diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 1eb72c887b5..ca67d5d6844 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -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, diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 1eb72c887b5..ca67d5d6844 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -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, diff --git a/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py b/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py index 3aa41e18f1e..36fc98a1f09 100644 --- a/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py +++ b/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py @@ -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): diff --git a/tests/test_litellm/test_gemini_3_1_flash_lite_image_model_metadata.py b/tests/test_litellm/test_gemini_3_1_flash_lite_image_model_metadata.py new file mode 100644 index 00000000000..aa6f03a47ff --- /dev/null +++ b/tests/test_litellm/test_gemini_3_1_flash_lite_image_model_metadata.py @@ -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) diff --git a/tests/test_litellm/test_utils.py b/tests/test_litellm/test_utils.py index 8e9e6167fb9..ada053ee38c 100644 --- a/tests/test_litellm/test_utils.py +++ b/tests/test_litellm/test_utils.py @@ -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(