feat: add OpenAI GPT-image models to image blocks

Add gpt-image-1, gpt-image-1.5, gpt-image-2, and gpt-image-1-mini
as model options in:
- AIImageGeneratorBlock
- AIImageEditorBlock
- AIImageCustomizerBlock

Changes:
- Expand model enums in all three blocks
- Update credentials to accept Union[Replicate, OpenAI]
- Add OpenAI API branches using images.generate and images.edit
- Add block pricing (8-20 credits) consistent with existing tiers
- Rename GeminiImageModel -> ImageCustomizerModel (backwards compatible)
- Rename FluxKontextModelName -> ImageEditorModel (backwards compatible alias)

autogpt_platform/backend/backend/blocks/ai_image_customizer.py

@@ -1,7 +1,8 @@
 import asyncio
 from enum import Enum
-from typing import Literal
+from typing import Literal, Union
 
+import openai
 from pydantic import SecretStr
 from replicate.client import Client as ReplicateClient
 from replicate.helpers import FileOutput
@@ -24,10 +25,20 @@ from backend.integrations.providers import ProviderName
 from backend.util.file import MediaFileType, store_media_file
 
 
-class GeminiImageModel(str, Enum):
+class ImageCustomizerModel(str, Enum):
+    """Models for the AI Image Customizer block, supporting both Replicate and OpenAI."""
+
     NANO_BANANA = "google/nano-banana"
     NANO_BANANA_PRO = "google/nano-banana-pro"
     NANO_BANANA_2 = "google/nano-banana-2"
+    GPT_IMAGE_1 = "gpt-image-1"
+    GPT_IMAGE_1_5 = "gpt-image-1-5"
+    GPT_IMAGE_2 = "gpt-image-2"
+    GPT_IMAGE_1_MINI = "gpt-image-1-mini"
+
+
+# Keep old name as alias for backwards compatibility
+GeminiImageModel = ImageCustomizerModel
 
 
 class AspectRatio(str, Enum):
@@ -49,6 +60,21 @@ class OutputFormat(str, Enum):
     PNG = "png"
 
 
+ASPECT_TO_OPENAI_SIZE = {
+    AspectRatio.MATCH_INPUT_IMAGE: "auto",
+    AspectRatio.ASPECT_1_1: "1024x1024",
+    AspectRatio.ASPECT_2_3: "1024x1536",
+    AspectRatio.ASPECT_3_2: "1536x1024",
+    AspectRatio.ASPECT_3_4: "1024x1536",
+    AspectRatio.ASPECT_4_3: "1536x1024",
+    AspectRatio.ASPECT_4_5: "1024x1536",
+    AspectRatio.ASPECT_5_4: "1536x1024",
+    AspectRatio.ASPECT_9_16: "1024x1536",
+    AspectRatio.ASPECT_16_9: "1536x1024",
+    AspectRatio.ASPECT_21_9: "1536x1024",
+}
+
+
 TEST_CREDENTIALS = APIKeyCredentials(
     id="01234567-89ab-cdef-0123-456789abcdef",
     provider="replicate",
@@ -68,17 +94,18 @@ TEST_CREDENTIALS_INPUT = {
 class AIImageCustomizerBlock(Block):
     class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput[
-            Literal[ProviderName.REPLICATE], Literal["api_key"]
+            Union[Literal[ProviderName.REPLICATE], Literal[ProviderName.OPENAI]],
+            Literal["api_key"],
         ] = CredentialsField(
-            description="Replicate API key with permissions for Google Gemini image models",
+            description="Replicate or OpenAI API key with permissions for image generation and editing models",
         )
         prompt: str = SchemaField(
             description="A text description of the image you want to generate",
             title="Prompt",
         )
-        model: GeminiImageModel = SchemaField(
+        model: ImageCustomizerModel = SchemaField(
             description="The AI model to use for image generation and editing",
-            default=GeminiImageModel.NANO_BANANA_2,
+            default=ImageCustomizerModel.NANO_BANANA_2,
             title="Model",
         )
         images: list[MediaFileType] = SchemaField(
@@ -104,15 +131,16 @@ class AIImageCustomizerBlock(Block):
         super().__init__(
             id="d76bbe4c-930e-4894-8469-b66775511f71",
             description=(
-                "Generate and edit custom images using Google's Nano-Banana models from Gemini. "
-                "Provide a prompt and optional reference images to create or modify images."
+                "Generate and edit custom images using Google's Nano-Banana models from Gemini "
+                "or OpenAI GPT-image models. Provide a prompt and optional reference images to "
+                "create or modify images."
             ),
             categories={BlockCategory.AI, BlockCategory.MULTIMEDIA},
             input_schema=AIImageCustomizerBlock.Input,
             output_schema=AIImageCustomizerBlock.Output,
             test_input={
                 "prompt": "Make the scene more vibrant and colorful",
-                "model": GeminiImageModel.NANO_BANANA_2,
+                "model": ImageCustomizerModel.NANO_BANANA_2,
                 "images": [],
                 "aspect_ratio": AspectRatio.MATCH_INPUT_IMAGE,
                 "output_format": OutputFormat.JPG,
@@ -171,6 +199,43 @@ class AIImageCustomizerBlock(Block):
         except Exception as e:
             yield "error", str(e)
 
+    async def _customize_with_openai(
+        self,
+        api_key: SecretStr,
+        model_name: str,
+        prompt: str,
+        images: list[MediaFileType],
+        aspect_ratio: str,
+        output_format: str,
+    ) -> MediaFileType:
+        client = openai.AsyncOpenAI(api_key=api_key.get_secret_value())
+        from io import BytesIO
+        import base64
+
+        image_bytes = None
+        if images:
+            header, encoded = str(images[0]).split(",", 1)
+            image_bytes = BytesIO(base64.b64decode(encoded))
+
+        size = ASPECT_TO_OPENAI_SIZE.get(aspect_ratio, "auto")
+
+        kwargs = {
+            "model": model_name,
+            "prompt": prompt,
+            "n": 1,
+            "size": size,  # type: ignore[arg-type]
+            "quality": "auto",
+        }
+        if image_bytes:
+            kwargs["image"] = image_bytes
+
+        response = await client.images.generate(**kwargs)
+        if response.data and response.data[0].url:
+            return MediaFileType(response.data[0].url)
+        if response.data and response.data[0].b64_json:
+            return MediaFileType(f"data:image/png;base64,{response.data[0].b64_json}")
+        raise ValueError("OpenAI image customization returned empty result")
+
     async def run_model(
         self,
         api_key: SecretStr,
@@ -180,6 +245,17 @@ class AIImageCustomizerBlock(Block):
         aspect_ratio: str,
         output_format: str,
     ) -> MediaFileType:
+        # Route to OpenAI for GPT-image models
+        if model_name.startswith("gpt-image"):
+            if len(images) > 1:
+                raise ValueError(
+                    "OpenAI image models support only a single reference image. "
+                    "Please provide one image or use a Replicate model."
+                )
+            return await self._customize_with_openai(
+                api_key, model_name, prompt, images, aspect_ratio, output_format
+            )
+
         client = ReplicateClient(api_token=api_key.get_secret_value())
 
         input_params: dict = {

autogpt_platform/backend/backend/blocks/ai_image_generator_block.py

@@ -1,6 +1,7 @@
 from enum import Enum
-from typing import Literal
+from typing import Literal, Union
 
+import openai
 from pydantic import SecretStr
 from replicate.client import Client as ReplicateClient
 from replicate.helpers import FileOutput
@@ -76,6 +77,14 @@ SIZE_TO_NANO_BANANA_RATIO = {
     ImageSize.TALL: "9:16",
 }
 
+SIZE_TO_OPENAI = {
+    ImageSize.SQUARE: "1024x1024",
+    ImageSize.LANDSCAPE: "1536x1024",
+    ImageSize.PORTRAIT: "1024x1536",
+    ImageSize.WIDE: "1536x1024",
+    ImageSize.TALL: "1024x1536",
+}
+
 
 class ImageStyle(str, Enum):
     """
@@ -107,7 +116,7 @@ class ImageStyle(str, Enum):
 
 class ImageGenModel(str, Enum):
     """
-    Available model providers
+    Available model providers including OpenAI GPT-image family
     """
 
     FLUX = "Flux 1.1 Pro"
@@ -116,14 +125,19 @@ class ImageGenModel(str, Enum):
     SD3_5 = "Stable Diffusion 3.5 Medium"
     NANO_BANANA_PRO = "Nano Banana Pro"
     NANO_BANANA_2 = "Nano Banana 2"
+    GPT_IMAGE_1 = "gpt-image-1"
+    GPT_IMAGE_1_5 = "gpt-image-1-5"
+    GPT_IMAGE_2 = "gpt-image-2"
+    GPT_IMAGE_1_MINI = "gpt-image-1-mini"
 
 
 class AIImageGeneratorBlock(Block):
     class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput[
-            Literal[ProviderName.REPLICATE], Literal["api_key"]
+            Union[Literal[ProviderName.REPLICATE], Literal[ProviderName.OPENAI]],
+            Literal["api_key"],
         ] = CredentialsField(
-            description="Enter your Replicate API key to access the image generation API. You can obtain an API key from https://replicate.com/account/api-tokens.",
+            description="Enter your Replicate or OpenAI API key to access the image generation API.",
         )
         prompt: str = SchemaField(
             description="Text prompt for image generation",
@@ -182,7 +196,10 @@ class AIImageGeneratorBlock(Block):
                 # Return a data URI directly so store_media_file doesn't need to download
                 "_run_client": lambda *args, **kwargs: (
                     "data:image/webp;base64,UklGRiQAAABXRUJQVlA4IBgAAAAwAQCdASoBAAEAAQAcJYgCdAEO"
-                )
+                ),
+                "_generate_with_openai": lambda *args, **kwargs: (
+                    "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mP8z8BQDwAEhQGAhKmMIQAAAABJRU5ErkJggg=="
+                ),
             },
         )
 
@@ -216,8 +233,34 @@ class AIImageGeneratorBlock(Block):
         except Exception as e:
             raise RuntimeError(f"Unexpected error during model execution: {e}")
 
+    async def _generate_with_openai(
+        self, input_data: Input, credentials: APIKeyCredentials
+    ) -> str:
+        client = openai.AsyncOpenAI(
+            api_key=credentials.api_key.get_secret_value()
+        )
+
+        size = SIZE_TO_OPENAI.get(input_data.size, "1024x1024")
+
+        response = await client.images.generate(
+            model=input_data.model.value,
+            prompt=input_data.prompt,
+            n=1,
+            size=size,  # type: ignore[arg-type]
+            quality="auto",
+        )
+        if response.data and response.data[0].url:
+            return response.data[0].url
+        if response.data and response.data[0].b64_json:
+            return f"data:image/png;base64,{response.data[0].b64_json}"
+        raise RuntimeError("OpenAI image generation returned empty result")
+
     async def generate_image(self, input_data: Input, credentials: APIKeyCredentials):
         try:
+            # Route to OpenAI for GPT-image models
+            if input_data.model.value.startswith("gpt-image"):
+                return await self._generate_with_openai(input_data, credentials)
+
             # Handle style-based prompt modification for models without native style support
             modified_prompt = input_data.prompt
             if input_data.model not in [ImageGenModel.RECRAFT]:

autogpt_platform/backend/backend/blocks/flux_kontext.py

@@ -1,6 +1,7 @@
 from enum import Enum
-from typing import Literal, Optional
+from typing import Literal, Optional, Union
 
+import openai
 from pydantic import SecretStr
 from replicate.client import Client as ReplicateClient
 from replicate.helpers import FileOutput
@@ -43,6 +44,10 @@ class ImageEditorModel(str, Enum):
     FLUX_KONTEXT_MAX = "Flux Kontext Max"
     NANO_BANANA_PRO = "Nano Banana Pro"
     NANO_BANANA_2 = "Nano Banana 2"
+    GPT_IMAGE_1 = "gpt-image-1"
+    GPT_IMAGE_1_5 = "gpt-image-1-5"
+    GPT_IMAGE_2 = "gpt-image-2"
+    GPT_IMAGE_1_MINI = "gpt-image-1-mini"
 
     @property
     def api_name(self) -> str:
@@ -76,12 +81,30 @@ class AspectRatio(str, Enum):
     ASPECT_1_2 = "1:2"
 
 
+ASPECT_TO_OPENAI_SIZE = {
+    AspectRatio.ASPECT_1_1: "1024x1024",
+    AspectRatio.ASPECT_16_9: "1536x1024",
+    AspectRatio.ASPECT_9_16: "1024x1536",
+    AspectRatio.ASPECT_4_3: "1536x1024",
+    AspectRatio.ASPECT_3_4: "1024x1536",
+    AspectRatio.ASPECT_3_2: "1536x1024",
+    AspectRatio.ASPECT_2_3: "1024x1536",
+    AspectRatio.ASPECT_4_5: "1024x1536",
+    AspectRatio.ASPECT_5_4: "1536x1024",
+    AspectRatio.ASPECT_21_9: "1536x1024",
+    AspectRatio.ASPECT_9_21: "1024x1536",
+    AspectRatio.ASPECT_2_1: "1536x1024",
+    AspectRatio.ASPECT_1_2: "1024x1536",
+}
+
+
 class AIImageEditorBlock(Block):
     class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput[
-            Literal[ProviderName.REPLICATE], Literal["api_key"]
+            Union[Literal[ProviderName.REPLICATE], Literal[ProviderName.OPENAI]],
+            Literal["api_key"],
         ] = CredentialsField(
-            description="Replicate API key with permissions for Flux Kontext and Nano Banana models",
+            description="Replicate or OpenAI API key with permissions for image editing models",
         )
         prompt: str = SchemaField(
             description="Text instruction describing the desired edit",
@@ -99,7 +122,7 @@ class AIImageEditorBlock(Block):
             advanced=False,
         )
         seed: Optional[int] = SchemaField(
-            description="Random seed. Set for reproducible generation (Flux Kontext only; ignored by Nano Banana models)",
+            description="Random seed. Set for reproducible generation (Flux Kontext only; ignored by other models)",
             default=None,
             title="Seed",
             advanced=True,
@@ -119,8 +142,8 @@ class AIImageEditorBlock(Block):
         super().__init__(
             id="3fd9c73d-4370-4925-a1ff-1b86b99fabfa",
             description=(
-                "Edit images using Flux Kontext or Google Nano Banana models. Provide a prompt "
-                "and optional reference image to generate a modified image."
+                "Edit images using Flux Kontext, Google Nano Banana, or OpenAI GPT-image models. "
+                "Provide a prompt and optional reference image to generate a modified image."
             ),
             categories={BlockCategory.AI, BlockCategory.MULTIMEDIA},
             input_schema=AIImageEditorBlock.Input,
@@ -140,7 +163,7 @@ class AIImageEditorBlock(Block):
             test_mock={
                 # Use data URI to avoid HTTP requests during tests
                 "run_model": lambda *args, **kwargs: (
-                    "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mNk+M9QDwADhgGAWjR9awAAAABJRU5ErkJggg=="
+                    "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mNk+M9QDwADhgGAhKmMIQAAAABJRU5ErkJggg=="
                 ),
             },
             test_credentials=TEST_CREDENTIALS,
@@ -180,6 +203,39 @@ class AIImageEditorBlock(Block):
         )
         yield "output_image", stored_url
 
+    async def _edit_with_openai(
+        self,
+        api_key: SecretStr,
+        model: ImageEditorModel,
+        prompt: str,
+        input_image_b64: Optional[str],
+        aspect_ratio: str,
+    ) -> MediaFileType:
+        if not input_image_b64:
+            raise ValueError("OpenAI image editing requires an input image.")
+
+        client = openai.AsyncOpenAI(api_key=api_key.get_secret_value())
+        from io import BytesIO
+        import base64
+
+        header, encoded = str(input_image_b64).split(",", 1)
+        image_bytes = BytesIO(base64.b64decode(encoded))
+
+        size = ASPECT_TO_OPENAI_SIZE.get(aspect_ratio, "1024x1024")
+
+        response = await client.images.edit(
+            model=model.value,
+            image=image_bytes,
+            prompt=prompt,
+            n=1,
+            size=size,  # type: ignore[arg-type]
+        )
+        if response.data and response.data[0].url:
+            return MediaFileType(response.data[0].url)
+        if response.data and response.data[0].b64_json:
+            return MediaFileType(f"data:image/png;base64,{response.data[0].b64_json}")
+        raise ValueError("OpenAI image edit returned empty result")
+
     async def run_model(
         self,
         api_key: SecretStr,
@@ -191,6 +247,12 @@ class AIImageEditorBlock(Block):
         user_id: str,
         graph_exec_id: str,
     ) -> MediaFileType:
+        # Route to OpenAI for GPT-image models
+        if model.value.startswith("gpt-image"):
+            return await self._edit_with_openai(
+                api_key, model, prompt, input_image_b64, aspect_ratio
+            )
+
         client = ReplicateClient(api_token=api_key.get_secret_value())
         model_name = model.api_name
 

autogpt_platform/backend/backend/data/block_cost_config.py

@@ -1,7 +1,7 @@
 from typing import Type
 
 from backend.blocks._base import Block, BlockCost, BlockCostType
-from backend.blocks.ai_image_customizer import AIImageCustomizerBlock, GeminiImageModel
+from backend.blocks.ai_image_customizer import AIImageCustomizerBlock, ImageCustomizerModel
 from backend.blocks.ai_image_generator_block import AIImageGeneratorBlock, ImageGenModel
 from backend.blocks.ai_music_generator import AIMusicGeneratorBlock
 from backend.blocks.ai_shortform_video_block import (
@@ -20,7 +20,7 @@ from backend.blocks.enrichlayer.linkedin import (
     LinkedinPersonLookupBlock,
     LinkedinRoleLookupBlock,
 )
-from backend.blocks.flux_kontext import AIImageEditorBlock, FluxKontextModelName
+from backend.blocks.flux_kontext import AIImageEditorBlock, ImageEditorModel
 from backend.blocks.ideogram import IdeogramModelBlock
 from backend.blocks.jina.embeddings import JinaEmbeddingBlock
 from backend.blocks.jina.fact_checker import FactCheckerBlock
@@ -477,7 +477,7 @@ BLOCK_COSTS: dict[Type[Block], list[BlockCost]] = {
         BlockCost(
             cost_amount=10,
             cost_filter={
-                "model": FluxKontextModelName.FLUX_KONTEXT_PRO,
+                "model": ImageEditorModel.FLUX_KONTEXT_PRO,
                 "credentials": {
                     "id": replicate_credentials.id,
                     "provider": replicate_credentials.provider,
@@ -488,7 +488,7 @@ BLOCK_COSTS: dict[Type[Block], list[BlockCost]] = {
         BlockCost(
             cost_amount=20,
             cost_filter={
-                "model": FluxKontextModelName.FLUX_KONTEXT_MAX,
+                "model": ImageEditorModel.FLUX_KONTEXT_MAX,
                 "credentials": {
                     "id": replicate_credentials.id,
                     "provider": replicate_credentials.provider,
@@ -499,7 +499,7 @@ BLOCK_COSTS: dict[Type[Block], list[BlockCost]] = {
         BlockCost(
             cost_amount=14,  # Nano Banana Pro
             cost_filter={
-                "model": FluxKontextModelName.NANO_BANANA_PRO,
+                "model": ImageEditorModel.NANO_BANANA_PRO,
                 "credentials": {
                     "id": replicate_credentials.id,
                     "provider": replicate_credentials.provider,
@@ -510,7 +510,7 @@ BLOCK_COSTS: dict[Type[Block], list[BlockCost]] = {
         BlockCost(
             cost_amount=14,  # Nano Banana 2
             cost_filter={
-                "model": FluxKontextModelName.NANO_BANANA_2,
+                "model": ImageEditorModel.NANO_BANANA_2,
                 "credentials": {
                     "id": replicate_credentials.id,
                     "provider": replicate_credentials.provider,
@@ -518,6 +518,50 @@ BLOCK_COSTS: dict[Type[Block], list[BlockCost]] = {
                 },
             },
         ),
+        BlockCost(
+            cost_amount=15,  # gpt-image-1
+            cost_filter={
+                "model": ImageEditorModel.GPT_IMAGE_1,
+                "credentials": {
+                    "id": openai_credentials.id,
+                    "provider": openai_credentials.provider,
+                    "type": openai_credentials.type,
+                },
+            },
+        ),
+        BlockCost(
+            cost_amount=18,  # gpt-image-1-5
+            cost_filter={
+                "model": ImageEditorModel.GPT_IMAGE_1_5,
+                "credentials": {
+                    "id": openai_credentials.id,
+                    "provider": openai_credentials.provider,
+                    "type": openai_credentials.type,
+                },
+            },
+        ),
+        BlockCost(
+            cost_amount=20,  # gpt-image-2
+            cost_filter={
+                "model": ImageEditorModel.GPT_IMAGE_2,
+                "credentials": {
+                    "id": openai_credentials.id,
+                    "provider": openai_credentials.provider,
+                    "type": openai_credentials.type,
+                },
+            },
+        ),
+        BlockCost(
+            cost_amount=8,  # gpt-image-1-mini
+            cost_filter={
+                "model": ImageEditorModel.GPT_IMAGE_1_MINI,
+                "credentials": {
+                    "id": openai_credentials.id,
+                    "provider": openai_credentials.provider,
+                    "type": openai_credentials.type,
+                },
+            },
+        ),
     ],
     AIMusicGeneratorBlock: [
         BlockCost(
@@ -718,12 +762,56 @@ BLOCK_COSTS: dict[Type[Block], list[BlockCost]] = {
                 },
             },
         ),
+        BlockCost(
+            cost_amount=15,  # gpt-image-1
+            cost_filter={
+                "model": ImageGenModel.GPT_IMAGE_1,
+                "credentials": {
+                    "id": openai_credentials.id,
+                    "provider": openai_credentials.provider,
+                    "type": openai_credentials.type,
+                },
+            },
+        ),
+        BlockCost(
+            cost_amount=18,  # gpt-image-1-5
+            cost_filter={
+                "model": ImageGenModel.GPT_IMAGE_1_5,
+                "credentials": {
+                    "id": openai_credentials.id,
+                    "provider": openai_credentials.provider,
+                    "type": openai_credentials.type,
+                },
+            },
+        ),
+        BlockCost(
+            cost_amount=20,  # gpt-image-2
+            cost_filter={
+                "model": ImageGenModel.GPT_IMAGE_2,
+                "credentials": {
+                    "id": openai_credentials.id,
+                    "provider": openai_credentials.provider,
+                    "type": openai_credentials.type,
+                },
+            },
+        ),
+        BlockCost(
+            cost_amount=8,  # gpt-image-1-mini
+            cost_filter={
+                "model": ImageGenModel.GPT_IMAGE_1_MINI,
+                "credentials": {
+                    "id": openai_credentials.id,
+                    "provider": openai_credentials.provider,
+                    "type": openai_credentials.type,
+                },
+            },
+        ),
     ],
     AIImageCustomizerBlock: [
         BlockCost(
             cost_amount=10,  # Nano Banana (original)
             cost_filter={
-                "model": GeminiImageModel.NANO_BANANA,
+                "model": ImageCustomizerModel.NANO_BANANA,
                 "credentials": {
                     "id": replicate_credentials.id,
                     "provider": replicate_credentials.provider,
@@ -734,7 +822,7 @@ BLOCK_COSTS: dict[Type[Block], list[BlockCost]] = {
         BlockCost(
             cost_amount=14,  # Nano Banana Pro: $0.14 per image at 2K
             cost_filter={
-                "model": GeminiImageModel.NANO_BANANA_PRO,
+                "model": ImageCustomizerModel.NANO_BANANA_PRO,
                 "credentials": {
                     "id": replicate_credentials.id,
                     "provider": replicate_credentials.provider,
@@ -745,7 +833,7 @@ BLOCK_COSTS: dict[Type[Block], list[BlockCost]] = {
         BlockCost(
             cost_amount=14,  # Nano Banana 2: same pricing tier as Pro
             cost_filter={
-                "model": GeminiImageModel.NANO_BANANA_2,
+                "model": ImageCustomizerModel.NANO_BANANA_2,
                 "credentials": {
                     "id": replicate_credentials.id,
                     "provider": replicate_credentials.provider,
@@ -753,6 +841,50 @@ BLOCK_COSTS: dict[Type[Block], list[BlockCost]] = {
                 },
             },
         ),
+        BlockCost(
+            cost_amount=15,  # gpt-image-1
+            cost_filter={
+                "model": ImageCustomizerModel.GPT_IMAGE_1,
+                "credentials": {
+                    "id": openai_credentials.id,
+                    "provider": openai_credentials.provider,
+                    "type": openai_credentials.type,
+                },
+            },
+        ),
+        BlockCost(
+            cost_amount=18,  # gpt-image-1-5
+            cost_filter={
+                "model": ImageCustomizerModel.GPT_IMAGE_1_5,
+                "credentials": {
+                    "id": openai_credentials.id,
+                    "provider": openai_credentials.provider,
+                    "type": openai_credentials.type,
+                },
+            },
+        ),
+        BlockCost(
+            cost_amount=20,  # gpt-image-2
+            cost_filter={
+                "model": ImageCustomizerModel.GPT_IMAGE_2,
+                "credentials": {
+                    "id": openai_credentials.id,
+                    "provider": openai_credentials.provider,
+                    "type": openai_credentials.type,
+                },
+            },
+        ),
+        BlockCost(
+            cost_amount=8,  # gpt-image-1-mini
+            cost_filter={
+                "model": ImageCustomizerModel.GPT_IMAGE_1_MINI,
+                "credentials": {
+                    "id": openai_credentials.id,
+                    "provider": openai_credentials.provider,
+                    "type": openai_credentials.type,
+                },
+            },
+        ),
     ],
     PerplexityBlock: [
         # Sonar Deep Research: up to $5/1K searches + $8/1M reasoning tokens.