ReMyndclaude haiku 5.5 vs gpt-6 luna: which cheap ai model should you use in 2026?
as of october 2026, the short answer to claude haiku 5.5 vs gpt-6 luna is this: for short prompts they cost the same per token, and haiku 5.5 wins on anthropic's benchmarks. for long prompts, luna is the cheaper model.
anthropic launched claude haiku 5.5 on october 7, calling it "the cheapest, fastest, and most capable small model we've ever released." its price for prompts up to 100,000 tokens matches gpt-6 luna's list price to the cent. past that line, haiku costs more.
our verdict: use haiku 5.5 for short, high volume agent work, and keep luna for long documents.
what did anthropic launch on october 7?
claude haiku 5.5 is anthropic's new small model, built for high volume, cost sensitive tasks. anthropic lists summaries, compactions, database queries and classification as the target jobs, and says it pairs well with opus 5.5 and sonnet 5.5 as a subagent on coding work.
three details matter for anyone choosing a cheap model:
- speed: anthropic calls it its fastest model to date at standard speed, though its opus models run faster in fast mode.
- effort: it is the first haiku class model with an adjustable effort setting, so you can trade cost for intelligence.
- availability: it is out now on the claude platform and on amazon web services, google cloud and microsoft azure.
anthropic also says haiku 5.5 costs around 75% less to run than haiku 4.5 on average. that figure already accounts for a tokenizer change, which we come back to below.
how do claude haiku 5.5 and gpt-6 luna prices compare?
claude haiku 5.5 pricing has two tiers, and on short prompts it is identical to luna's. here is each vendor's standard rate per million tokens, from anthropic's pricing docs and openai's luna model page, checked on october 10, 2026.
| per 1m tokens | haiku 5.5, prompts up to 100k | haiku 5.5, prompts over 100k | gpt-6 luna |
|---|---|---|---|
| input | $0.10 | $0.50 | $0.10 |
| cached input | $0.01 | $0.05 | $0.01 |
| cache writes (5 min on haiku) | $0.125 | $0.625 | $0.125 |
| output | $0.50 | $2.50 | $0.50 |
the catch is the threshold. anthropic counts every input token toward the 100,000 line, including cache reads and writes, and a request over it pays the higher prices even when part of its prompt is a cache hit. openai's step comes much later: prompts over 272k input tokens pay 2x the input and cache rates and 1.5x output.
batch work is a draw on short prompts. anthropic lists haiku 5.5 batch at $0.05 in and $0.25 out up to 100,000 tokens, and openai prices luna's batch and flex at half its standard rates.
one more caution. anthropic says haiku 5.5 has an updated tokenizer and uses slightly more tokens per task than haiku 4.5. its docs also note that the same text produces a different token count depending on the model's tokenizer, so identical per token prices do not guarantee identical bills. run your own prompts through both before you commit.
which is better on benchmarks, haiku 5.5 or luna?
on anthropic's published table, haiku 5.5 beats gpt-6 luna on every row where both have a score. treat these as haiku 5.5 benchmarks from the vendor: anthropic ran them, so read them as its numbers until independent tests land.
| benchmark (anthropic's table) | haiku 5.5 | gpt-6 luna |
|---|---|---|
| gdpval-aa v2.1, knowledge work (elo) | 1620 | 1437 |
| aa-briefcase v1.1, knowledge work | 1578 | 1336 |
| osworld 2.1, computer use (offline subset) | 72.4% | 48.9% |
| terminal-bench 4.0, agentic coding | 39.2% | 16.4% |
| frontiercode 1.1 (main), agentic coding | 46.4% | 42.4% |
| chartography, visual reasoning (no tools) | 46.4% | 29.1% |
in our reading the gaps are wide on computer use and terminal work and narrow on frontiercode, which fits anthropic's pitch for browser use and subagents.
anthropic is also candid about the ceiling. it says sonnet 5.5 and opus 5.5 remain better choices for complex agentic coding of the kind terminal-bench measures. for that tier, see our best ai model for coding roundup.
where does gpt-6 luna still win?
in our judgement, luna wins on long inputs and on the processing options openai offers around it. both models take very large prompts: anthropic lists a 1m token window for haiku 5.5, and openai lists 1,050,000 tokens for luna, with 128k max output on both. the difference is what a long prompt costs, as the table above shows.
openai also offers luna on flex processing at the same half price as batch, and on october 6 it released a decisions api in beta with luna, for turning text and images into typed answers. luna's effort setting runs from none to max.
haiku lists the later knowledge cutoff. anthropic's overview gives haiku 5.5 a reliable knowledge cutoff of june 2026, while openai lists may 18, 2026 for luna, which arrived in the api on september 22. our context window comparison covers what those big windows are good for in practice.
is claude haiku 5.5 worth it if you already pay for claude?
for claude max and team subscribers, anthropic is making it easier to try. alongside the launch, it said it would roll out a monthly api credit this week, usable on any of its models:
- max 5x: $100 in credits per month;
- max 20x: $200 per month;
- team: up to $500, pooled across the team's users.
anthropic also halved sonnet 5.5's cache read price, from $0.20 to $0.10 per million tokens, the same day. in our view, if your agents already run on sonnet, that cut may matter more to your bill than switching to a small model for agents. for the full price grid across vendors, published before haiku 5.5, see our ai model pricing comparison.
which cheap ai model should you pick?
our verdict: default to haiku 5.5 for short agent tasks, and keep luna for anything that reads long documents. pick by your workload:
- subagents, routing and classification with short prompts: haiku 5.5. same price as luna, higher scores on anthropic's table.
- browser use and computer use: haiku 5.5, which scores well ahead of luna on osworld 2.1 in anthropic's table.
- long documents, transcripts or big repos in one prompt: gpt-6 luna, since its base rate holds well past haiku's 100,000 line.
- overnight bulk jobs: either, on batch. luna adds flex if you want the discount without a batch queue.
- hard coding tasks: neither. step up to sonnet 5.5 or a frontier model, and compare them in our claude vs chatgpt vs gemini guide.
our take: the small model price war is now being fought to the cent, so the deciding factor is prompt length, not the headline rate.
keep your context when you switch models
our guess: the cheap ai model you use today will be repriced or replaced within months. the part worth keeping is your own working context, not loyalty to one api.
remynd is a mac app that keeps that context. it captures the focused window, runs ocr locally with apple vision, and keeps the searchable index on your mac. you can exclude apps and sites, and history access is read only.
its agent window runs your own claude code or codex cli against that history.
download remynd for mac and keep your work history yours, whichever small model wins next month.