GPT-5.6 Luna API pricing

By the aitokensai editors · Verified 2026-11-01 Sources: OpenAI official pricing

Lowest-cost tier in the GPT-5.6 family (drafts, routing, high-frequency calls). Available at about 50% below official list.

USD, standard tier. List price cross-checked against OpenAI’s published pricing on 2026-11-01; the discounted route was read from the provider’s live rate config the same day.

Specifications

What GPT-5.6 Luna costs at your volume

Calculated at $0.1 input / $0.6 output per 1M tokens, 6.0× apart on this model, assuming a 5:1 input-to-output ratio. That ratio is the single largest source of error in this table: resend-heavy workloads drift well past it, and at 6.0× the correction is not linear. The cost calculator takes the actual figures.

What GPT-5.6 Luna is for

$0.1 per million input tokens puts GPT-5.6 Luna low enough that volume stops being the first question. This is a model you can point at thousands of calls a day and not think about: summarisation at scale, autocomplete, extraction pipelines, tier-one filtering in front of a stronger model.

Two honest limits. At 6.0×, output is still the majority of the bill, so this is not the cheapest option for genuinely long generation. And at this price the saving from staying cheap is small — escalating a hard case to a flagship costs pennies next to the cost of getting it wrong.

Where GPT-5.6 Luna sits in the index

GPT-5.6 Luna is 35 of 226 on input rate and 53 of 226 on output rate. A gap of 18 places, because on this model the output rate is 6.0× the input rate, steeper than the index median of 4.0× An output-heavy workload therefore leaves it almost where it started, which is why the cheapest model per input token is routinely not the cheapest per task. Percentile is the share of the 226 models in this index that cost more than GPT-5.6 Luna.

On an agent mix the lowest blended rate in the same index is Gemma2 9B It at $0.01 per 1M tokens, about 47.5× less than this model at the same ratio. Across the whole index, sorting by input price alone moves a model by as much as 74 places; on GPT-5.6 Luna specifically the shift is 18 — which is what the re-ranked index exists to show.

GPT-5.6 Luna is 35 of 226 across every model in the index; the comparison that usually settles a choice is OpenAI's own lineup further down.

GPT-5.6 Luna among OpenAI's 12 models

GPT-5.6 Luna is the 3rd cheapest of OpenAI's 12 models in this index at $0.1 per 1M input tokens. The range inside that group runs from GPT-5 nano at $0.025 to GPT-6 Astra at $5 (a spread of 200×, and this one sits at 4).0× the group's cheapest tier.

On input rate alone, stepping up to GPT-5 mini costs 1.2× the input rate and dropping to GPT-6 Luna cuts it by 50%. Both measured at 2026-11-01, the same check as every other rate on this page.

Full OpenAI range, including models outside this shortlist: OpenAI rate index.

Similarly priced alternatives

Blended comparison (input + output per 1M). Price is one axis, these are the three closest to GPT-5.6 Luna in blended rate, not interchangeable substitutes for it.

Calling GPT-5.6 Luna

The identifier you send is gpt-5.6-luna. Copy it exactly, because a mismatch returns a model error rather than a connection error, which sends most people to debug the wrong thing. Everything else is a base URL argument, GPT-5.6 Luna's own SDKs work unchanged.

from openai import OpenAI

client = OpenAI(base_url="https://aicomp.ai/v1", api_key="sk-...")

resp = client.chat.completions.create(
    model="gpt-5.6-luna",
    messages=[
        {"role": "system", "content": "Answer in one short paragraph."},
        {"role": "user", "content": "Explain what changed in this release."},
    ],
    max_tokens=300,
)

# prompt_tokens / completion_tokens 是真实账单的唯一依据 —— 记下来，别猜
print(resp.usage.prompt_tokens, resp.usage.completion_tokens)

Log the usage object on every call, not just while you are optimising. On GPT-5.6 Luna output costs 6.0× what input costs, so the same monthly token count bills very differently depending on how much of it comes back as output — measure your own split before extrapolating from the headline input rate, because rates move with vendor promotions.

Setting it up

Pointing an existing integration at GPT-5.6 Luna is a base URL change, not a rewrite. Pick your tool:

• Claude Code — set the base URL via environment variables
• Cursor — override the API endpoint in settings
• OpenAI / Anthropic SDK — one base_url argument
• Python (sync), streaming and async examples
• Cutting the bill — eight levers ranked by effect

What different usage shapes cost on GPT-5.6 Luna

A monthly token budget does not convert cleanly into a monthly invoice, and GPT-5.6 Luna is an ordinary case rather than an extreme one: output is priced at 6.0× input, above the median of 4.0× across the 226 models in our index. Everything follows from that one number, at 5:1 it is manageable, at 1:1 the same token count costs half as much. Below are five shapes real GPT-5.6 Luna deployments fall into, each costing it out at the split it actually produces.

The cheapest comparison is GPT-5 nano at $0.025 / $0.2 per 1M — 3.1× cheaper than GPT-5.6 Luna on a blended rate. Treat it as a rate floor, not a recommendation: it says nothing about whether GPT-5 nano can do the work you need GPT-5.6 Luna for.

Read the table as a sanity check rather than a forecast. If your own output share sits anywhere near 3%, everything above is priced almost entirely at GPT-5.6 Luna's $0.1 input rate; at 40% the $0.6 output rate has taken the larger half. Nobody's split is exactly the one you assumed when you picked the model, which is why the calculator asks for it before quoting anything.

FAQ

Get GPT-5.6 Luna access