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feat(api-nodes): add Gemini 3.1 Flash Lite model to LLM node (#12803)
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This commit is contained in:
Alexander Piskun 2026-03-06 19:54:27 +02:00 committed by GitHub
parent 3b93d5d571
commit 34e55f0061
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1 changed files with 27 additions and 20 deletions
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47
comfy_api_nodes/nodes_gemini.py
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@ -72,18 +72,6 @@ GEMINI_IMAGE_2_PRICE_BADGE = IO.PriceBadge(
) )
class GeminiModel(str, Enum):
"""
Gemini Model Names allowed by comfy-api
"""
gemini_2_5_pro_preview_05_06 = "gemini-2.5-pro-preview-05-06"
gemini_2_5_flash_preview_04_17 = "gemini-2.5-flash-preview-04-17"
gemini_2_5_pro = "gemini-2.5-pro"
gemini_2_5_flash = "gemini-2.5-flash"
gemini_3_0_pro = "gemini-3-pro-preview"
class GeminiImageModel(str, Enum): class GeminiImageModel(str, Enum):
""" """
Gemini Image Model Names allowed by comfy-api Gemini Image Model Names allowed by comfy-api
@ -237,10 +225,14 @@ def calculate_tokens_price(response: GeminiGenerateContentResponse) -> float | N
input_tokens_price = 0.30 input_tokens_price = 0.30
output_text_tokens_price = 2.50 output_text_tokens_price = 2.50
output_image_tokens_price = 30.0 output_image_tokens_price = 30.0
elif response.modelVersion == "gemini-3-pro-preview": elif response.modelVersion in ("gemini-3-pro-preview", "gemini-3.1-pro-preview"):
input_tokens_price = 2 input_tokens_price = 2
output_text_tokens_price = 12.0 output_text_tokens_price = 12.0
output_image_tokens_price = 0.0 output_image_tokens_price = 0.0
elif response.modelVersion == "gemini-3.1-flash-lite-preview":
input_tokens_price = 0.25
output_text_tokens_price = 1.50
output_image_tokens_price = 0.0
elif response.modelVersion == "gemini-3-pro-image-preview": elif response.modelVersion == "gemini-3-pro-image-preview":
input_tokens_price = 2 input_tokens_price = 2
output_text_tokens_price = 12.0 output_text_tokens_price = 12.0
@ -292,8 +284,16 @@ class GeminiNode(IO.ComfyNode):
), ),
IO.Combo.Input( IO.Combo.Input(
"model", "model",
options=GeminiModel, options=[
default=GeminiModel.gemini_2_5_pro, "gemini-2.5-pro-preview-05-06",
"gemini-2.5-flash-preview-04-17",
"gemini-2.5-pro",
"gemini-2.5-flash",
"gemini-3-pro-preview",
"gemini-3-1-pro",
"gemini-3-1-flash-lite",
],
default="gemini-3-1-pro",
tooltip="The Gemini model to use for generating responses.", tooltip="The Gemini model to use for generating responses.",
), ),
IO.Int.Input( IO.Int.Input(
@ -363,11 +363,16 @@ class GeminiNode(IO.ComfyNode):
"usd": [0.00125, 0.01], "usd": [0.00125, 0.01],
"format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" } "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
} }
: $contains($m, "gemini-3-pro-preview") ? { : ($contains($m, "gemini-3-pro-preview") or $contains($m, "gemini-3-1-pro")) ? {
"type": "list_usd", "type": "list_usd",
"usd": [0.002, 0.012], "usd": [0.002, 0.012],
"format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" } "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
} }
: $contains($m, "gemini-3-1-flash-lite") ? {
"type": "list_usd",
"usd": [0.00025, 0.0015],
"format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
}
: {"type":"text", "text":"Token-based"} : {"type":"text", "text":"Token-based"}
) )
""", """,
@ -436,12 +441,14 @@ class GeminiNode(IO.ComfyNode):
files: list[GeminiPart] | None = None, files: list[GeminiPart] | None = None,
system_prompt: str = "", system_prompt: str = "",
) -> IO.NodeOutput: ) -> IO.NodeOutput:
validate_string(prompt, strip_whitespace=False) if model == "gemini-3-pro-preview":
model = "gemini-3.1-pro-preview" # model "gemini-3-pro-preview" will be soon deprecated by Google
elif model == "gemini-3-1-pro":
model = "gemini-3.1-pro-preview"
elif model == "gemini-3-1-flash-lite":
model = "gemini-3.1-flash-lite-preview"
# Create parts list with text prompt as the first part
parts: list[GeminiPart] = [GeminiPart(text=prompt)] parts: list[GeminiPart] = [GeminiPart(text=prompt)]
# Add other modal parts
if images is not None: if images is not None:
parts.extend(await create_image_parts(cls, images)) parts.extend(await create_image_parts(cls, images))
if audio is not None: if audio is not None: