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fix(pricing): add Azure GPT-5.6 Sol cache-write rates

Browse source 
Foundry blog publishes Sol cached writes at $6.25/M; match OpenAI
long-context and priority cache-creation tiers on azure/gpt-5.6 and
azure/gpt-5.6-sol. Update Azure gpt-5.6 unit expectations for the
Terra/Luna cuts already on this branch
This commit is contained in:
mubashir1osmani 2026-08-04 16:10:46 -07:00
parent 91b965732e
commit 6e512e0ebd
3 changed files with 61 additions and 4 deletions
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6
litellm/model_prices_and_context_window_backup.json
View file

@ -6341,6 +6341,9 @@
"supports_web_search": true
},
"azure/gpt-5.6": {
"cache_creation_input_token_cost": 6.25e-06,
"cache_creation_input_token_cost_above_272k_tokens": 1.25e-05,
"cache_creation_input_token_cost_priority": 1.25e-05,
"cache_read_input_token_cost": 5e-07,
"cache_read_input_token_cost_above_272k_tokens": 1e-06,
"cache_read_input_token_cost_priority": 1e-06,
@ -6386,6 +6389,9 @@
"supports_minimal_reasoning_effort": false
},
"azure/gpt-5.6-sol": {
"cache_creation_input_token_cost": 6.25e-06,
"cache_creation_input_token_cost_above_272k_tokens": 1.25e-05,
"cache_creation_input_token_cost_priority": 1.25e-05,
"cache_read_input_token_cost": 5e-07,
"cache_read_input_token_cost_above_272k_tokens": 1e-06,
"cache_read_input_token_cost_priority": 1e-06,

6
model_prices_and_context_window.json
View file

@ -6341,6 +6341,9 @@
"supports_web_search": true
},
"azure/gpt-5.6": {
"cache_creation_input_token_cost": 6.25e-06,
"cache_creation_input_token_cost_above_272k_tokens": 1.25e-05,
"cache_creation_input_token_cost_priority": 1.25e-05,
"cache_read_input_token_cost": 5e-07,
"cache_read_input_token_cost_above_272k_tokens": 1e-06,
"cache_read_input_token_cost_priority": 1e-06,
@ -6386,6 +6389,9 @@
"supports_minimal_reasoning_effort": false
},
"azure/gpt-5.6-sol": {
"cache_creation_input_token_cost": 6.25e-06,
"cache_creation_input_token_cost_above_272k_tokens": 1.25e-05,
"cache_creation_input_token_cost_priority": 1.25e-05,
"cache_read_input_token_cost": 5e-07,
"cache_read_input_token_cost_above_272k_tokens": 1e-06,
"cache_read_input_token_cost_priority": 1e-06,

53
tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py
View file

@ -757,11 +757,11 @@ def test_generic_cost_per_token_gpt56_terra_cache_costs_by_tier_and_context(
[
("azure/gpt-5.6", 5e-6, 3e-5, 5e-7),
("azure/gpt-5.6-sol", 5e-6, 3e-5, 5e-7),
("azure/gpt-5.6-terra", 2.5e-6, 1.5e-5, 2.5e-7),
("azure/gpt-5.6-luna", 1e-6, 6e-6, 1e-7),
("azure/gpt-5.6-terra", 2e-6, 1.2e-5, 2e-7),
("azure/gpt-5.6-luna", 2e-7, 1.2e-6, 2e-8),
("azure/us/gpt-5.6", 5.5e-6, 3.3e-5, 5.5e-7),
("azure/eu/gpt-5.6-terra", 2.75e-6, 1.65e-5, 2.75e-7),
("azure/eu/gpt-5.6-luna", 1.1e-6, 6.6e-6, 1.1e-7),
("azure/eu/gpt-5.6-terra", 2.2e-6, 1.32e-5, 2.2e-7),
("azure/eu/gpt-5.6-luna", 2.2e-7, 1.32e-6, 2.2e-8),
],
)
def test_generic_cost_per_token_azure_gpt56(
@ -795,6 +795,51 @@ def test_generic_cost_per_token_azure_gpt56(
assert round(completion_cost, 10) == round(output_cost * completion_tokens, 10)
@pytest.mark.parametrize(
"model",
[
"azure/gpt-5.6",
"azure/gpt-5.6-sol",
],
)
def test_generic_cost_per_token_azure_gpt56_sol_cache_write(model):
"""Azure Sol cache-write rate from the Foundry pricing table ($6.25/M)."""
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
litellm.model_cost = litellm.get_model_cost_map(url="")
cache_write_rate = 6.25e-6
cache_read_rate = 5e-7
input_rate = 5e-6
model_cost_map = litellm.model_cost[model]
assert model_cost_map["cache_creation_input_token_cost"] == cache_write_rate
assert model_cost_map["cache_creation_input_token_cost_above_272k_tokens"] == 1.25e-5
assert model_cost_map["cache_creation_input_token_cost_priority"] == 1.25e-5
cached_tokens = 50000
cache_write_tokens = 40000
prompt_tokens = 100000
text_tokens = prompt_tokens - cached_tokens - cache_write_tokens
usage = Usage(
prompt_tokens=prompt_tokens,
completion_tokens=100,
total_tokens=prompt_tokens + 100,
prompt_tokens_details=PromptTokensDetailsWrapper(
cached_tokens=cached_tokens, cache_write_tokens=cache_write_tokens
),
)
prompt_cost, _ = generic_cost_per_token(
model=model,
usage=usage,
custom_llm_provider="azure",
)
expected_prompt_cost = (
text_tokens * input_rate
+ cached_tokens * cache_read_rate
+ cache_write_tokens * cache_write_rate
)
assert prompt_cost == pytest.approx(expected_prompt_cost)
@pytest.mark.parametrize(
"model,expected_none,expected_xhigh,expected_minimal",
[