy 80% and only pay for thinking when it earns its keep. This idea works for small models for agentic workflows in general, where a cheap model does most of the grunt work and escalates to something stronger only when it has to.

One more thing on tokens. Luna’s habit of using fewer of them isn’t just good for cost. It also means shorter waits. A model that writes 11,000 tokens finishes sooner than one that writes 33,000, even if the second one streams faster. When you’re comparing speed, look at total time to finish a task, not just tokens per second.

Final Verdict: Which Budget Model Should You Choose for Production?

Okay. Time to commit to something.

Choose Claude Haiku 5.5 if:

• You’re building agents that use software, like clicking through apps, filling forms or managing files. The gap on OSWorld is hard to ignore.
• You need coding help inside an automated workflow. Terminal-Bench says Haiku is far ahead.
• Wrong answers are expensive. Fewer hallucinations is a real feature.
• You care about streaming speed and a quicker first word.
• Your prompts stay under 100K tokens, or you’re willing to chunk them.

Choose GPT-6 Luna if:

• You’re feeding in long documents or running long agent loops that will cross 100K tokens.
• Your tasks are simple and high-volume, so you don’t need the extra intelligence.
• Your bill is the number one thing you watch. Luna’s smaller token appetite and later price jump make it the cheaper option per task in most tests.
• You need a bigger working memory at a stable price.

Skip Haiku if you run big-context jobs all day and can’t restructure them. The 5x step will eat your savings.

Skip Luna if you’re automating anything that has to operate a computer or write real code. It’s behind, and it isn’t close.

And if you can’t decide? Use both. Send simple, long-context and bulk work to Luna. Send anything involving tools, code or tricky judgment to Haiku, with the effort setting turned down by default. That split costs you a bit of extra routing code, but it captures what each model does best.

One last bit of advice. Don’t trust any single table, including mine. Pull 50 to 100 real requests from your own app, run them through both models at two or three effort levels, and measure cost per finished task, not cost per token. It’ll take an afternoon. It could save you five figures a year.

FAQ

Q. Is Claude Haiku 5.5 cheaper than GPT-6 Luna?

A. The base price is the same: $0.10 input and $0.50 output per million tokens. But Luna is cheaper for prompts over 100K tokens, and in most tests it was cheaper per task because it uses fewer reasoning tokens.

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

A. One measurement put Haiku at 155 tokens per second and 3.5 seconds to first token, against 96.7 tokens per second and 4.6 seconds for Luna. Speeds vary by provider and time of day, so test your own setup.

Q. Which is better for coding and computer use?

A. Haiku 5.5, by a wide margin. It scored 72.4% on OSWorld 2.1 (offline subset) and 39.2% on Terminal-Bench 4.0, against 48.9% and 16.4% for Luna.

Q. What’s the Haiku 5.5 100K token price step?

A. Past 100,000 prompt tokens, Haiku moves from $0.10 / $0.50 to $0.50 / $2.50 per million input / output tokens for the whole request. Luna’s increase starts at 272,000 input tokens.

Q. Can I control how much Haiku 5.5 thinks?

A. Yes. It has adjustable effort settings: Low, Medium, High and Max. Lower settings are cheaper and faster. Higher settings are smarter but use far more hidden reasoning tokens.

Pricing and benchmark figures reflect public sources as of October 9, 2026. Both vendors change prices often, so confirm on Anthropic’s and GPT’s official pricing pages before you budget.

How Compare BizTech helps you decide?

Picking an AI model shouldn’t come down to a launch-day price tag. At Compare BizTech, we put tools side by side on the things that change your real bill and your results, like pricing tiers, speed, and where each one falls short. Then we tell you plainly who each tool is right for and who should skip it. Use this guide to narrow your list, test your top two on your own workload, and choose with numbers instead of hype.