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Merge pull request #39983 from BerriAI/litellm_lit_7081_azure_ai_gpt_6_astra_pricing

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feat(cost-map): add azure_ai/gpt-6-astra Foundry pricing
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Mateo Wang 2026-09-06 01:27:22 -07:00 • committed by GitHub
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9 changed files with 271 additions and 17 deletions
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49
litellm/llms/azure_ai/chat/transformation.py
View file

@ -17,6 +17,7 @@ from litellm.litellm_core_utils.prompt_templates.common_utils import (
from litellm.llms.azure.common_utils import BaseAzureLLM
from litellm.llms.azure_ai.common_utils import is_foundry_model_inference_base
from litellm.llms.base_llm.chat.transformation import LiteLLMLoggingObj
from litellm.llms.openai.chat.gpt_5_transformation import OpenAIGPT5Config
from litellm.llms.openai.common_utils import drop_params_from_unprocessable_entity_error
from litellm.llms.openai.openai import OpenAIConfig
from litellm.llms.xai.chat.transformation import XAIChatConfig
@ -42,12 +43,37 @@ NON_OPENAI_SPEC_MESSAGE_FIELDS: Final = (
)
class AzureAIGPT5Config(OpenAIGPT5Config):
@classmethod
def _model_map_lookup_name(cls, model: str) -> str:
"""Normalise a Foundry routing name to its cost-map key, when the map has one.
A Foundry deployment and its OpenAI-hosted namesake are different products with
different capabilities, so ``azure_ai/<model>`` is the entry to read whenever the map
carries it. Most gpt-5-family names have no ``azure_ai/`` row, though, and prefixing
those anyway costs them every flag: ``get_llm_provider`` re-resolves an ``azure_ai/``
name to the azure provider when a global AZURE_AI_API_BASE points at an
openai.azure.com host, ``azure/<model>`` is not a key either, so the lookup lands
nowhere and every effort answer degrades to False. A missing key defers to the base
resolver instead.
"""
prefixed: Final = model if model.startswith("azure_ai/") else f"azure_ai/{model}"
return prefixed if prefixed in litellm.model_cost else super()._model_map_lookup_name(model)
azureAIGPT5Config: Final = AzureAIGPT5Config()
class AzureAIStudioConfig(OpenAIConfig):
def get_supported_openai_params(self, model: str) -> list:
model_supports_tool_choice = True # azure ai supports this by default
if not supports_tool_choice(model=f"azure_ai/{model}"):
model_supports_tool_choice = False
supported_params = super().get_supported_openai_params(model)
supported_params = (
azureAIGPT5Config.get_supported_openai_params(model)
if azureAIGPT5Config.is_model_gpt_5_model(model)
else super().get_supported_openai_params(model)
)
if not model_supports_tool_choice:
filtered_supported_params: Final = []
for param in supported_params:
@ -61,6 +87,27 @@ class AzureAIStudioConfig(OpenAIConfig):
return supported_params
def map_openai_params(
self,
non_default_params: dict[str, object], # mutable-ok: OpenAIConfig.map_openai_params signature
optional_params: dict[str, object], # mutable-ok: OpenAIConfig.map_openai_params signature
model: str,
drop_params: bool,
) -> dict[str, object]: # mutable-ok: OpenAIConfig.map_openai_params signature
if not azureAIGPT5Config.is_model_gpt_5_model(model):
return super().map_openai_params(
non_default_params=non_default_params,
optional_params=optional_params,
model=model,
drop_params=drop_params,
)
return azureAIGPT5Config.map_openai_params(
non_default_params=non_default_params,
optional_params=optional_params,
model=model,
drop_params=drop_params,
)
def _supports_stop_reason(self, model: str) -> bool:
"""
Check if the model supports stop tokens.

53
litellm/model_prices_and_context_window_backup.json
View file

@ -3485,6 +3485,55 @@
"supports_response_schema": true,
"supports_tool_choice": true
},
"azure_ai/gpt-6-astra": {
"cache_creation_input_token_cost": 1.25e-05,
"cache_creation_input_token_cost_above_272k_tokens": 2.5e-05,
"cache_read_input_token_cost": 1e-06,
"cache_read_input_token_cost_above_272k_tokens": 2e-06,
"input_cost_per_token": 1e-05,
"input_cost_per_token_above_272k_tokens": 2e-05,
"litellm_provider": "azure_ai",
"max_input_tokens": 922000,
"max_output_tokens": 128000,
"max_tokens": 128000,
"mode": "chat",
"output_cost_per_token": 5e-05,
"output_cost_per_token_above_272k_tokens": 7.5e-05,
"search_context_cost_per_query": {
"search_context_size_high": 0.01,
"search_context_size_low": 0.01,
"search_context_size_medium": 0.01
},
"source": "https://ai.azure.com/catalog/models/gpt-6-astra",
"supported_endpoints": [
"/v1/chat/completions",
"/v1/responses"
],
"supported_modalities": [
"text",
"image"
],
"supported_output_modalities": [
"text"
],
"supports_computer_use": true,
"supports_function_calling": true,
"supports_max_reasoning_effort": false,
"supports_minimal_reasoning_effort": false,
"supports_native_streaming": true,
"supports_none_reasoning_effort": true,
"supports_parallel_function_calling": true,
"supports_pdf_input": true,
"supports_prompt_cache_breakpoint": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_response_schema": true,
"supports_system_messages": true,
"supports_tool_choice": true,
"supports_vision": true,
"supports_web_search": true,
"supports_xhigh_reasoning_effort": true
},
"azure_ai/gpt-5.5": {
"deprecation_date": "2027-10-26",
"cache_read_input_token_cost": 5e-07,
@ -7189,7 +7238,7 @@
],
"supports_computer_use": true,
"supports_function_calling": true,
"supports_max_reasoning_effort": true,
"supports_max_reasoning_effort": false,
"supports_minimal_reasoning_effort": false,
"supports_native_streaming": true,
"supports_none_reasoning_effort": true,
@ -7455,7 +7504,7 @@
],
"supports_computer_use": true,
"supports_function_calling": true,
"supports_max_reasoning_effort": true,
"supports_max_reasoning_effort": false,
"supports_minimal_reasoning_effort": false,
"supports_native_streaming": true,
"supports_none_reasoning_effort": true,

4
litellm/router_utils/reasoning_effort_capability.py
View file

@ -10,8 +10,8 @@ opt-in. none is opt-out everywhere except the azure gpt-5 family, whose config r
UnsupportedParamsError without an explicit true.
xhigh is gated on the request path by the openai and azure gpt-5 configs. max is not gated there at
all: every entry carrying supports_max_reasoning_effort is Claude-family, and
anthropic/chat/transformation.py gates max on the output_config path while its reasoning_effort
all: outside the gpt-6-astra rows every entry carrying supports_max_reasoning_effort is Claude-family,
and anthropic/chat/transformation.py gates max on the output_config path while its reasoning_effort
path maps any level to a thinking budget. Making max opt-in is a deliberate trade, then, since an
explicit flag is the only signal that the tier is a real one rather than litellm rounding the level
to a budget, and a missing flag costs advisory metadata rather than a rejected request.

53
model_prices_and_context_window.json
View file

@ -3485,6 +3485,55 @@
"supports_response_schema": true,
"supports_tool_choice": true
},
"azure_ai/gpt-6-astra": {
"cache_creation_input_token_cost": 1.25e-05,
"cache_creation_input_token_cost_above_272k_tokens": 2.5e-05,
"cache_read_input_token_cost": 1e-06,
"cache_read_input_token_cost_above_272k_tokens": 2e-06,
"input_cost_per_token": 1e-05,
"input_cost_per_token_above_272k_tokens": 2e-05,
"litellm_provider": "azure_ai",
"max_input_tokens": 922000,
"max_output_tokens": 128000,
"max_tokens": 128000,
"mode": "chat",
"output_cost_per_token": 5e-05,
"output_cost_per_token_above_272k_tokens": 7.5e-05,
"search_context_cost_per_query": {
"search_context_size_high": 0.01,
"search_context_size_low": 0.01,
"search_context_size_medium": 0.01
},
"source": "https://ai.azure.com/catalog/models/gpt-6-astra",
"supported_endpoints": [
"/v1/chat/completions",
"/v1/responses"
],
"supported_modalities": [
"text",
"image"
],
"supported_output_modalities": [
"text"
],
"supports_computer_use": true,
"supports_function_calling": true,
"supports_max_reasoning_effort": false,
"supports_minimal_reasoning_effort": false,
"supports_native_streaming": true,
"supports_none_reasoning_effort": true,
"supports_parallel_function_calling": true,
"supports_pdf_input": true,
"supports_prompt_cache_breakpoint": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_response_schema": true,
"supports_system_messages": true,
"supports_tool_choice": true,
"supports_vision": true,
"supports_web_search": true,
"supports_xhigh_reasoning_effort": true
},
"azure_ai/gpt-5.5": {
"deprecation_date": "2027-10-26",
"cache_read_input_token_cost": 5e-07,
@ -7189,7 +7238,7 @@
],
"supports_computer_use": true,
"supports_function_calling": true,
"supports_max_reasoning_effort": true,
"supports_max_reasoning_effort": false,
"supports_minimal_reasoning_effort": false,
"supports_native_streaming": true,
"supports_none_reasoning_effort": true,
@ -7455,7 +7504,7 @@
],
"supports_computer_use": true,
"supports_function_calling": true,
"supports_max_reasoning_effort": true,
"supports_max_reasoning_effort": false,
"supports_minimal_reasoning_effort": false,
"supports_native_streaming": true,
"supports_none_reasoning_effort": true,

27
tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py
View file

@ -2008,7 +2008,14 @@ def test_generic_cost_per_token_azure_gpt56(_local_model_cost_map,
assert round(completion_cost, 10) == round(output_cost * completion_tokens, 10)
@pytest.mark.parametrize("model,zone_multiplier", [("azure/gpt-6-astra", 1.0), ("azure/us/gpt-6-astra", 1.1)])
@pytest.mark.parametrize(
"model,custom_llm_provider,zone_multiplier",
[
("azure/gpt-6-astra", "azure", 1.0),
("azure/us/gpt-6-astra", "azure", 1.1),
("azure_ai/gpt-6-astra", "azure_ai", 1.0),
],
)
@pytest.mark.parametrize(
"prompt_tokens,input_side_multiplier,output_multiplier",
[(100000, 1.0, 1.0), (300000, 2.0, 1.5)],
@ -2016,6 +2023,7 @@ def test_generic_cost_per_token_azure_gpt56(_local_model_cost_map,
def test_generic_cost_per_token_azure_gpt_6_astra_foundry_price_sheet(
_local_model_cost_map,
model,
custom_llm_provider,
zone_multiplier,
prompt_tokens,
input_side_multiplier,
@ -2023,7 +2031,8 @@ def test_generic_cost_per_token_azure_gpt_6_astra_foundry_price_sheet(
):
"""Microsoft Foundry sells gpt-6-astra at the OpenAI rates: $10 input, $1 cache read, $12.50 cache write,
$50 output per 1M tokens on Standard Global, with the input side doubling and output 1.5x above 272K
prompt tokens. Standard US Data Zone carries the usual 10% uplift on every rate.
prompt tokens. Standard US Data Zone carries the usual 10% uplift on every rate. A Foundry
deployment reached through the azure_ai route bills the same Standard Global sheet.
"""
cached_tokens = 50000
cache_write_tokens = 40000
@ -2041,7 +2050,7 @@ def test_generic_cost_per_token_azure_gpt_6_astra_foundry_price_sheet(
prompt_cost, completion_cost = generic_cost_per_token(
model=model,
usage=usage,
custom_llm_provider="azure",
custom_llm_provider=custom_llm_provider,
)
input_side = zone_multiplier * input_side_multiplier
@ -2051,6 +2060,18 @@ def test_generic_cost_per_token_azure_gpt_6_astra_foundry_price_sheet(
assert completion_cost == pytest.approx(zone_multiplier * output_multiplier * completion_tokens * 5e-5)
def test_generic_cost_per_token_azure_ai_gpt_6_astra_flex_bills_the_standard_rate(_local_model_cost_map):
usage = Usage(prompt_tokens=1000, completion_tokens=100, total_tokens=1100)
standard = generic_cost_per_token(model="azure_ai/gpt-6-astra", usage=usage, custom_llm_provider="azure_ai")
flex = generic_cost_per_token(
model="azure_ai/gpt-6-astra", usage=usage, custom_llm_provider="azure_ai", service_tier="flex"
)
assert flex == standard
assert standard == pytest.approx((1000 * 1e-05, 100 * 5e-05))
@pytest.mark.parametrize(
"model,expected_none,expected_xhigh,expected_minimal",
[

19
tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_handler_reasoning_effort_normalization.py
View file

@ -82,3 +82,22 @@ class TestTheNormalizedTierIsTheTierSent:
self, local_model_cost_map, model, provider, effort, expected
):
assert _reasoning_effort_sent(model, provider, effort) == expected
@pytest.mark.parametrize(
"model, provider",
[
("gpt-6-astra", "azure_ai"),
("azure_ai/gpt-6-astra", "azure_ai"),
("gpt-6-astra", "azure"),
("us/gpt-6-astra", "azure"),
],
)
def test_an_azure_hosted_astra_deployment_drops_to_the_tier_it_accepts(
self, local_model_cost_map, model, provider
):
"""The deployment answers ``max`` with a 400 naming ``none`` through ``xhigh``, so the rows
say so and the adapter sends the tier below instead of the rejected one."""
assert _reasoning_effort_sent(model, provider, "max") == "xhigh"
def test_the_openai_hosted_twin_still_sends_max(self, local_model_cost_map):
assert _reasoning_effort_sent("gpt-6-astra", "openai", "max") == "max"

42
tests/test_litellm/llms/azure_ai/chat/test_azure_ai_transformation.py
View file

@ -3,6 +3,8 @@ from unittest.mock import MagicMock, patch
import pytest
import litellm
from litellm.litellm_core_utils.get_model_cost_map import get_model_cost_map
from litellm.llms.azure_ai.azure_model_router.transformation import (
AzureModelRouterConfig,
)
@ -138,6 +140,46 @@ def test_azure_ai_validate_environment_with_azure_ad_token():
assert headers["Content-Type"] == "application/json"
@pytest.fixture
def _local_model_cost_map(monkeypatch: pytest.MonkeyPatch) -> None:
monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
monkeypatch.setattr(litellm, "model_cost", get_model_cost_map(url=litellm.model_cost_map_url))
def test_foundry_gpt_6_astra_keeps_sampling_params_when_reasoning_effort_is_none(_local_model_cost_map):
optional_params = AzureAIStudioConfig().map_openai_params(
non_default_params={"reasoning_effort": "none", "temperature": 0.2, "top_p": 0.9},
optional_params={},
model="gpt-6-astra",
drop_params=False,
)
assert optional_params == {"reasoning_effort": "none", "temperature": 0.2, "top_p": 0.9}
def test_a_gpt_5_name_without_a_foundry_row_keeps_reading_its_own_entry(
monkeypatch: pytest.MonkeyPatch, _local_model_cost_map
):
"""Most gpt-5-family names have no azure_ai/ row. Reading an azure_ai/ key for those finds
nothing, and an openai.azure.com base sends the name down the azure provider, which has no key
for it either, so every effort answer would silently fall back to false and take temperature,
top_p and logprobs down with it."""
monkeypatch.setenv("AZURE_AI_API_BASE", "https://example-resource.openai.azure.com")
monkeypatch.setenv("AZURE_AI_API_KEY", "placeholder")
optional_params = litellm.utils.get_optional_params(
model="gpt-5.1-chat-latest",
custom_llm_provider="azure_ai",
temperature=0.2,
top_p=0.9,
logprobs=True,
)
assert optional_params["temperature"] == 0.2
assert optional_params["top_p"] == 0.9
assert optional_params["logprobs"] is True
def test_azure_ai_grok_stop_parameter_handling():
"""
Test that Grok models properly handle stop parameter filtering in Azure AI Studio.

18
tests/test_litellm/proxy/auth/test_model_checks.py
View file

@ -857,6 +857,24 @@ def test_add_known_models_refreshes_models_by_provider_for_wildcard_expansion():
litellm.add_known_models(model_cost_map={})
assert fake_model not in litellm.models_by_provider["vertex_ai"]
def test_azure_ai_wildcard_lists_the_foundry_gpt_6_astra_entry(monkeypatch):
import litellm
from litellm.proxy.auth.model_checks import get_known_models_from_wildcard
monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
foundry_key = "azure_ai/gpt-6-astra"
local_entry = litellm.get_model_cost_map(url="")[foundry_key]
registered_before = foundry_key in litellm.azure_ai_models
try:
litellm.add_known_models(model_cost_map={foundry_key: local_entry})
assert foundry_key in get_known_models_from_wildcard("azure_ai/*")
finally:
if not registered_before:
litellm.azure_ai_models.discard(foundry_key)
litellm.add_known_models(model_cost_map={})
def test_get_complete_model_list_drops_no_default_models_sentinel():
from litellm.proxy.auth.model_checks import get_complete_model_list

23
tests/test_litellm/router_utils/test_reasoning_effort_capability.py
View file

@ -389,14 +389,24 @@ class TestGpt6AstraAdvertisesItsDocumentedLevels:
"max",
)
@pytest.mark.parametrize("model", ["azure/gpt-6-astra", "azure/us/gpt-6-astra"])
def test_a_foundry_deployment_also_advertises_none(self, local_model_cost_map, model):
"""Microsoft Foundry serves the same model but its API accepts reasoning_effort none
(verified live: 200 with zero reasoning tokens, and it unlocks temperature), which
OpenAI's rejects, so an Azure deployment offers none on top of low through max."""
@pytest.mark.parametrize(
"model,custom_llm_provider",
[
("azure/gpt-6-astra", "azure"),
("azure/us/gpt-6-astra", "azure"),
("azure_ai/gpt-6-astra", "azure_ai"),
],
)
def test_an_azure_hosted_deployment_advertises_none_but_not_max(
self, local_model_cost_map, model, custom_llm_provider
):
"""Microsoft hosts the same model with a different level set than OpenAI does. Verified live
on both Azure routes: none returns 200 with zero reasoning tokens and unlocks temperature,
which OpenAI's API rejects, while max returns 400 unsupported_value naming none through
xhigh as the levels it does take."""
from litellm.utils import _get_model_info_helper
model_info = dict(_get_model_info_helper(model=model, custom_llm_provider="azure"))
model_info = dict(_get_model_info_helper(model=model, custom_llm_provider=custom_llm_provider))
assert resolve_supported_reasoning_efforts(model_info, deployment_is_mapped=True) == (
"none",
@ -404,5 +414,4 @@ class TestGpt6AstraAdvertisesItsDocumentedLevels:
"medium",
"high",
"xhigh",
"max",
)