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Merge pull request #36849 from emerzon/litellm_add_gemini_3_1_flash_lite_image

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fix(model_prices): correct gemini-3.1-flash-lite-image capabilities and dedupe its entries
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Mateo Wang 2026-08-21 17:56:43 -07:00 • committed by GitHub
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6 changed files with 510 additions and 320 deletions
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212
litellm/model_prices_and_context_window_backup.json
View file

@ -19498,106 +19498,6 @@
},
"web_search_billing_unit": "per_query"
},
"gemini-3.1-flash-lite-image": {
"input_cost_per_image": 0.00028,
"input_cost_per_token": 2.5e-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,
"source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#gemini-models",
"supported_endpoints": [
"/v1/chat/completions",
"/v1/completions",
"/v1/batch"
],
"supported_modalities": [
"text",
"image"
],
"supported_output_modalities": [
"text",
"image"
],
"supports_function_calling": false,
"supports_prompt_caching": true,
"supports_response_schema": false,
"supports_reasoning": true,
"supports_system_messages": true,
"supports_vision": true
},
"gemini/gemini-3.1-flash-lite-image": {
"rpm": 1000,
"tpm": 4000000,
"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,
"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_response_schema": false,
"supports_reasoning": true,
"supports_system_messages": true,
"supports_vision": true
},
"vertex_ai/gemini-3.1-flash-lite-image": {
"input_cost_per_image": 0.00028,
"input_cost_per_token": 2.5e-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,
"source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#gemini-models",
"supported_endpoints": [
"/v1/chat/completions",
"/v1/completions",
"/v1/batch"
],
"supported_modalities": [
"text",
"image"
],
"supported_output_modalities": [
"text",
"image"
],
"supports_function_calling": false,
"supports_prompt_caching": true,
"supports_response_schema": false,
"supports_reasoning": true,
"supports_system_messages": true,
"supports_vision": true
},
"gemini-3.1-flash-image": {
"deprecation_date": "2027-05-28",
"input_cost_per_image": 0.00056,
@ -19675,6 +19575,44 @@
},
"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,
@ -21505,6 +21443,42 @@
},
"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,
"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,
"tpm": 4000000
},
"gemini/deep-research-pro-preview-12-2025": {
"input_cost_per_image": 0.0011,
"input_cost_per_token": 2e-06,
@ -41066,6 +41040,44 @@
"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,
"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
},
"vertex_ai/gemini-3.1-flash-lite-preview": {
"cache_read_input_token_cost": 2.5e-08,
"input_cost_per_audio_token": 5e-07,

212
model_prices_and_context_window.json
View file

@ -19498,106 +19498,6 @@
},
"web_search_billing_unit": "per_query"
},
"gemini-3.1-flash-lite-image": {
"input_cost_per_image": 0.00028,
"input_cost_per_token": 2.5e-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,
"source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#gemini-models",
"supported_endpoints": [
"/v1/chat/completions",
"/v1/completions",
"/v1/batch"
],
"supported_modalities": [
"text",
"image"
],
"supported_output_modalities": [
"text",
"image"
],
"supports_function_calling": false,
"supports_prompt_caching": true,
"supports_response_schema": false,
"supports_reasoning": true,
"supports_system_messages": true,
"supports_vision": true
},
"gemini/gemini-3.1-flash-lite-image": {
"rpm": 1000,
"tpm": 4000000,
"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,
"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_response_schema": false,
"supports_reasoning": true,
"supports_system_messages": true,
"supports_vision": true
},
"vertex_ai/gemini-3.1-flash-lite-image": {
"input_cost_per_image": 0.00028,
"input_cost_per_token": 2.5e-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,
"source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#gemini-models",
"supported_endpoints": [
"/v1/chat/completions",
"/v1/completions",
"/v1/batch"
],
"supported_modalities": [
"text",
"image"
],
"supported_output_modalities": [
"text",
"image"
],
"supports_function_calling": false,
"supports_prompt_caching": true,
"supports_response_schema": false,
"supports_reasoning": true,
"supports_system_messages": true,
"supports_vision": true
},
"gemini-3.1-flash-image": {
"deprecation_date": "2027-05-28",
"input_cost_per_image": 0.00056,
@ -19675,6 +19575,44 @@
},
"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,
@ -21505,6 +21443,42 @@
},
"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,
"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,
"tpm": 4000000
},
"gemini/deep-research-pro-preview-12-2025": {
"input_cost_per_image": 0.0011,
"input_cost_per_token": 2e-06,
@ -41066,6 +41040,44 @@
"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,
"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
},
"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

@ -1909,6 +1909,7 @@ def test_service_tier_ultrafast_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):

377
tests/test_litellm/test_gemini_3_1_flash_lite_image_pricing.py
View file

@ -1,147 +1,284 @@
"""Pricing entry for ``gemini-3.1-flash-lite-image`` (Google's Nano Banana 2 Lite).
Google publishes: $0.25/1M input, $1.50/1M text output, and $30/1M image-output
tokens for the Lite image model (https://cloud.google.com/vertex-ai/generative-ai/pricing).
A 1K image is ~1120 output image tokens => ~$0.0336 / image.
Without this entry, ``completion_cost`` raises "model isn't mapped yet" and Vertex
generateContent pass-through cost tracking silently logs $0. These tests pin the
values in both the primary price map and the ``litellm/`` backup, and verify
``get_model_info`` / ``completion_cost`` surface them.
"""
import json
import os
from pathlib import Path
import pytest
import litellm
from litellm import completion_cost
from litellm.types.utils import CompletionTokensDetailsWrapper, ModelResponse, Usage
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,
)
VARIANTS = [
"gemini-3.1-flash-lite-image",
"gemini/gemini-3.1-flash-lite-image",
"vertex_ai/gemini-3.1-flash-lite-image",
]
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"
EXPECTED = {
"input_cost_per_token": 2.5e-07,
"output_cost_per_token": 1.5e-06,
"output_cost_per_image_token": 3e-05,
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
SHARED_FIELDS = {
"mode": "image_generation",
}
EXPECTED_CAPABILITIES = {
"max_output_tokens": 4096,
"max_tokens": 4096,
"input_cost_per_token": INPUT_COST,
"input_cost_per_token_batches": INPUT_COST_BATCHES,
"input_cost_per_image": INPUT_COST_PER_IMAGE,
"output_cost_per_token": OUTPUT_TEXT_COST,
"output_cost_per_token_batches": OUTPUT_TEXT_COST_BATCHES,
"output_cost_per_image": OUTPUT_COST_PER_1K_IMAGE,
"output_cost_per_image_token": OUTPUT_IMAGE_TOKEN_COST,
"max_input_tokens": MAX_INPUT_TOKENS,
"max_output_tokens": MAX_OUTPUT_TOKENS,
"max_tokens": MAX_OUTPUT_TOKENS,
"supported_endpoints": ["/v1/chat/completions", "/v1/completions", "/v1/batch"],
"supported_output_modalities": ["text", "image"],
"supports_reasoning": False,
"supports_response_schema": False,
"supports_reasoning": True,
"supports_system_messages": True,
"supports_vision": True,
}
EXPECTED_PER_ROUTE = {
"gemini-3.1-flash-lite-image": {
"supports_prompt_caching": True,
"supports_function_calling": False,
},
"vertex_ai/gemini-3.1-flash-lite-image": {
"supports_prompt_caching": True,
"supports_function_calling": False,
},
"gemini/gemini-3.1-flash-lite-image": {
"supports_prompt_caching": False,
VERTEX_ROUTE_FIELDS = {
"litellm_provider": "vertex_ai-language-models",
"cache_read_input_token_cost": CACHE_READ_COST,
"supported_modalities": ["text", "image", "video"],
"supports_function_calling": False,
"supports_pdf_input": True,
"supports_prompt_caching": True,
"supports_video_input": True,
}
PER_ROUTE_FIELDS = {
UNPREFIXED: VERTEX_ROUTE_FIELDS,
VERTEX: VERTEX_ROUTE_FIELDS,
GEMINI: {
"litellm_provider": "gemini",
"supported_modalities": ["text", "image"],
"supports_function_calling": True,
"input_cost_per_token_batches": 1.25e-07,
"output_cost_per_token_batches": 7.5e-07,
"supports_prompt_caching": False,
"rpm": 1000,
"tpm": 4000000,
},
}
GROUNDING_FIELDS = (
"supports_web_search",
"search_context_cost_per_query",
"web_search_billing_unit",
)
def _load_json(path: str) -> dict:
def _load(path: Path) -> dict:
with open(path, encoding="utf-8") as f:
return json.load(f)
def _backup_path() -> str:
return os.path.join(
os.path.dirname(litellm.__file__),
"model_prices_and_context_window_backup.json",
@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)
@pytest.mark.parametrize("path", (MAIN_PATH, BACKUP_PATH), ids=("main", "backup"))
def test_published_prices_are_registered(model: str, path: Path):
info = _load(path).get(model)
assert info is not None, f"{model} missing from {path.name}"
for field, value in SHARED_FIELDS.items():
assert info[field] == value, f"{model} {field} in {path.name}: {info.get(field)} != {value}"
@pytest.mark.parametrize("model", ALL_KEYS)
@pytest.mark.parametrize("path", (MAIN_PATH, BACKUP_PATH), ids=("main", "backup"))
def test_per_route_capabilities_match_model_cards(model: str, path: Path):
info = _load(path)[model]
for field, value in PER_ROUTE_FIELDS[model].items():
assert info[field] == value, f"{model} {field} in {path.name}: {info.get(field)} != {value}"
@pytest.mark.parametrize("model", ALL_KEYS)
@pytest.mark.parametrize("path", (MAIN_PATH, BACKUP_PATH), ids=("main", "backup"))
def test_grounding_fields_absent(model: str, path: Path):
info = _load(path)[model]
for field in GROUNDING_FIELDS:
assert field not in info, f"{model} should not define {field}"
@pytest.mark.parametrize("path", (MAIN_PATH, BACKUP_PATH), ids=("main", "backup"))
def test_ai_studio_route_has_no_implicit_cache_price(path: Path):
assert "cache_read_input_token_cost" not in _load(path)[GEMINI]
@pytest.mark.parametrize("model", ALL_KEYS)
def test_backup_matches_main(model: str):
assert _load(BACKUP_PATH).get(model) == _load(MAIN_PATH).get(model)
def test_one_k_image_price_matches_official_token_math():
assert TOKENS_PER_1K_IMAGE * OUTPUT_IMAGE_TOKEN_COST == pytest.approx(OUTPUT_COST_PER_1K_IMAGE)
assert TOKENS_PER_1K_IMAGE * INPUT_COST == pytest.approx(INPUT_COST_PER_IMAGE)
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_get_model_info_reports_published_costs(local_model_cost_map):
info = litellm.get_model_info(UNPREFIXED)
assert info["input_cost_per_token"] == INPUT_COST
assert info["output_cost_per_token"] == OUTPUT_TEXT_COST
assert info["cache_read_input_token_cost"] == CACHE_READ_COST
@pytest.mark.parametrize("model", ALL_KEYS)
def test_reasoning_params_are_not_offered_on_an_image_endpoint(model: str, local_model_cost_map):
assert litellm.supports_reasoning(model) is False
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 _one_k_image_response() -> ImageResponse:
return 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,
),
)
def _main_path() -> str:
return os.path.join(
os.path.dirname(__file__), "..", "..", "model_prices_and_context_window.json"
def test_gemini_image_generation_uses_token_pricing(local_model_cost_map):
cost = gemini_image_generation_cost_calculator(
model=GEMINI, image_response=_one_k_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
class TestGeminiFlashLiteImagePricingData:
"""Both price maps must carry Google's published Nano Banana 2 Lite costs."""
def test_present_in_both_maps(self):
main = _load_json(_main_path())
backup = _load_json(_backup_path())
for key in VARIANTS:
for label, data in (("main", main), ("backup", backup)):
assert key in data, f"{key} missing from {label} JSON"
entry = data[key]
for field, value in EXPECTED.items():
assert entry[field] == value, f"{key} {field} in {label}: {entry.get(field)} != {value}"
def test_capabilities_match_model_cards(self):
main = _load_json(_main_path())
backup = _load_json(_backup_path())
for key in VARIANTS:
expected = {**EXPECTED_CAPABILITIES, **EXPECTED_PER_ROUTE[key]}
for label, data in (("main", main), ("backup", backup)):
entry = data[key]
for field, value in expected.items():
assert entry[field] == value, f"{key} {field} in {label}: {entry.get(field)} != {value}"
def test_grounding_fields_absent(self):
"""Grounding with Google Search is unsupported on Lite, so no search pricing."""
for path in (_main_path(), _backup_path()):
data = _load_json(path)
for key in VARIANTS:
for field in (
"supports_web_search",
"search_context_cost_per_query",
"web_search_billing_unit",
):
assert field not in data[key], f"{key} should not define {field}"
def test_image_output_pricing_consistent(self):
"""1120 image-output tokens * output_cost_per_image_token == output_cost_per_image."""
backup = _load_json(_backup_path())
entry = backup["gemini-3.1-flash-lite-image"]
assert round(1120 * entry["output_cost_per_image_token"], 6) == entry["output_cost_per_image"]
def test_vertex_image_generation_uses_token_pricing(local_model_cost_map):
cost = vertex_image_generation_cost_calculator(
model=UNPREFIXED, image_response=_one_k_image_response()
)
expected = (
50 + TOKENS_PER_1K_IMAGE
) * INPUT_COST + TOKENS_PER_1K_IMAGE * OUTPUT_IMAGE_TOKEN_COST
assert cost == pytest.approx(expected)
class TestGeminiFlashLiteImageModelInfo:
"""``get_model_info`` and ``completion_cost`` must report the new costs."""
def test_get_model_info_and_cost(self):
original = litellm.model_cost
try:
litellm.model_cost = _load_json(_backup_path())
info = litellm.get_model_info("gemini-3.1-flash-lite-image")
assert info["input_cost_per_token"] == EXPECTED["input_cost_per_token"]
assert info["output_cost_per_token"] == EXPECTED["output_cost_per_token"]
resp = ModelResponse()
resp.model = "gemini-3.1-flash-lite-image"
resp.usage = Usage(
prompt_tokens=7,
completion_tokens=1120,
total_tokens=1127,
completion_tokens_details=CompletionTokensDetailsWrapper(
image_tokens=1120, text_tokens=0
),
)
cost = completion_cost(
completion_response=resp,
model="gemini-3.1-flash-lite-image",
custom_llm_provider="vertex_ai",
)
expected_cost = 1120 * 3e-05 + 7 * 2.5e-07
assert abs(cost - expected_cost) < 1e-6, f"unexpected cost {cost}"
finally:
litellm.model_cost = original
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)

26
tests/test_litellm/test_model_prices_schema.py
View file

@ -11,6 +11,7 @@ import pytest
REPO_ROOT = Path(__file__).parents[2]
GENERATOR_PATH = REPO_ROOT / "ci_cd" / "generate_model_prices_schema.py"
PRICES_PATH = REPO_ROOT / "model_prices_and_context_window.json"
BACKUP_PRICES_PATH = REPO_ROOT / "litellm" / "model_prices_and_context_window_backup.json"
SCHEMA_PATH = REPO_ROOT / "model_prices_and_context_window.schema.json"
@ -118,6 +119,31 @@ def test_schema_accepts_cache_creation_cost_inside_a_pricing_tier(committed_sche
assert validator.is_valid({"some-model": entry})
def find_duplicate_keys(path: Path) -> list[str]:
duplicates: list[str] = []
def record_duplicates(pairs):
seen: set[str] = set()
for key, _ in pairs:
if key in seen:
duplicates.append(key)
seen.add(key)
return dict(pairs)
json.loads(path.read_text(), object_pairs_hook=record_duplicates)
return duplicates
@pytest.mark.parametrize("path", (PRICES_PATH, BACKUP_PRICES_PATH), ids=("main", "backup"))
def test_price_map_has_no_duplicate_keys(path: Path):
assert find_duplicate_keys(path) == [], (
f"{path.name} defines the same key twice; JSON parsers keep only the last "
"occurrence, so the earlier entry's fields are silently dropped. This is what "
"a clean text merge of two branches that both added a model looks like: "
"deduplicate the keys into one entry"
)
DATED_VARIANT = re.compile(r"^(.*?)-(\d{4}-\d{2}-\d{2})$")
SERVICE_TIER_SUFFIXES = ("_flex", "_priority")

2
tests/test_litellm/test_utils.py
View file

@ -4391,11 +4391,13 @@ 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(