Abstract
GPT-Image-1 and GPT-Image-2 both use token-based billing, but the two models differ significantly in image input pricing, image output pricing, supported resolutions, per-image cost behavior, and applicable production scenarios.
GPT-Image-1 is the previous-generation image generation model. It offers stable and predictable pricing for 1024×1024 square image generation, especially in Medium and High quality tiers. GPT-Image-2 reduces image input and image output token rates, supports more flexible image sizes, and handles high-fidelity image inputs, making it more suitable for portrait images, landscape images, high-resolution generation, reference-image editing, inpainting, and non-real-time batch workflows.
However, GPT-Image-2 is not cheaper in every scenario. According to OpenAI’s official image generation pricing examples, for 1024×1024 square images, GPT-Image-2 is cheaper in the Low quality tier, but GPT-Image-1 remains more cost-effective for Medium and High quality. By contrast, GPT-Image-2 has a clear cost advantage for 1024×1536 portrait and 1536×1024 landscape outputs.
This article compares the two models based on OpenAI’s official pricing and image generation documentation, covering token unit prices, per-image generation costs, edit workflow billing, batch production simulations, hidden cost factors, and practical model selection rules for developers building image generation pipelines.
1. Core Official Token Pricing Comparison
The following prices are based on OpenAI’s current API pricing documentation. The unit is USD per 1 million tokens.
| Token Category | GPT-Image-1 | GPT-Image-2 | Practical Meaning |
|---|---|---|---|
| Regular Text Input | $5.00 / 1M tokens | $5.00 / 1M tokens | Same baseline text input price |
| Cached Text Input | $1.25 / 1M tokens | $1.25 / 1M tokens | Both apply a 75% discount for cached repeated text |
| Regular Image Input | $10.00 / 1M tokens | $8.00 / 1M tokens | GPT-Image-2 reduces image input cost by 20% |
| Cached Image Input | $2.50 / 1M tokens | $2.00 / 1M tokens | Same discount ratio, lower base price for GPT-Image-2 |
| Image Output | $40.00 / 1M tokens | $30.00 / 1M tokens | GPT-Image-2 reduces output token cost by 25% |
At the token unit level, GPT-Image-2 is cheaper for both image input and image output. This matters most for editing, retouching, reference-image generation, e-commerce product image revision, and batch creative asset variation, where image input and output tokens can account for a large share of total cost.
But final cost cannot be judged only by token unit price. OpenAI’s image generation guide indicates that GPT-Image-2 output token estimation should be calculated differently from earlier GPT Image models. Developers should not directly apply GPT-Image-1’s fixed output token table to GPT-Image-2.
2. Official Per-Image Cost by Resolution and Quality
OpenAI’s image generation guide provides example per-image prices for GPT-Image-2, GPT-Image-1.5, and GPT-Image-1 across common sizes. For developers, this table is often more useful than token unit pricing alone because it reflects real production cost under common output specifications.
| Size / Quality | GPT-Image-1 | GPT-Image-2 | Cheaper Option |
|---|---|---|---|
| 1024×1024 Low | $0.011 | $0.006 | GPT-Image-2 |
| 1024×1024 Medium | $0.042 | $0.053 | GPT-Image-1 |
| 1024×1024 High | $0.167 | $0.211 | GPT-Image-1 |
| 1024×1536 Low | $0.016 | $0.005 | GPT-Image-2 |
| 1024×1536 Medium | $0.063 | $0.041 | GPT-Image-2 |
| 1024×1536 High | $0.250 | $0.165 | GPT-Image-2 |
| 1536×1024 Low | $0.016 | $0.005 | GPT-Image-2 |
| 1536×1024 Medium | $0.063 | $0.041 | GPT-Image-2 |
| 1536×1024 High | $0.250 | $0.165 | GPT-Image-2 |
This data leads to an important conclusion:
GPT-Image-2 is not simply a cheaper replacement for GPT-Image-1. It is a newer model with stronger cost advantages in specific image formats and workflows.
If your workload mainly generates 1024×1024 Low square images, GPT-Image-2 is cheaper. But if you generate large volumes of 1024×1024 Medium or High square images, GPT-Image-1 is still more economical.
On the other hand, if your workload involves portrait images, landscape images, mobile posters, ad banners, product detail images, social media creatives, or presentation visuals, GPT-Image-2 is more cost-effective across all three quality tiers for 1024×1536 and 1536×1024 outputs.
3. Resolution Support and High-Resolution Output
GPT-Image-2 supports more flexible image sizes than GPT-Image-1 and is designed for higher-quality image generation and editing workflows.
Common supported sizes include:
- 1024×1024
- 1536×1024
- 1024×1536
- 2048×2048
- 2048×1152
- 3840×2160
- 2160×3840
The maximum edge length must not exceed 3840 pixels, and outputs above 2560×1440 are considered experimental.
This makes GPT-Image-2 more suitable for:
- high-resolution marketing banners;
- vertical mobile posters;
- e-commerce product detail pages;
- advertising creatives;
- presentation covers and campaign key visuals;
- near-4K experimental outputs;
- SaaS products that want to reduce post-generation cropping or upscaling.
By comparison, GPT-Image-1 remains more suitable for traditional fixed-format image generation pipelines, especially large-scale 1024×1024 square output.
4. Edit, Retouching, and Inpainting Cost Logic
For editing and inpainting workflows, the final cost usually consists of three parts:
- text prompt input tokens;
- image input tokens from uploaded reference images;
- image output tokens from the final generated image.
The general cost formula is:
Total cost = text input cost + reference image input cost + generated image output cost
Assume an edit request contains 1,250 reference image input tokens. The reference image input cost would be:
| Model | Image Input Price | Cost for 1,250 Image Input Tokens |
|---|---|---|
| GPT-Image-1 | $10 / 1M tokens | $0.0125 |
| GPT-Image-2 | $8 / 1M tokens | $0.0100 |
This means GPT-Image-2 has a 20% unit-price advantage for reference image input.
However, the final edit cost still depends on output size and quality. For example, if the output is a 1024×1024 Medium square image, GPT-Image-1’s official per-image output cost is $0.042, while GPT-Image-2’s is $0.053. In that case, GPT-Image-2 has cheaper reference image input, but the overall request may still not be cheaper.
If the output is a 1024×1536 Medium portrait image or a 1536×1024 Medium landscape image, GPT-Image-2’s official per-image cost is $0.041, lower than GPT-Image-1’s $0.063. In these cases, GPT-Image-2’s overall advantage becomes clearer.
Therefore, edit workflows should not evaluate cost only by reference image input price. Output size and quality must be included in the calculation.
5. Batch Production Cost Simulation
The following simulations calculate only official per-image generation cost. They do not include extra text prompt tokens or reference image input tokens. In real projects, developers should also account for prompt length, reference image count, cache hit rate, and API path.
Scenario 1: 10,000 Low-Quality 1024×1024 Square Images
| Model | Unit Price | Total Cost |
|---|---|---|
| GPT-Image-1 | $0.011 | $110 |
| GPT-Image-2 | $0.006 | $60 |
For low-quality square draft images, GPT-Image-2 is clearly cheaper. It is suitable for thumbnails, creative previews, and low-cost visual testing.
Scenario 2: 10,000 Medium-Quality 1024×1024 Square Images
| Model | Unit Price | Total Cost |
|---|---|---|
| GPT-Image-1 | $0.042 | $420 |
| GPT-Image-2 | $0.053 | $530 |
For Medium square generation, GPT-Image-1 is cheaper. If your business produces square social images, product square images, square cards, or square posters in bulk, GPT-Image-1 is still worth keeping.
Scenario 3: 10,000 High-Quality 1024×1024 Square Images
| Model | Unit Price | Total Cost |
|---|---|---|
| GPT-Image-1 | $0.167 | $1,670 |
| GPT-Image-2 | $0.211 | $2,110 |
For High-quality square generation, GPT-Image-1 has a stronger cost advantage. Directly migrating all fixed-spec square image production to GPT-Image-2 may not be cost-effective.
Scenario 4: 10,000 Medium-Quality 1024×1536 Portrait Images
| Model | Unit Price | Total Cost |
|---|---|---|
| GPT-Image-1 | $0.063 | $630 |
| GPT-Image-2 | $0.041 | $410 |
Portrait generation is one of GPT-Image-2’s strongest cost scenarios. It is suitable for short-video covers, mobile posters, e-commerce detail pages, and feed ads.
Scenario 5: 10,000 High-Quality 1536×1024 Landscape Images
| Model | Unit Price | Total Cost |
|---|---|---|
| GPT-Image-1 | $0.250 | $2,500 |
| GPT-Image-2 | $0.165 | $1,650 |
For high-quality landscape visual assets, GPT-Image-2 is significantly cheaper than GPT-Image-1. It is suitable for website banners, campaign hero visuals, landing page headers, and horizontal advertising creatives.
6. Hidden Cost Factors Developers Should Track
6.1 Image API vs Responses API
OpenAI’s image generation guide distinguishes between direct Image API usage and Responses API usage. The Image API is suitable for generating or editing images directly with a selected GPT Image model. The Responses API is more suitable for conversational, multi-step image editing experiences, but these requests may include main model token usage in addition to image generation costs.
If your goal is low-cost single-image or batch image generation, the Image API is usually more direct. If your product requires multi-turn conversational editing, image-context memory, and complex interactive workflows, the Responses API is more flexible, but you need to track the additional main-model token cost.
6.2 Reference Image Input Cost
GPT-Image-2 has a lower image input token price than GPT-Image-1, but it processes image inputs at high fidelity. Therefore, actual reference image token usage should be measured from API usage logs.
For enterprise teams, reference image count, image size, reuse frequency, and cache hit rate can all affect the final bill. This is especially important in product image retouching, brand template revision, local image repainting, and multi-reference composition workflows.
6.3 Batch Pricing
OpenAI’s pricing page lists Batch pricing for GPT-Image-2. Under Batch pricing, image input costs $4 / 1M tokens, cached image input costs $1 / 1M tokens, and image output costs $15 / 1M tokens. Text input costs $2.50 / 1M tokens, and cached text input costs $0.625 / 1M tokens.
This is important for non-real-time workloads such as:
- e-commerce product image drafts;
- large-scale ad creative variations;
- A/B testing image generation;
- batch cover image production for content platforms;
- social media asset pre-generation;
- internal enterprise design draft automation.
If immediate response time is not required, Batch mode can significantly reduce long-term GPT-Image-2 cost.
7. Practical Model Selection Rules
Use GPT-Image-2 when:
- you need portrait or landscape image generation;
- you need flexible sizes or experimental high-resolution output;
- you frequently use reference images, image edits, or inpainting;
- you build image retouching, product image revision, or ad creative tools;
- your workload can use Batch mode to reduce cost;
- you want the newer image model with more flexible size support.
Use GPT-Image-1 when:
- you mainly generate 1024×1024 square images;
- you produce large volumes of Medium or High square images;
- your pipeline depends on fixed specifications and predictable cost;
- you do not need high-resolution or flexible aspect ratios;
- you care more about fixed square image production cost.
Use Both Models When:
- your product supports multiple image formats;
- your workflow includes e-commerce, advertising, or creative SaaS scenarios;
- some tasks generate square thumbnails while others generate vertical posters or horizontal banners;
- you need A/B testing across GPT-Image-1 and GPT-Image-2 for both cost and quality.
In multi-model image generation pipelines, developers should define clear model selection rules inside the business system.
For example:
- 1024×1024 Medium / High square images → GPT-Image-1;
- portrait images, landscape images, reference-image editing, high-resolution output, and Batch tasks → GPT-Image-2.
Treerouter，a unified API access layer can help reduce key management, interface maintenance, and invocation configuration costs when working with multiple models. The actual model selection logic should still be implemented by developers in the business layer.
Conclusion
GPT-Image-2 improves image input and image output token pricing, supports more flexible sizes, enables higher-resolution generation, and handles high-fidelity image inputs. It is especially suitable for portrait images, landscape images, reference-image editing, inpainting, high-resolution output, and non-real-time Batch production.
But GPT-Image-2 is not cheaper for every task. According to OpenAI’s official per-image pricing examples, GPT-Image-1 remains more cost-effective for 1024×1024 Medium and High square generation.
Therefore, the best strategy is not to fully migrate from GPT-Image-1 to GPT-Image-2 blindly. Instead, developers should allocate models based on real business needs:
- fixed-spec Medium / High square generation → GPT-Image-1;
- portrait, landscape, high-resolution, editing, retouching, and non-real-time Batch tasks → GPT-Image-2;
- multi-format image platforms → use both models and define business-side routing rules.
Enterprise image generation systems should first establish clear model selection rules in the business layer, then use unified access methods to reduce multi-model integration and maintenance overhead. This approach helps balance visual quality, generation flexibility, and long-term API cost.
