GPT-5 nano API pricing

By the aitokensai editors · Verified 2026-11-14 Sources: OpenAI official pricing

The smallest GPT-5 tier. Available at listed at platform rate (no published official USD price).

USD, standard tier. Rate read from the live rate config on 2026-11-14. OpenAI publishes no USD list price for this model, so nothing here is cross-checked against one — this is a gateway rate, not a discount.

Specifications

What GPT-5 nano costs at your volume

Calculated at $0.025 input / $0.2 output per 1M tokens, 8.0× apart on this model. The 5:1 input-to-output assumption in this table is generous, responses longer than that cost more per response than the row suggests, so treat these as floors rather than estimates. Put your own split through the cost calculator before committing a budget.

Cheap in, expensive out: how GPT-5 nano is priced

GPT-5 nano will quote you $0.025 per million input tokens, which reads as very cheap. It is. The trap is the other side of the request: output bills at $0.2, around 8.0×. A task that sends a short instruction and gets back a long answer pays most of its cost at the output rate, and this model stops being cheap there.

Where the shape works in your favour is the reverse: large input, small output. Classification of big documents, extraction from long text, embedding-adjacent pipelines, anything that swallows a lot and answers briefly. That is what $0.025 is genuinely good for.

Where GPT-5 nano sits in the index

GPT-5 nano is 5 of 231 on input rate and 17 of 231 on output rate. A gap of 12 places, because on this model the output rate is 8.0× 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 231 models in this index that cost more than GPT-5 nano.

On an agent mix the lowest blended rate in the same index is Gemma2 9B It at $0.01 per 1M tokens, about 15.6× less than this model at the same ratio. Across the whole index, sorting by input price alone moves a model by as much as 77 places; on GPT-5 nano specifically the shift is 12 — which is what the re-ranked index exists to show.

GPT-5 nano is 5 of 231 across every model in the index; the comparison that usually settles a choice is OpenAI's own lineup further down.

GPT-5 nano among OpenAI's 12 models

GPT-5 nano is the cheapest of OpenAI's 12 models in this index at $0.025 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 every other model in the group costs more on input than this one.

On input rate alone, stepping up to GPT-6 Luna costs 2.0× the input rate. Both measured at 2026-11-14, 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 nano in blended rate, not interchangeable substitutes for it.

Calling GPT-5 nano

The identifier you send is gpt-5-nano. 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 nano'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-nano",
    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 nano output costs 8.0× 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 nano 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

Where GPT-5 nano bills from: the output half

Quoting a monthly token figure tells you very little about GPT-5 nano. Output here costs 8.0× what input does, above the index median of 4.0× across 231 models, so two deployments moving the same number of tokens can differ by several times depending purely on how much the model writes back. Generation is the bill; everything else is rounding. The five shapes below are the ones GPT-5 nano actually gets used for, priced out at each one's real ratio.

This is the lowest blended rate in the index at $0.025 / $0.2 per 1M. Even so it is only a rate floor. Whether it can do the work is a separate question from what it costs.

The spread between the first and last row above is not caused by doing more work — it is caused by asking GPT-5 nano to write more. At 8.0×, the cheapest intervention available to you is shortening responses: a tighter system prompt, a lower max_tokens, a shorter answer format. Optimising the input side barely registers against your $0.2 per 1M output rate.

FAQ

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