AI Image API Pricing Comparison 2026: GPT Image 2 vs Nano Banana 2 vs FLUX.2 vs Ideogram 4.0

TL;DR

AI image API pricing is not directly comparable: GPT Image 2 uses image tokens, Nano Banana 2 ties cost to resolution, FLUX.2 charges by megapixel, and Ideogram 4.0 uses per-image tiers. For production teams, the more useful metric is cost per usable image—what you actually spend after retries, image inputs, editing, and other workflow costs.

AI image generation has become cheaper, but comparing API prices has become more complicated.

A GPT Image 2 output may cost as little as a few thousandths of a dollar, while Ideogram charges a flat per-image rate and FLUX.2 prices generation by megapixel. These numbers look easy to compare, but they represent different billing systems and often exclude costs such as reference images, retries, and post-processing.

This guide compares the current pricing of GPT Image 2, Gemini 3.1 Flash Image (Nano Banana 2), FLUX.2, and Ideogram 4.0, then shows how to estimate the cost that matters in production: how much you pay for an image you can actually use.

AI Image API Pricing at a Glance

Prices are provider list prices checked on July 21, 2026. They may not include retries, reference-image inputs, additional tools, storage, or human review.

For live platform rates, see CometAPI pricing.

Why AI Image API Prices Are Hard to Compare

Image APIs do not share one standard billing unit.

Depending on the provider, you may pay for:

1. Image output tokens.
2. Fixed token counts tied to resolution.
3. Megapixels.
4. A flat price per generated image.
5. Image inputs used for editing.
6. Additional tools such as grounding or upscaling.

That means a price such as $0.03 per image cannot be compared directly with $30 per million image tokens.

For a meaningful comparison, normalize three things:

resolution, workflow type, and approval rate.

GPT Image 2 Pricing: Quality and Size Drive Cost

According to OpenAI's image generation guide, GPT Image 2 has the following estimated output costs for common dimensions:

These figures represent output estimates, not necessarily the full request cost.

OpenAI's API pricing documentation lists separate rates for image input, cached image input, image output, and text input.

This matters most in editing workflows. If you send multiple reference or source images, those inputs add to the request cost.

For implementation details and prompting patterns, see the CometAPI GPT Image 2 guide.

Nano Banana 2 Pricing: Resolution Maps to Image Tokens

Google's official model name is Gemini 3.1 Flash Image, commonly known as Nano Banana 2.

According to Google's Gemini API pricing:

For Nano Banana 2, Google maps common resolutions to fixed image-token counts. A 1K output uses 1,120 image tokens, resulting in an effective standard price of about $0.067.

Nano Banana 2 Lite uses the same 1,120-token 1K output at a lower image-output rate, bringing the price to $0.0336.

Grounding with Google Web or Image Search can add another cost layer, so search-dependent workflows should track grounding separately.

For API usage details, see the Nano Banana 2 API guide.

FLUX.2 Pricing: Megapixels Change the Baseline

Black Forest Labs uses megapixel-based pricing for FLUX.2.

According to the official BFL pricing documentation:

The important word is "from."

FLUX.2 costs change with output resolution, and editing can have a different starting price from basic text-to-image generation.

When comparing FLUX.2 with other providers, use the resolution and workflow you actually expect to run rather than the lowest advertised starting price.

See the CometAPI FLUX.2 Pro API guide for implementation details.

Ideogram 4.0 Pricing: Simple Per-Image Rates

Ideogram 4.0 uses straightforward per-image pricing for its main workflows.

According to Ideogram's official API pricing:

Additional operations such as Instructional Edit, Upscale, Describe, and Layerize have separate prices.

For simple generations, ideograms are easy to budget. For a full asset pipeline, include any additional tools used after generation.

Why Midjourney and Self-Hosted Models Need Separate Pricing Logic

Midjourney does not offer a general public API directly to developers, so its official subscription pricing is not directly comparable with the usage-based APIs above. However, developers can access Midjourney workflows through the CometAPI Midjourney API, the MidJourney API simulates Discord button interactions. Unlike typical REST APIs, it works as a state machine where each operation returns new buttons for the next step.

Self-hosted open models follow a different cost model. Their economics depend on GPU infrastructure, utilization, hosting, engineering, and maintenance rather than a simple per-request API fee.

For that reason, this comparison focuses on provider-hosted APIs with transparent usage-based pricing, while Midjourney API access and self-hosted models are better evaluated as separate cost categories.

The Metric That Matters: Cost per Usable Image

The lowest generation price does not always produce the lowest production cost.

A simple metric is:

Cost per usable image = Total variable spend ÷ Approved images

For a generation-only estimate:

Cost per usable image ≈ Listed generation price ÷ Approval rate

These approval rates are illustrative, not model benchmark results.

A cheaper model that requires frequent retries can cost more than a higher-priced model that produces usable results more consistently.

What Else Affects the Real Cost?

When estimating production cost, also consider:

Image Inputs and Editing

Reference images, upscaling, advanced editing, and other processing steps may increase the request cost.

Retries and Failed Requests

Rejected drafts and safety-related failures can increase both cost and latency.

Grounding

Search-grounded workflows may introduce separate query charges.

Human Review

A low API price can be misleading if the output requires significantly more manual correction.

Storage and Delivery

At scale, storage, resizing, format conversion, and CDN delivery also matter.

You do not need to model every cost perfectly. Start with the variables that differ most between the routes you are comparing.

Batch Pricing: Useful When Latency Is Flexible

OpenAI and Google both offer lower Batch rates for some image-generation workloads.

Batch processing works well for:

• overnight catalog generation;
• bulk marketing assets;
• scheduled content pipelines;
• large evaluation runs.

It is less suitable for interactive products where users expect immediate results.

A batch discount is only valuable when the additional latency fits the workflow.

A Practical Image API Routing Strategy

Not every image request needs to go through the same model.

A practical workflow is:

1. Generate early drafts at a lower cost or resolution.
2. Remove weak concepts before final rendering.
3. Route difficult prompt categories to the model that performs best on them.
4. Choose editing models based on edit success rate.
5. Use batch processing for non-interactive workloads.
6. Re-evaluate routes using real approval and retry data.

The best production setup may use different models for drafts, final generation, and editing.

How to Compare Image APIs Yourself

The best comparison uses your own prompts and production requirements rather than public demo results.

You can start with CometAPI's model comparison tool to compare supported models side by side, then run a representative test set through the routes you are considering. Use your CometAPI dashboard to track actual usage and spend during testing.

A useful starting point is:

• 10 product or lifestyle prompts
• 10 ads or posters containing text
• 10 reference-image edits
• 10 complex composition prompts

For each route, record:

Run prompts more than once because image generation is stochastic.

Then calculate:

Approval rate = Approved outputs ÷ Total outputs

Cost per usable image = Total workflow spend ÷ Approved outputs

This gives you a much more useful production benchmark than comparing provider pricing pages alone.

FAQ

What is the cheapest AI image API?

There is no universal cheapest option. GPT Image 2 Low, FLUX.2 [klein], Nano Banana 2 Lite, and Ideogram 4.0 Turbo all offer relatively low starting prices, but production cost depends on resolution, retries, editing, latency, and approval rate.

How much does GPT Image 2 cost per image?

OpenAI's published estimates range from about $0.005–$0.006 for common Low-quality outputs to $0.211 for a High-quality 1024×1024 image. Image and text inputs can add to the total request cost.

How much does Nano Banana 2 cost?

Gemini 3.1 Flash Image, also known as Nano Banana 2, costs approximately $0.067 for a standard 1K image. Nano Banana 2 Lite costs $0.0336 for a 1K image.

How much does FLUX.2 cost?

FLUX.2 uses megapixel-based pricing. Current text-to-image starting prices range from $0.014 for FLUX.2 [klein] 4B to $0.07 for FLUX.2 [max], depending on the model and resolution.

What is the best image generation API for production?

The best option is the route that delivers the lowest cost per usable image while meeting your requirements for quality, latency, resolution, text accuracy, and editing consistency.

Test Multiple Image Models With the Same Workload

CometAPI provides access to multiple image models through a unified API platform, making it easier to compare different routes with the same prompts and evaluation process.

Start with the current CometAPI pricing, then track:

• request cost;
• resolution;
• latency;
• retries;
• image inputs;
• approved outputs.

You may find that one model works best for low-cost drafts, another for final assets, and another for reference-heavy editing.

The goal is not to choose one permanent winner, but to use the right route for each workload.