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feat(model_prices): add gemini-3.1-flash-lite-image

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Register Nano Banana 2 Lite on the unprefixed, gemini/, and vertex_ai/ keys
so completion_cost and pass-through spend tracking no longer treat the
model as unmapped
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Emerson Gomes 2026-08-13 19:18:22 -05:00
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95
litellm/model_prices_and_context_window_backup.json
View file

@ -18141,6 +18141,44 @@
},
"web_search_billing_unit": "per_query"
},
"gemini-3.1-flash-lite-image": {
"cache_read_input_token_cost": 2.5e-08,
"input_cost_per_image": 0.00028,
"input_cost_per_token": 2.5e-07,
"input_cost_per_token_batches": 1.25e-07,
"litellm_provider": "vertex_ai-language-models",
"max_input_tokens": 65536,
"max_output_tokens": 4096,
"max_tokens": 4096,
"mode": "image_generation",
"output_cost_per_image": 0.0336,
"output_cost_per_image_token": 3e-05,
"output_cost_per_token": 1.5e-06,
"output_cost_per_token_batches": 7.5e-07,
"source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing",
"supported_endpoints": [
"/v1/chat/completions",
"/v1/completions",
"/v1/batch"
],
"supported_modalities": [
"text",
"image",
"video"
],
"supported_output_modalities": [
"text",
"image"
],
"supports_function_calling": false,
"supports_pdf_input": true,
"supports_prompt_caching": true,
"supports_reasoning": false,
"supports_response_schema": false,
"supports_system_messages": true,
"supports_video_input": true,
"supports_vision": true
},
"gemini-3.1-flash-lite-preview": {
"cache_read_input_token_cost": 2.5e-08,
"input_cost_per_audio_token": 5e-07,
@ -19949,6 +19987,42 @@
},
"web_search_billing_unit": "per_query"
},
"gemini/gemini-3.1-flash-lite-image": {
"input_cost_per_image": 0.00028,
"input_cost_per_token": 2.5e-07,
"input_cost_per_token_batches": 1.25e-07,
"litellm_provider": "gemini",
"max_input_tokens": 65536,
"max_output_tokens": 4096,
"max_tokens": 4096,
"mode": "image_generation",
"output_cost_per_image": 0.0336,
"output_cost_per_image_token": 3e-05,
"output_cost_per_token": 1.5e-06,
"output_cost_per_token_batches": 7.5e-07,
"rpm": 1000,
"tpm": 4000000,
"source": "https://ai.google.dev/gemini-api/docs/pricing#gemini-3.1-flash-lite-image",
"supported_endpoints": [
"/v1/chat/completions",
"/v1/completions",
"/v1/batch"
],
"supported_modalities": [
"text",
"image"
],
"supported_output_modalities": [
"text",
"image"
],
"supports_function_calling": true,
"supports_prompt_caching": false,
"supports_reasoning": false,
"supports_response_schema": false,
"supports_system_messages": true,
"supports_vision": true
},
"gemini/deep-research-pro-preview-12-2025": {
"input_cost_per_image": 0.0011,
"input_cost_per_token": 2e-06,
@ -38760,6 +38834,27 @@
"supports_reasoning": false,
"source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#gemini-models"
},
"vertex_ai/gemini-3.1-flash-lite-image": {
"cache_read_input_token_cost": 2.5e-08,
"input_cost_per_image": 0.00028,
"input_cost_per_token": 2.5e-07,
"input_cost_per_token_batches": 1.25e-07,
"litellm_provider": "vertex_ai-language-models",
"max_input_tokens": 65536,
"max_output_tokens": 4096,
"max_tokens": 4096,
"mode": "image_generation",
"output_cost_per_image": 0.0336,
"output_cost_per_image_token": 3e-05,
"output_cost_per_token": 1.5e-06,
"output_cost_per_token_batches": 7.5e-07,
"supports_function_calling": false,
"supports_prompt_caching": true,
"supports_reasoning": false,
"supports_response_schema": false,
"supports_vision": true,
"source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing"
},
"vertex_ai/gemini-3.1-flash-lite-preview": {
"cache_read_input_token_cost": 2.5e-08,
"input_cost_per_audio_token": 5e-07,

95
model_prices_and_context_window.json
View file

@ -18141,6 +18141,44 @@
},
"web_search_billing_unit": "per_query"
},
"gemini-3.1-flash-lite-image": {
"cache_read_input_token_cost": 2.5e-08,
"input_cost_per_image": 0.00028,
"input_cost_per_token": 2.5e-07,
"input_cost_per_token_batches": 1.25e-07,
"litellm_provider": "vertex_ai-language-models",
"max_input_tokens": 65536,
"max_output_tokens": 4096,
"max_tokens": 4096,
"mode": "image_generation",
"output_cost_per_image": 0.0336,
"output_cost_per_image_token": 3e-05,
"output_cost_per_token": 1.5e-06,
"output_cost_per_token_batches": 7.5e-07,
"source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing",
"supported_endpoints": [
"/v1/chat/completions",
"/v1/completions",
"/v1/batch"
],
"supported_modalities": [
"text",
"image",
"video"
],
"supported_output_modalities": [
"text",
"image"
],
"supports_function_calling": false,
"supports_pdf_input": true,
"supports_prompt_caching": true,
"supports_reasoning": false,
"supports_response_schema": false,
"supports_system_messages": true,
"supports_video_input": true,
"supports_vision": true
},
"gemini-3.1-flash-lite-preview": {
"cache_read_input_token_cost": 2.5e-08,
"input_cost_per_audio_token": 5e-07,
@ -19949,6 +19987,42 @@
},
"web_search_billing_unit": "per_query"
},
"gemini/gemini-3.1-flash-lite-image": {
"input_cost_per_image": 0.00028,
"input_cost_per_token": 2.5e-07,
"input_cost_per_token_batches": 1.25e-07,
"litellm_provider": "gemini",
"max_input_tokens": 65536,
"max_output_tokens": 4096,
"max_tokens": 4096,
"mode": "image_generation",
"output_cost_per_image": 0.0336,
"output_cost_per_image_token": 3e-05,
"output_cost_per_token": 1.5e-06,
"output_cost_per_token_batches": 7.5e-07,
"rpm": 1000,
"tpm": 4000000,
"source": "https://ai.google.dev/gemini-api/docs/pricing#gemini-3.1-flash-lite-image",
"supported_endpoints": [
"/v1/chat/completions",
"/v1/completions",
"/v1/batch"
],
"supported_modalities": [
"text",
"image"
],
"supported_output_modalities": [
"text",
"image"
],
"supports_function_calling": true,
"supports_prompt_caching": false,
"supports_reasoning": false,
"supports_response_schema": false,
"supports_system_messages": true,
"supports_vision": true
},
"gemini/deep-research-pro-preview-12-2025": {
"input_cost_per_image": 0.0011,
"input_cost_per_token": 2e-06,
@ -38760,6 +38834,27 @@
"supports_reasoning": false,
"source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#gemini-models"
},
"vertex_ai/gemini-3.1-flash-lite-image": {
"cache_read_input_token_cost": 2.5e-08,
"input_cost_per_image": 0.00028,
"input_cost_per_token": 2.5e-07,
"input_cost_per_token_batches": 1.25e-07,
"litellm_provider": "vertex_ai-language-models",
"max_input_tokens": 65536,
"max_output_tokens": 4096,
"max_tokens": 4096,
"mode": "image_generation",
"output_cost_per_image": 0.0336,
"output_cost_per_image_token": 3e-05,
"output_cost_per_token": 1.5e-06,
"output_cost_per_token_batches": 7.5e-07,
"supports_function_calling": false,
"supports_prompt_caching": true,
"supports_reasoning": false,
"supports_response_schema": false,
"supports_vision": true,
"source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing"
},
"vertex_ai/gemini-3.1-flash-lite-preview": {
"cache_read_input_token_cost": 2.5e-08,
"input_cost_per_audio_token": 5e-07,

1
tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py
View file

@ -1546,6 +1546,7 @@ def test_service_tier_fallback_pricing():
[
"gemini-3-pro-image-preview",
"gemini-3.1-flash-image-preview",
"gemini-3.1-flash-lite-image",
],
)
def test_gemini_image_generation_cost_with_zero_text_tokens(model: str):

242
tests/test_litellm/test_gemini_3_1_flash_lite_image_model_metadata.py Normal file
View file

@ -0,0 +1,242 @@
import json
from pathlib import Path
import pytest
import litellm
from litellm import completion_cost
from litellm.cost_calculator import cost_per_token
from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider
from litellm.litellm_core_utils.llm_cost_calc.utils import generic_cost_per_token
from litellm.llms.gemini.image_generation.cost_calculator import (
cost_calculator as gemini_image_generation_cost_calculator,
)
from litellm.llms.vertex_ai.image_generation.cost_calculator import (
cost_calculator as vertex_image_generation_cost_calculator,
)
from litellm.types.utils import (
CompletionTokensDetailsWrapper,
ImageObject,
ImageResponse,
ImageUsage,
ImageUsageInputTokensDetails,
ModelResponse,
PromptTokensDetailsWrapper,
Usage,
)
REPO_ROOT = Path(__file__).parents[2]
MAIN_PATH = REPO_ROOT / "model_prices_and_context_window.json"
BACKUP_PATH = REPO_ROOT / "litellm" / "model_prices_and_context_window_backup.json"
UNPREFIXED = "gemini-3.1-flash-lite-image"
GEMINI = "gemini/gemini-3.1-flash-lite-image"
VERTEX = "vertex_ai/gemini-3.1-flash-lite-image"
ALL_KEYS = (UNPREFIXED, GEMINI, VERTEX)
INPUT_COST = 2.5e-07
INPUT_COST_BATCHES = 1.25e-07
OUTPUT_TEXT_COST = 1.5e-06
OUTPUT_TEXT_COST_BATCHES = 7.5e-07
OUTPUT_IMAGE_TOKEN_COST = 3e-05
OUTPUT_COST_PER_1K_IMAGE = 0.0336
INPUT_COST_PER_IMAGE = 0.00028
CACHE_READ_COST = 2.5e-08
MAX_INPUT_TOKENS = 65536
MAX_OUTPUT_TOKENS = 4096
TOKENS_PER_1K_IMAGE = 1120
def _load(path: Path) -> dict:
with open(path) as f:
return json.load(f)
@pytest.fixture
def local_model_cost_map(monkeypatch):
original_model_cost = litellm.model_cost
monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
litellm.model_cost = litellm.get_model_cost_map(url="")
litellm.get_model_info.cache_clear()
try:
yield
finally:
litellm.model_cost = original_model_cost
litellm.get_model_info.cache_clear()
@pytest.mark.parametrize("model", ALL_KEYS)
def test_gemini_3_1_flash_lite_image_is_registered(model: str):
info = _load(MAIN_PATH).get(model)
assert info is not None, f"{model} not found in model_prices_and_context_window.json"
assert info["mode"] == "image_generation"
assert info["input_cost_per_token"] == INPUT_COST
assert info["input_cost_per_token_batches"] == INPUT_COST_BATCHES
assert info["output_cost_per_token"] == OUTPUT_TEXT_COST
assert info["output_cost_per_token_batches"] == OUTPUT_TEXT_COST_BATCHES
assert info["output_cost_per_image"] == OUTPUT_COST_PER_1K_IMAGE
assert info["output_cost_per_image_token"] == OUTPUT_IMAGE_TOKEN_COST
assert info["max_input_tokens"] == MAX_INPUT_TOKENS
assert info["max_output_tokens"] == MAX_OUTPUT_TOKENS
assert info["max_tokens"] == MAX_OUTPUT_TOKENS
assert info["supports_reasoning"] is False
assert info["supports_response_schema"] is False
assert info["supports_vision"] is True
for field in ("supports_web_search", "search_context_cost_per_query", "web_search_billing_unit"):
assert field not in info
def test_gemini_3_1_flash_lite_image_provider_specific_fields():
cost_map = _load(MAIN_PATH)
unprefixed = cost_map[UNPREFIXED]
assert unprefixed["litellm_provider"] == "vertex_ai-language-models"
assert unprefixed["cache_read_input_token_cost"] == CACHE_READ_COST
assert unprefixed["input_cost_per_image"] == INPUT_COST_PER_IMAGE
assert unprefixed["supports_function_calling"] is False
assert unprefixed["supports_prompt_caching"] is True
assert unprefixed["supports_pdf_input"] is True
assert unprefixed["supports_video_input"] is True
assert unprefixed["supported_modalities"] == ["text", "image", "video"]
gemini = cost_map[GEMINI]
assert gemini["litellm_provider"] == "gemini"
assert gemini["supports_function_calling"] is True
assert gemini["supports_prompt_caching"] is False
assert "cache_read_input_token_cost" not in gemini
assert gemini["supported_modalities"] == ["text", "image"]
assert gemini["supported_output_modalities"] == ["text", "image"]
assert gemini["rpm"] == 1000
assert gemini["tpm"] == 4000000
assert gemini["input_cost_per_image"] == INPUT_COST_PER_IMAGE
vertex = cost_map[VERTEX]
assert vertex["litellm_provider"] == "vertex_ai-language-models"
assert vertex["cache_read_input_token_cost"] == CACHE_READ_COST
assert vertex["input_cost_per_image"] == INPUT_COST_PER_IMAGE
assert vertex["supports_function_calling"] is False
assert vertex["supports_prompt_caching"] is True
def test_one_k_image_price_matches_official_token_math():
assert TOKENS_PER_1K_IMAGE * OUTPUT_IMAGE_TOKEN_COST == OUTPUT_COST_PER_1K_IMAGE
assert TOKENS_PER_1K_IMAGE * INPUT_COST == INPUT_COST_PER_IMAGE
@pytest.mark.parametrize("model", ALL_KEYS)
def test_backup_matches_main(model: str):
main_cost = _load(MAIN_PATH)
backup_cost = _load(BACKUP_PATH)
assert backup_cost.get(model) == main_cost.get(model), f"{model} differs between main and backup model cost maps"
def test_gemini_prefix_routes_to_gemini():
routed_model, provider, _, _ = get_llm_provider(model=GEMINI)
assert routed_model == UNPREFIXED
assert provider == "gemini"
def test_vertex_prefix_routes_to_vertex():
routed_model, provider, _, _ = get_llm_provider(model=VERTEX)
assert routed_model == UNPREFIXED
assert provider == "vertex_ai"
def test_text_token_cost(local_model_cost_map):
prompt_cost, text_completion_cost = cost_per_token(model=GEMINI, prompt_tokens=1000, completion_tokens=500)
assert prompt_cost == pytest.approx(1000 * INPUT_COST)
assert text_completion_cost == pytest.approx(500 * OUTPUT_TEXT_COST)
def test_completion_cost_bills_one_k_image(local_model_cost_map):
response = ModelResponse()
response.model = UNPREFIXED
response.usage = Usage(
prompt_tokens=7,
completion_tokens=TOKENS_PER_1K_IMAGE,
total_tokens=7 + TOKENS_PER_1K_IMAGE,
completion_tokens_details=CompletionTokensDetailsWrapper(image_tokens=TOKENS_PER_1K_IMAGE, text_tokens=0),
)
billed = completion_cost(
completion_response=response,
model=UNPREFIXED,
custom_llm_provider="vertex_ai",
)
expected = TOKENS_PER_1K_IMAGE * OUTPUT_IMAGE_TOKEN_COST + 7 * INPUT_COST
assert billed == pytest.approx(expected)
def test_image_tokens_are_not_billed_as_text(local_model_cost_map):
usage = Usage(
completion_tokens=1345,
prompt_tokens=10,
total_tokens=1355,
completion_tokens_details=CompletionTokensDetailsWrapper(
accepted_prediction_tokens=None,
audio_tokens=None,
reasoning_tokens=225,
rejected_prediction_tokens=None,
text_tokens=0,
image_tokens=TOKENS_PER_1K_IMAGE,
),
prompt_tokens_details=PromptTokensDetailsWrapper(
audio_tokens=None, cached_tokens=None, text_tokens=10, image_tokens=None
),
)
_, image_completion_cost = generic_cost_per_token(
model=UNPREFIXED,
usage=usage,
custom_llm_provider="vertex_ai",
)
expected_completion_cost = TOKENS_PER_1K_IMAGE * OUTPUT_IMAGE_TOKEN_COST + 225 * OUTPUT_TEXT_COST
bugged_text_only_cost = 1345 * OUTPUT_TEXT_COST
assert image_completion_cost > bugged_text_only_cost * 2
assert image_completion_cost == pytest.approx(expected_completion_cost)
def test_gemini_image_generation_uses_token_pricing(local_model_cost_map):
image_response = ImageResponse(
data=[ImageObject(b64_json="img1")],
usage=ImageUsage(
input_tokens=50 + TOKENS_PER_1K_IMAGE,
input_tokens_details=ImageUsageInputTokensDetails(
text_tokens=50,
image_tokens=TOKENS_PER_1K_IMAGE,
),
output_tokens=TOKENS_PER_1K_IMAGE,
total_tokens=50 + TOKENS_PER_1K_IMAGE + TOKENS_PER_1K_IMAGE,
),
)
cost = gemini_image_generation_cost_calculator(model=GEMINI, image_response=image_response)
expected = (50 + TOKENS_PER_1K_IMAGE) * INPUT_COST + TOKENS_PER_1K_IMAGE * OUTPUT_IMAGE_TOKEN_COST
assert cost == pytest.approx(expected)
assert cost != OUTPUT_COST_PER_1K_IMAGE
def test_vertex_image_generation_uses_token_pricing(local_model_cost_map):
image_response = ImageResponse(
data=[ImageObject(b64_json="img1")],
usage=ImageUsage(
input_tokens=50 + TOKENS_PER_1K_IMAGE,
input_tokens_details=ImageUsageInputTokensDetails(
text_tokens=50,
image_tokens=TOKENS_PER_1K_IMAGE,
),
output_tokens=TOKENS_PER_1K_IMAGE,
total_tokens=50 + TOKENS_PER_1K_IMAGE + TOKENS_PER_1K_IMAGE,
),
)
cost = vertex_image_generation_cost_calculator(model=UNPREFIXED, image_response=image_response)
expected = (50 + TOKENS_PER_1K_IMAGE) * INPUT_COST + TOKENS_PER_1K_IMAGE * OUTPUT_IMAGE_TOKEN_COST
assert cost == pytest.approx(expected)
def test_vertex_image_generation_falls_back_to_flat_image_price(local_model_cost_map):
image_response = ImageResponse(data=[ImageObject(b64_json="img1"), ImageObject(b64_json="img2")])
cost = vertex_image_generation_cost_calculator(model=UNPREFIXED, image_response=image_response)
assert cost == pytest.approx(2 * OUTPUT_COST_PER_1K_IMAGE)

2
tests/test_litellm/test_utils.py
View file

@ -4312,11 +4312,13 @@ class TestVertexEmbeddingEncodingFormat:
"vertex_ai/gemini-3-pro-image-preview",
"vertex_ai/gemini-3.1-flash-image",
"vertex_ai/gemini-3.1-flash-image-preview",
"vertex_ai/gemini-3.1-flash-lite-image",
"gemini/gemini-2.5-flash-image",
"gemini/gemini-3-pro-image",
"gemini/gemini-3-pro-image-preview",
"gemini/gemini-3.1-flash-image",
"gemini/gemini-3.1-flash-image-preview",
"gemini/gemini-3.1-flash-lite-image",
],
)
def test_gemini_image_models_do_not_support_reasoning(