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test(cost): add best-guess Claude and GPT-6 cache-write cases (#46687)

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88
tests/integration/cost_calculation/cases/azure.py
View file

@ -5,6 +5,7 @@ PARITY pairs the streamed case with its plain twin where both bill the same row.
from typing import Final
from integration.cost_calculation.cost_tracking_case import (
with_response_usage,
with_usage,
BinaryResponse,
CostTrackingTestCase,
@ -6116,6 +6117,90 @@ AZURE_GPT_6_1_SOL_TIERED_PRIORITY_ABOVE_272K: Final = with_usage(
ExactExpected(spend=1.449888, input_cost=1.44, output_cost=0.009888, prompt_tokens=300000, completion_tokens=412),
)
AZURE_GPT_6_1_SOL_CACHE_WRITE_5M: Final = with_response_usage(
AZURE_GPT_6_1_SOL_CACHE_READ,
"azure/gpt-6.1-sol-cache_write_5m",
{
"completion_tokens": 380,
"completion_tokens_details": {
"accepted_prediction_tokens": 0,
"audio_tokens": 0,
"reasoning_tokens": 0,
"rejected_prediction_tokens": 0,
},
"latency_checkpoint": {
"engine_tbt_ms": 15,
"engine_ttft_ms": 1118,
"engine_ttlt_ms": 1221,
"pre_inference_ms": 172,
"service_tbt_ms": 15,
"service_ttft_ms": 1562,
"service_ttlt_ms": 1652,
"user_visible_ttft_ms": 1390,
},
"prompt_tokens": 12928,
"prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 12288, "cached_tokens": 0},
"total_tokens": 13308,
},
ExactExpected(spend=0.04296, input_cost=0.0384, output_cost=0.00456, prompt_tokens=12928, completion_tokens=380),
)
AZURE_GPT_6_LUNA_CACHE_WRITE_5M: Final = with_response_usage(
AZURE_GPT_6_LUNA_CACHE_READ,
"azure/gpt-6-luna-cache_write_5m",
{
"completion_tokens": 380,
"completion_tokens_details": {
"accepted_prediction_tokens": 0,
"audio_tokens": 0,
"reasoning_tokens": 0,
"rejected_prediction_tokens": 0,
},
"latency_checkpoint": {
"engine_tbt_ms": 7,
"engine_ttft_ms": 143,
"engine_ttlt_ms": 309,
"pre_inference_ms": 132,
"service_tbt_ms": 8,
"service_ttft_ms": 566,
"service_ttlt_ms": 748,
"user_visible_ttft_ms": 434,
},
"prompt_tokens": 12928,
"prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 12288, "cached_tokens": 0},
"total_tokens": 13308,
},
ExactExpected(spend=0.002327, input_cost=0.00208, output_cost=0.000247, prompt_tokens=12928, completion_tokens=380),
choice_updates={"content_filter_result": {"error": {"code": "content_filter_error", "message": "The contents are not filtered"}}},
)
AZURE_GPT_6_ASTRA_CACHE_WRITE_5M: Final = with_response_usage(
AZURE_GPT_6_ASTRA_CACHE_READ,
"azure/gpt-6-astra-cache_write_5m",
{
"completion_tokens": 380,
"completion_tokens_details": {
"accepted_prediction_tokens": 0,
"audio_tokens": 0,
"reasoning_tokens": 0,
"rejected_prediction_tokens": 0,
},
"latency_checkpoint": {
"engine_tbt_ms": 36,
"engine_ttft_ms": 537,
"engine_ttlt_ms": 751,
"pre_inference_ms": 356,
"service_tbt_ms": 41,
"service_ttft_ms": 1166,
"service_ttlt_ms": 1371,
"user_visible_ttft_ms": 810,
},
"prompt_tokens": 12928,
"prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 12288, "cached_tokens": 0},
"total_tokens": 13308,
},
ExactExpected(spend=0.22375, input_cost=0.2, output_cost=0.02375, prompt_tokens=12928, completion_tokens=380),
)
CASES: Final[tuple[CostTrackingTestCase, ...]] = (
AZURE_GPT_6_SOL_MESSAGES_BRIDGED_JSON,
AZURE_GPT_6_LUNA_MESSAGES_BRIDGED_JSON,
@ -6214,6 +6299,9 @@ CASES: Final[tuple[CostTrackingTestCase, ...]] = (
AZURE_GPT_6_1_SOL_CACHE_READ,
AZURE_GPT_6_LUNA_CACHE_READ,
AZURE_GPT_6_ASTRA_CACHE_READ,
AZURE_GPT_6_1_SOL_CACHE_WRITE_5M,
AZURE_GPT_6_LUNA_CACHE_WRITE_5M,
AZURE_GPT_6_ASTRA_CACHE_WRITE_5M,
)
PARITY: Final[tuple[StreamParityTestCase, ...]] = (

133
tests/integration/cost_calculation/cases/misc.py
View file

@ -17,6 +17,7 @@ from integration.cost_calculation.cost_tracking_case import (
WavUpload,
)
from integration.cost_calculation.stream_parity.case import StreamParityTestCase, sse_frames
from integration.cost_calculation.cases.anthropic import CLAUDE_FABLE_5_1_INPUT_TEXT, CLAUDE_FABLE_5_1_STREAM
# nova-next
@ -1420,6 +1421,133 @@ XAI_GROK_4_7_INPUT_TEXT: Final = CostTrackingTestCase(
)
AZURE_AI_CLAUDE_FABLE_5_1_INPUT_TEXT: Final = CLAUDE_FABLE_5_1_INPUT_TEXT.model_copy(
update={
"name": "azure_ai/claude-fable-5-1-input_text",
"model": "azure_ai/claude-fable-5-1",
"response": JsonResponse(
content_type="application/json",
body={
"id": "msg_$REQUEST_ID",
"type": "message",
"role": "assistant",
"model": "claude-fable-5-1",
"content": [
{"type": "thinking", "thinking": "scripted thought", "signature": "scripted signature"},
{"type": "text", "text": "scripted answer 3d584fa07ae1"},
],
"stop_reason": "end_turn",
"stop_sequence": None,
"stop_details": None,
"container": None,
"diagnostics": None,
"usage": {
"input_tokens": 1840,
"cache_creation_input_tokens": 0,
"cache_read_input_tokens": 0,
"cache_creation": {"ephemeral_5m_input_tokens": 0, "ephemeral_1h_input_tokens": 0},
"output_tokens": 412,
"output_tokens_details": {"thinking_tokens": 0},
"service_tier": "standard",
"inference_geo": "not_available",
},
},
),
"expected": ExactExpected(
spend=0.0663, input_cost=0.03128, output_cost=0.03502, prompt_tokens=1840, completion_tokens=412
),
}
)
AZURE_AI_CLAUDE_FABLE_5_1_STREAM: Final = CLAUDE_FABLE_5_1_STREAM.model_copy(
update={
"name": "azure_ai/claude-fable-5-1-stream",
"model": "azure_ai/claude-fable-5-1",
"response": SseResponse(
content_type="text/event-stream",
frames=sse_frames(
*(
{
"type": "message_start",
"message": {
"id": "msg_$REQUEST_ID",
"type": "message",
"role": "assistant",
"model": "claude-fable-5-1",
"content": [],
"stop_reason": None,
"stop_sequence": None,
"stop_details": None,
"container": None,
"diagnostics": None,
"usage": {
"input_tokens": 1840,
"output_tokens": 1,
"cache_creation_input_tokens": 0,
"cache_read_input_tokens": 0,
"cache_creation": {"ephemeral_5m_input_tokens": 0, "ephemeral_1h_input_tokens": 0},
"service_tier": "standard",
"inference_geo": "not_available",
},
},
},
{"type": "content_block_start", "index": 0, "content_block": {"type": "thinking", "thinking": "", "signature": ""}},
{"type": "ping"},
{"type": "content_block_delta", "index": 0, "delta": {"type": "thinking_delta", "thinking": ""}},
{
"type": "content_block_delta",
"index": 0,
"delta": {"type": "signature_delta", "signature": "scripted signature"},
},
{"type": "content_block_stop", "index": 0},
{"type": "content_block_start", "index": 1, "content_block": {"type": "text", "text": ""}},
*(
{
"type": "content_block_delta",
"index": 1,
"delta": {"type": "text_delta", "text": text},
}
for text in (
"scripted ",
"answer ",
"3d",
"58",
"4f",
"a0",
"7a",
"e1",
"",
"",
)
),
{"type": "content_block_stop", "index": 1},
{
"type": "message_delta",
"delta": {
"container": None,
"stop_details": None,
"stop_reason": "end_turn",
"stop_sequence": None,
},
"usage": {
"input_tokens": 1840,
"output_tokens": 412,
"cache_creation_input_tokens": 0,
"cache_read_input_tokens": 0,
"output_tokens_details": {"thinking_tokens": 0},
},
},
{"type": "message_stop"},
)
),
),
"expected": ExactExpected(
spend=0.0663, input_cost=0.03128, output_cost=0.03502, prompt_tokens=1840, completion_tokens=412
),
}
)
CASES: Final[tuple[CostTrackingTestCase, ...]] = (
NOVA_NEXT_TRANSCRIPTIONS_PER_SECOND,
AMAZON_NOVA_CANVAS_NEXT_IMAGES_ONE,
@ -1468,8 +1596,13 @@ CASES: Final[tuple[CostTrackingTestCase, ...]] = (
COHERE_COMMAND_A_V2_TOKENS,
MISTRAL_MEDIUM_2604_JSON,
XAI_GROK_4_7_INPUT_TEXT,
AZURE_AI_CLAUDE_FABLE_5_1_INPUT_TEXT,
AZURE_AI_CLAUDE_FABLE_5_1_STREAM,
)
PARITY: Final[tuple[StreamParityTestCase, ...]] = (
StreamParityTestCase(plain=TEXT_COMPLETIONS_OPENAI_BASIC, streamed=TEXT_COMPLETIONS_OPENAI_STREAM_USAGE),
StreamParityTestCase(
plain=AZURE_AI_CLAUDE_FABLE_5_1_INPUT_TEXT, streamed=AZURE_AI_CLAUDE_FABLE_5_1_STREAM
),
)

37
tests/integration/cost_calculation/cases/openai.py
View file

@ -5,6 +5,7 @@ PARITY pairs the streamed case with its plain twin where both bill the same row.
from typing import Final, cast
from integration.cost_calculation.cost_tracking_case import (
with_response_usage,
with_usage,
COST_MAP,
BinaryResponse,
@ -11104,6 +11105,39 @@ GPT_6_1_SOL_TIERED_PRIORITY_ABOVE_272K: Final = with_usage(
ExactExpected(spend=2.41648, input_cost=2.4, output_cost=0.01648, prompt_tokens=300000, completion_tokens=412),
)
_GPT_6_CACHE_WRITE_USAGE: Final = {
"prompt_tokens": 12928,
"completion_tokens": 380,
"total_tokens": 13308,
"prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 12288, "audio_tokens": 0},
"completion_tokens_details": {
"reasoning_tokens": 0,
"audio_tokens": 0,
"accepted_prediction_tokens": 0,
"rejected_prediction_tokens": 0,
},
}
GPT_6_1_SOL_CACHE_WRITE_5M: Final = with_response_usage(
GPT_6_1_SOL_CACHE_READ,
"gpt-6.1-sol-cache_write_5m",
_GPT_6_CACHE_WRITE_USAGE,
ExactExpected(spend=0.0358, input_cost=0.032, output_cost=0.0038, prompt_tokens=12928, completion_tokens=380),
)
GPT_6_LUNA_CACHE_WRITE_5M: Final = with_response_usage(
GPT_6_LUNA_CACHE_READ,
"gpt-6-luna-cache_write_5m",
_GPT_6_CACHE_WRITE_USAGE,
ExactExpected(spend=0.00179, input_cost=0.0016, output_cost=0.00019, prompt_tokens=12928, completion_tokens=380),
)
GPT_6_ASTRA_CACHE_WRITE_5M: Final = with_response_usage(
GPT_6_ASTRA_CACHE_READ,
"gpt-6-astra-cache_write_5m",
_GPT_6_CACHE_WRITE_USAGE,
ExactExpected(spend=0.2148, input_cost=0.192, output_cost=0.0228, prompt_tokens=12928, completion_tokens=380),
)
CASES: Final[tuple[CostTrackingTestCase, ...]] = (
GPT_6_SOL_RESPONSES_DEPLOYMENT_JSON,
GPT_6_SOL_CHAT_BRIDGED_JSON,
@ -11309,6 +11343,9 @@ CASES: Final[tuple[CostTrackingTestCase, ...]] = (
GPT_6_1_SOL_CACHE_READ,
GPT_6_LUNA_CACHE_READ,
GPT_6_ASTRA_CACHE_READ,
GPT_6_1_SOL_CACHE_WRITE_5M,
GPT_6_LUNA_CACHE_WRITE_5M,
GPT_6_ASTRA_CACHE_WRITE_5M,
)
PARITY: Final[tuple[StreamParityTestCase, ...]] = (

215
tests/integration/cost_calculation/cases/vertex_ai.py
View file

@ -4,6 +4,14 @@ PARITY pairs the streamed case with its plain twin where both bill the same row.
from typing import Final
from integration.cost_calculation.cases.anthropic import (
CLAUDE_FABLE_5_1_INPUT_TEXT,
CLAUDE_FABLE_5_1_STREAM,
CLAUDE_OPUS_5_5_INPUT_TEXT,
CLAUDE_OPUS_5_5_STREAM,
CLAUDE_SONNET_5_5_INPUT_TEXT,
CLAUDE_SONNET_5_5_STREAM,
)
from integration.cost_calculation.cost_tracking_case import (
CostTrackingTestCase,
ExactExpected,
@ -15,6 +23,76 @@ from integration.cost_calculation.cost_tracking_case import (
from integration.cost_calculation.stream_parity.case import StreamParityTestCase, sse_frames
def _vertex_claude_stream_response(model: str, stop_reason: str, text_deltas: tuple[str, ...]) -> SseResponse:
text_frames: Final = (
(
{"type": "content_block_start", "index": 1, "content_block": {"type": "text", "text": ""}},
*(
{
"type": "content_block_delta",
"index": 1,
"delta": {"type": "text_delta", "text": text},
}
for text in text_deltas
),
{"type": "content_block_stop", "index": 1},
)
if text_deltas
else ()
)
return SseResponse(
content_type="text/event-stream",
frames=sse_frames(
*(
{
"type": "message_start",
"message": {
"id": "msg_$REQUEST_ID",
"type": "message",
"role": "assistant",
"model": model,
"content": [],
"container": None,
"stop_reason": None,
"stop_sequence": None,
"stop_details": None,
"usage": {
"input_tokens": 1840,
"cache_creation_input_tokens": 0,
"cache_read_input_tokens": 0,
"cache_creation": {"ephemeral_5m_input_tokens": 0, "ephemeral_1h_input_tokens": 0},
"output_tokens": 0,
},
},
},
{"type": "ping"},
{"type": "content_block_start", "index": 0, "content_block": {"type": "thinking", "thinking": "", "signature": ""}},
{"type": "content_block_delta", "index": 0, "delta": {"type": "thinking_delta", "thinking": ""}},
{"type": "content_block_delta", "index": 0, "delta": {"type": "signature_delta", "signature": "scripted signature"}},
{"type": "content_block_stop", "index": 0},
*text_frames,
{
"type": "message_delta",
"delta": {
"stop_reason": stop_reason,
"stop_sequence": None,
"stop_details": None,
"container": None,
},
"usage": {
"input_tokens": 1840,
"cache_creation_input_tokens": 0,
"cache_read_input_tokens": 0,
"output_tokens": 412,
"output_tokens_details": {"thinking_tokens": 0},
},
},
{"type": "message_stop"},
)
),
)
# gemini-3.1-pro
GEMINI_3_1_PRO_INPUT_TEXT: Final = CostTrackingTestCase(
name="gemini-3.1-pro-input_text",
@ -2877,6 +2955,132 @@ VERTEX_EMBEDDINGS_TEXT_006: Final = CostTrackingTestCase(
)
VERTEX_AI_CLAUDE_SONNET_5_5_INPUT_TEXT: Final = CLAUDE_SONNET_5_5_INPUT_TEXT.model_copy(
update={
"name": "vertex_ai/claude-sonnet-5-5-input_text",
"model": "vertex_ai/claude-sonnet-5-5",
"response": JsonResponse(
content_type="application/json",
body={
"model": "claude-sonnet-5-5",
"id": "msg_$REQUEST_ID",
"type": "message",
"role": "assistant",
"content": [{"type": "thinking", "thinking": "scripted thought", "signature": "scripted signature"}],
"container": None,
"stop_reason": "max_tokens",
"stop_sequence": None,
"stop_details": None,
"usage": {
"input_tokens": 1840,
"cache_creation_input_tokens": 0,
"cache_read_input_tokens": 0,
"cache_creation": {"ephemeral_5m_input_tokens": 0, "ephemeral_1h_input_tokens": 0},
"output_tokens": 412,
"output_tokens_details": {"thinking_tokens": 0},
},
},
),
"expected": ExactExpected(
spend=0.01326, input_cost=0.006256, output_cost=0.007004, prompt_tokens=1840, completion_tokens=412
),
}
)
VERTEX_AI_CLAUDE_SONNET_5_5_STREAM: Final = CLAUDE_SONNET_5_5_STREAM.model_copy(
update={
"name": "vertex_ai/claude-sonnet-5-5-stream",
"model": "vertex_ai/claude-sonnet-5-5",
"response": _vertex_claude_stream_response("claude-sonnet-5-5", "max_tokens", ()),
"expected": VERTEX_AI_CLAUDE_SONNET_5_5_INPUT_TEXT.expected,
}
)
VERTEX_AI_CLAUDE_OPUS_5_5_INPUT_TEXT: Final = CLAUDE_OPUS_5_5_INPUT_TEXT.model_copy(
update={
"name": "vertex_ai/claude-opus-5-5-input_text",
"model": "vertex_ai/claude-opus-5-5",
"response": JsonResponse(
content_type="application/json",
body={
"model": "claude-opus-5-5",
"id": "msg_$REQUEST_ID",
"type": "message",
"role": "assistant",
"content": [
{"type": "thinking", "thinking": "scripted thought", "signature": "scripted signature"},
{"type": "text", "text": "scripted answer for cost fixtures"},
],
"container": None,
"stop_reason": "end_turn",
"stop_sequence": None,
"stop_details": None,
"usage": {
"input_tokens": 1840,
"cache_creation_input_tokens": 0,
"cache_read_input_tokens": 0,
"cache_creation": {"ephemeral_5m_input_tokens": 0, "ephemeral_1h_input_tokens": 0},
"output_tokens": 412,
"output_tokens_details": {"thinking_tokens": 0},
},
},
),
"expected": ExactExpected(
spend=0.02808, input_cost=0.013248, output_cost=0.014832, prompt_tokens=1840, completion_tokens=412
),
}
)
VERTEX_AI_CLAUDE_OPUS_5_5_STREAM: Final = CLAUDE_OPUS_5_5_STREAM.model_copy(
update={
"name": "vertex_ai/claude-opus-5-5-stream",
"model": "vertex_ai/claude-opus-5-5",
"response": _vertex_claude_stream_response(
"claude-opus-5-5",
"end_turn",
("scripted ", "answer ", "for ", "cost ", "fixtures", "", "", "", ""),
),
"expected": VERTEX_AI_CLAUDE_OPUS_5_5_INPUT_TEXT.expected,
}
)
VERTEX_AI_CLAUDE_FABLE_5_1_INPUT_TEXT: Final = CLAUDE_FABLE_5_1_INPUT_TEXT.model_copy(
update={
"name": "vertex_ai/claude-fable-5-1-input_text",
"model": "vertex_ai/claude-fable-5-1",
"response": JsonResponse(
content_type="application/json",
body={
"model": "claude-fable-5-1",
"id": "msg_$REQUEST_ID",
"type": "message",
"role": "assistant",
"content": [{"type": "thinking", "thinking": "scripted thought", "signature": "scripted signature"}],
"container": None,
"stop_reason": "max_tokens",
"stop_sequence": None,
"stop_details": None,
"usage": {
"input_tokens": 1840,
"cache_creation_input_tokens": 0,
"cache_read_input_tokens": 0,
"cache_creation": {"ephemeral_5m_input_tokens": 0, "ephemeral_1h_input_tokens": 0},
"output_tokens": 412,
"output_tokens_details": {"thinking_tokens": 0},
},
},
),
"expected": ExactExpected(
spend=0.0741, input_cost=0.03496, output_cost=0.03914, prompt_tokens=1840, completion_tokens=412
),
}
)
VERTEX_AI_CLAUDE_FABLE_5_1_STREAM: Final = CLAUDE_FABLE_5_1_STREAM.model_copy(
update={
"name": "vertex_ai/claude-fable-5-1-stream",
"model": "vertex_ai/claude-fable-5-1",
"response": _vertex_claude_stream_response("claude-fable-5-1", "max_tokens", ()),
"expected": VERTEX_AI_CLAUDE_FABLE_5_1_INPUT_TEXT.expected,
}
)
CASES: Final[tuple[CostTrackingTestCase, ...]] = (
GEMINI_3_1_PRO_INPUT_TEXT,
GEMINI_3_1_PRO_CACHE_READ,
@ -2926,6 +3130,12 @@ CASES: Final[tuple[CostTrackingTestCase, ...]] = (
IMAGEN_NEXT_IMAGES_ONE,
VERTEX_EMBEDDINGS_TEXT_006,
GEMINI_3_1_PRO_PASSTHROUGH_STREAM_GENERATE_CONTENT_PRICED_VIA_VERTEX_KEY,
VERTEX_AI_CLAUDE_SONNET_5_5_INPUT_TEXT,
VERTEX_AI_CLAUDE_SONNET_5_5_STREAM,
VERTEX_AI_CLAUDE_OPUS_5_5_INPUT_TEXT,
VERTEX_AI_CLAUDE_OPUS_5_5_STREAM,
VERTEX_AI_CLAUDE_FABLE_5_1_INPUT_TEXT,
VERTEX_AI_CLAUDE_FABLE_5_1_STREAM,
)
PARITY: Final[tuple[StreamParityTestCase, ...]] = (
@ -2941,4 +3151,9 @@ PARITY: Final[tuple[StreamParityTestCase, ...]] = (
plain=GEMINI_3_8_FLASH_RESPONSE_MODEL_OVERRIDE, streamed=GEMINI_3_8_FLASH_STREAM_RESPONSE_MODEL_OVERRIDE
),
StreamParityTestCase(plain=GEMINI_3_8_FLASH_TOOL_CALL, streamed=GEMINI_3_8_FLASH_STREAM_TOOL_CALL),
StreamParityTestCase(
plain=VERTEX_AI_CLAUDE_SONNET_5_5_INPUT_TEXT, streamed=VERTEX_AI_CLAUDE_SONNET_5_5_STREAM
),
StreamParityTestCase(plain=VERTEX_AI_CLAUDE_OPUS_5_5_INPUT_TEXT, streamed=VERTEX_AI_CLAUDE_OPUS_5_5_STREAM),
StreamParityTestCase(plain=VERTEX_AI_CLAUDE_FABLE_5_1_INPUT_TEXT, streamed=VERTEX_AI_CLAUDE_FABLE_5_1_STREAM),
)

81
tests/integration/cost_calculation/cost_map.py
View file

@ -630,6 +630,87 @@ COST_MAP_ENTRIES: Final[dict[str, dict[str, JsonValue]]] = {
"input_cost_per_token": 3e-06,
"output_cost_per_token": 1.5e-05,
},
"azure_ai/claude-fable-5-1": {
"litellm_provider": "azure_ai",
"mode": "chat",
"input_cost_per_token": 1.7e-05,
"output_cost_per_token": 8.5e-05,
"cache_creation_input_token_cost": 2.125e-05,
"cache_creation_input_token_cost_above_1hr": 3.4e-05,
"cache_read_input_token_cost": 4.25e-07,
"supports_function_calling": True,
"supports_prompt_caching": True,
"supports_reasoning": True,
"max_input_tokens": 1000000,
"max_output_tokens": 128000,
"max_tokens": 128000,
},
"vertex_ai/claude-sonnet-5-5": {
"litellm_provider": "vertex_ai-anthropic_models",
"mode": "chat",
"input_cost_per_token": 3.4e-06,
"input_cost_per_token_batches": 1.7e-06,
"output_cost_per_token": 1.7e-05,
"output_cost_per_token_batches": 8.5e-06,
"cache_creation_input_token_cost": 4.25e-06,
"cache_creation_input_token_cost_above_1hr": 6.8e-06,
"cache_read_input_token_cost": 1.7e-07,
"search_context_cost_per_query": {
"search_context_size_high": 0.017,
"search_context_size_low": 0.017,
"search_context_size_medium": 0.017,
},
"supports_function_calling": True,
"supports_prompt_caching": True,
"supports_reasoning": True,
"max_input_tokens": 1000000,
"max_output_tokens": 128000,
"max_tokens": 128000,
},
"vertex_ai/claude-opus-5-5": {
"litellm_provider": "vertex_ai-anthropic_models",
"mode": "chat",
"input_cost_per_token": 7.2e-06,
"input_cost_per_token_batches": 4.5e-06,
"output_cost_per_token": 3.6e-05,
"output_cost_per_token_batches": 2.25e-05,
"cache_creation_input_token_cost": 9e-06,
"cache_creation_input_token_cost_above_1hr": 1.44e-05,
"cache_read_input_token_cost": 3.6e-07,
"search_context_cost_per_query": {
"search_context_size_high": 0.018,
"search_context_size_low": 0.018,
"search_context_size_medium": 0.018,
},
"supports_function_calling": True,
"supports_prompt_caching": True,
"supports_reasoning": True,
"max_input_tokens": 1000000,
"max_output_tokens": 128000,
"max_tokens": 128000,
},
"vertex_ai/claude-fable-5-1": {
"litellm_provider": "vertex_ai-anthropic_models",
"mode": "chat",
"input_cost_per_token": 1.9e-05,
"input_cost_per_token_batches": 9.5e-06,
"output_cost_per_token": 9.5e-05,
"output_cost_per_token_batches": 4.75e-05,
"cache_creation_input_token_cost": 2.375e-05,
"cache_creation_input_token_cost_above_1hr": 3.8e-05,
"cache_read_input_token_cost": 4.75e-07,
"search_context_cost_per_query": {
"search_context_size_high": 0.019,
"search_context_size_low": 0.019,
"search_context_size_medium": 0.019,
},
"supports_function_calling": True,
"supports_prompt_caching": True,
"supports_reasoning": True,
"max_input_tokens": 1000000,
"max_output_tokens": 128000,
"max_tokens": 128000,
},
"groq/qwen/qwen3.8-27b": {
"litellm_provider": "groq",
"mode": "chat",

35
tests/integration/cost_calculation/cost_tracking_case.py
View file

@ -532,6 +532,7 @@ _PROVIDER_PREFIXES: Final[Mapping[str, str]] = MappingProxyType(
"cohere": "cohere",
"vertex_ai-language-models": "vertex_ai",
"vertex_ai-image-models": "vertex_ai",
"vertex_ai-anthropic_models": "vertex_ai",
"vertex_ai-embedding-models": "vertex_ai",
"gemini": "",
"together_ai": "",
@ -584,6 +585,9 @@ _LITELLM_PARAMS: Final[Mapping[str, Mapping[str, str]]] = MappingProxyType(
"vertex_ai-image-models": MappingProxyType(
{"vertex_project": "cc-scripted-project", "vertex_location": "us-central1"}
),
"vertex_ai-anthropic_models": MappingProxyType(
{"vertex_project": "cc-scripted-project", "vertex_location": "us-central1"}
),
"vertex_ai-embedding-models": MappingProxyType(
{"vertex_project": "cc-scripted-project", "vertex_location": "us-central1"}
),
@ -629,3 +633,34 @@ def with_usage(
"expected": expected,
}
)
def with_response_usage(
base: CostTrackingTestCase,
name: str,
usage: Mapping[str, JsonValue],
expected: Expected,
choice_updates: Mapping[str, JsonValue] | None = None,
) -> CostTrackingTestCase:
if not isinstance(base.response, JsonResponse):
raise TypeError(f"{base.name}: with_response_usage needs a JsonResponse base")
body: Final = {**base.response.body, "usage": dict(usage)}
response_body: Final = (
_with_choice_updates(body, choice_updates) if choice_updates is not None else body
)
return base.model_copy(
update={
"name": name,
"response": base.response.model_copy(update={"body": response_body}),
"expected": expected,
}
)
def _with_choice_updates(
body: Mapping[str, JsonValue], choice_updates: Mapping[str, JsonValue]
) -> dict[str, JsonValue]:
choices: Final = body.get("choices")
if not isinstance(choices, list) or not choices or not isinstance(choices[0], dict):
raise TypeError("stored body has no choice object")
return {**body, "choices": [{**choices[0], **choice_updates}, *choices[1:]]}