Which LLM is the cheapest for our workload?

The best-for pages rank the live catalogue per task by the index that measures it, and the router picks the cheapest model that meets your quality rule for each request.

How much can we save on our LLM bill?

On the public benchmark the router saved 89% against sending everything to the reference model. Import last month's provider bill on the bill page and the estimate is computed on your own traffic before you change a line of code.

How often do model prices change?

The catalogue is refreshed from the vendors' price lists; each model page shows the date of its data.

Model catalogue

OpenAI

GPT-5.4 Nano

openai/gpt-5.4-nanoCatalogue: Indexed in the reference catalogue; nothing on this deployment answers as this model yet.

JSON

Specification

Published by the vendor; the platform bills at the target's declared price.

GPT-5.4 Nano

reasoning tools

OpenAI openai/gpt-5.4-nano

GPT-5.4 nano is the most lightweight and cost-efficient variant of the GPT-5.4 family, optimized for speed-critical and high-volume tasks. It supports text and image inputs and is designed for low-latency...

Input $/1M: $0.20
Output $/1M: $1.25
Context: 400K
Max output: 128K
Released: Mar 17, 2026
Modalities: fileimagetext

Intelligence index21

Coding index56

Agentic index16

Cached input reads $0.02 per 1M

Alternatives, and where the router puts GPT-5.4 Nano

GPT-5.4 Nano earns a request when it needs step-by-step reasoning, tool calls, very long inputs; a short, general request would go to Gemma 4 26B A4B (free) instead - free per million input tokens against $0.20 here; and the hardest requests, where the quality floor rules it out, to Claude Opus 5.5 (batch). Your rules decide the floor; the trace shows the comparison for every request.

Cheaper alternatives

Lower input price and an intelligence index within 5 points.

Better alternatives

A higher intelligence index, best first, with the price it comes at.

On this deployment

Nothing on this deployment answers as this model yet. Connect your own provider under Model providers, or add a target to your catalogue - the router picks it up without retraining.

Not available here yet

Signed in, add the model under Model providers; self-hosting, add a target with metadata.model set to this id.

targets.yamlyaml

# targets.yaml - declare a target that runs GPT-5.4 Nano
targets:
  - id: gpt-5-4-nano
    kind: llm
    name: GPT-5.4 Nano
    metadata:
      model: openai/gpt-5.4-nano          # links the target to this catalogue entry
    capabilities:
      domains: [general]
      supports_tools: true
      context_window: 400000
    cost:
      usd_per_1k_tokens: 0.000725

Price among OpenAI models

Input list price per million tokens; GPT-5.4 Nano is the 5th cheapest of 13 listed.

More from OpenAI

Other models by the same vendor in the catalogue.

List price is where the decision starts

GPT-5.4 Nano earns a request when it beats the field on your objective, not on the vendor's page.

The specification above is what OpenAI publishes. What the router uses is the price you actually pay here, the latency measured on this deployment and the outcomes people reported - and it compares those against every other model that could take the request. Price a workload on the estimator, or send one live in the playground.

Photo: RDNE Stock project

For the business

What it means for which models you pay for.

Catalogue of every vendor
The right model, not the famous one
Price, context, abilities and benchmark indexes side by side, with the model that fits each task named.
The rankings
89% less
Lower cost per request
On the 150-prompt benchmark the router spent 89% less than sending every prompt to the reference model, and the judged quality went up, not down.
Read the benchmark
Per million tokens, every model
Prices you can compare
The catalogue prints input and output prices and what three typical monthly workloads cost on each.
The model catalogue

Which LLM is the cheapest for our workload?: The best-for pages rank the live catalogue per task by the index that measures it, and the router picks the cheapest model that meets your quality rule for each request.
How much can we save on our LLM bill?: On the public benchmark the router saved 89% against sending everything to the reference model. Import last month's provider bill on the bill page and the estimate is computed on your own traffic before you change a line of code.
How often do model prices change?: The catalogue is refreshed from the vendors' price lists; each model page shows the date of its data.

On this deployment

Nothing on this deployment answers as this model yet. Connect your own provider under Model providers, or add a target to your catalogue - the router picks it up without retraining.

Not available here yet

Signed in, add the model under Model providers; self-hosting, add a target with metadata.model set to this id.

targets.yamlyaml

# targets.yaml - declare a target that runs GPT-5.4 Nano
targets:
  - id: gpt-5-4-nano
    kind: llm
    name: GPT-5.4 Nano
    metadata:
      model: openai/gpt-5.4-nano          # links the target to this catalogue entry
    capabilities:
      domains: [general]
      supports_tools: true
      context_window: 400000
    cost:
      usd_per_1k_tokens: 0.000725

GPT-5.4 Nano earns a request when it beats the field on your objective, not on the vendor's page.

Specification

GPT-5.4 Nano

Alternatives, and where the router puts GPT-5.4 Nano

Cheaper alternatives

Better alternatives

On this deployment

Price among OpenAI models

More from OpenAI

GPT-5.4 Nano earns a request when it beats the field on your objective, not on the vendor's page.

What it means for which models you pay for.

The right model, not the famous one

Lower cost per request

Prices you can compare

Specification

GPT-5.4 Nano

Alternatives, and where the router puts GPT-5.4 Nano

Cheaper alternatives

Better alternatives

On this deployment

Price among OpenAI models

More from OpenAI

GPT-5.4 Nano earns a request when it beats the field on your objective, not on the vendor's page.

What it means for which models you pay for.

The right model, not the famous one

Lower cost per request

Prices you can compare