scriptoriumadmin/litellm
1
0
Fork 0
mirror of https://github.com/BerriAI/litellm.git synced 2026-10-11 03:38:38 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions 84

Merge 064152c354 into 7b47dffefd

Browse source
This commit is contained in:
Rida Lemkaalel 2026-04-24 14:05:57 +08:00 • committed by GitHub
commit 3348a20896
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
4 changed files with 653 additions and 1 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

12
docs/my-website/docs/providers/snowflake.md
View file

@ -8,10 +8,20 @@ import TabItem from '@theme/TabItem';
| Description | The Snowflake Cortex LLM REST API lets you access the COMPLETE and EMBED functions via HTTP POST requests |
| Provider Route on LiteLLM | `snowflake/` |
| Link to Provider Doc | [Snowflake ↗](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-llm-rest-api) |
| Base URLs | `https://{account-id}.snowflakecomputing.com/api/v2/cortex/inference:complete`,`https://{account-id}.snowflakecomputing.com/api/v2/cortex/inference:embed`|
| Base URLs | **Cortex LLM (v1):** `https://{account-id}.snowflakecomputing.com/api/v2/cortex/v1` <br/> **Cortex Search:** `https://{account-id}.snowflakecomputing.com/api/v2/databases/{db}/schemas/{schema}/cortex-search-services/{service}:query` <br/> **Cortex Agents:** `https://{account-id}.snowflakecomputing.com/api/v2/databases/{db}/schemas/{schema}/agents` <br/> **Cortex inference (COMPLETE/EMBED):** `https://{account-id}.snowflakecomputing.com/api/v2/cortex/inference:complete`, `https://{account-id}.snowflakecomputing.com/api/v2/cortex/inference:embed` |
| Supported OpenAI Endpoints | `/chat/completions`, `/completions`, `/embeddings` |
## Available APIs
Snowflake Cortex exposes three REST API surfaces:
| API | Endpoint Pattern | Description | Reference |
|-----|-----------------|-------------|-----------|
| **Cortex LLM (v1)** | `POST /api/v2/cortex/v1/chat/completions` <br/> `POST /api/v2/cortex/v1/messages` | OpenAI-compatible chat completions and Anthropic-compatible messages. Supports all Cortex models. | [Cortex REST API ↗](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-rest-api) |
| **Cortex Search** | `POST /api/v2/databases/{db}/schemas/{schema}/cortex-search-services/{service}:query` | Query a Cortex Search Service for low-latency semantic/hybrid search. | [Query Cortex Search ↗](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/query-cortex-search-service) |
| **Cortex Agents** | `POST /api/v2/databases/{db}/schemas/{schema}/agents` <br/> `GET /api/v2/databases/{db}/schemas/{schema}/agents/{name}` <br/> `PUT /api/v2/databases/{db}/schemas/{schema}/agents/{name}` <br/> `DELETE /api/v2/databases/{db}/schemas/{schema}/agents/{name}` | Create, manage, and interact with Cortex Agent objects. | [Cortex Agents REST API ↗](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-rest-api) |
## Supported OpenAI Parameters
```
"temperature",

257
litellm/model_prices_and_context_window_backup.json
View file

@ -28069,6 +28069,149 @@
"mode": "chat",
"supports_computer_use": true
},
"snowflake/claude-3-7-sonnet": {
"litellm_provider": "snowflake",
"max_input_tokens": 200000,
"max_output_tokens": 64000,
"max_tokens": 64000,
"mode": "chat",
"input_cost_per_token": 3e-06,
"output_cost_per_token": 1.5e-05,
"cache_creation_input_token_cost": 3.75e-06,
"cache_read_input_token_cost": 3e-07,
"supports_prompt_caching": true,
"supports_function_calling": true,
"supports_vision": true,
"supports_reasoning": true
},
"snowflake/claude-4-opus": {
"litellm_provider": "snowflake",
"max_input_tokens": 200000,
"max_output_tokens": 32000,
"max_tokens": 32000,
"mode": "chat",
"input_cost_per_token": 1.5e-05,
"output_cost_per_token": 7.5e-05,
"cache_creation_input_token_cost": 1.875e-05,
"cache_read_input_token_cost": 1.5e-06,
"supports_prompt_caching": true,
"supports_function_calling": true,
"supports_vision": true,
"supports_reasoning": true,
"supports_computer_use": true
},
"snowflake/claude-4-sonnet": {
"litellm_provider": "snowflake",
"max_input_tokens": 1000000,
"max_output_tokens": 64000,
"max_tokens": 64000,
"mode": "chat",
"input_cost_per_token": 3e-06,
"output_cost_per_token": 1.5e-05,
"cache_creation_input_token_cost": 3.75e-06,
"cache_read_input_token_cost": 3e-07,
"supports_prompt_caching": true,
"supports_function_calling": true,
"supports_vision": true,
"supports_reasoning": true,
"supports_computer_use": true
},
"snowflake/claude-haiku-4-5": {
"litellm_provider": "snowflake",
"max_input_tokens": 200000,
"max_output_tokens": 64000,
"max_tokens": 64000,
"mode": "chat",
"input_cost_per_token": 1.1e-06,
"output_cost_per_token": 5.5e-06,
"cache_creation_input_token_cost": 1.38e-06,
"cache_read_input_token_cost": 1.1e-07,
"supports_prompt_caching": true,
"supports_function_calling": true,
"supports_vision": true,
"supports_reasoning": true,
"supports_computer_use": true
},
"snowflake/claude-opus-4-5": {
"litellm_provider": "snowflake",
"max_input_tokens": 200000,
"max_output_tokens": 64000,
"max_tokens": 64000,
"mode": "chat",
"input_cost_per_token": 5.5e-06,
"output_cost_per_token": 2.75e-05,
"cache_creation_input_token_cost": 6.88e-06,
"cache_read_input_token_cost": 5.5e-07,
"supports_prompt_caching": true,
"supports_function_calling": true,
"supports_vision": true,
"supports_reasoning": true,
"supports_computer_use": true
},
"snowflake/claude-opus-4-6": {
"litellm_provider": "snowflake",
"max_input_tokens": 1000000,
"max_output_tokens": 128000,
"max_tokens": 128000,
"mode": "chat",
"input_cost_per_token": 5.5e-06,
"output_cost_per_token": 2.75e-05,
"cache_creation_input_token_cost": 6.88e-06,
"cache_read_input_token_cost": 5.5e-07,
"supports_prompt_caching": true,
"supports_function_calling": true,
"supports_vision": true,
"supports_reasoning": true,
"supports_computer_use": true
},
"snowflake/claude-sonnet-4-5": {
"litellm_provider": "snowflake",
"max_input_tokens": 200000,
"max_output_tokens": 64000,
"max_tokens": 64000,
"mode": "chat",
"input_cost_per_token": 3.3e-06,
"output_cost_per_token": 1.65e-05,
"cache_creation_input_token_cost": 4.13e-06,
"cache_read_input_token_cost": 3.3e-07,
"supports_prompt_caching": true,
"supports_function_calling": true,
"supports_vision": true,
"supports_reasoning": true,
"supports_computer_use": true
},
"snowflake/claude-sonnet-4-5-long-context": {
"litellm_provider": "snowflake",
"max_input_tokens": 200000,
"max_output_tokens": 64000,
"max_tokens": 64000,
"mode": "chat",
"input_cost_per_token": 6.6e-06,
"output_cost_per_token": 2.475e-05,
"cache_creation_input_token_cost": 8.25e-06,
"cache_read_input_token_cost": 6.6e-07,
"supports_prompt_caching": true,
"supports_function_calling": true,
"supports_vision": true,
"supports_reasoning": true,
"supports_computer_use": true
},
"snowflake/claude-sonnet-4-6": {
"litellm_provider": "snowflake",
"max_input_tokens": 1000000,
"max_output_tokens": 64000,
"max_tokens": 64000,
"mode": "chat",
"input_cost_per_token": 3.3e-06,
"output_cost_per_token": 1.65e-05,
"cache_creation_input_token_cost": 4.13e-06,
"cache_read_input_token_cost": 3.3e-07,
"supports_prompt_caching": true,
"supports_function_calling": true,
"supports_vision": true,
"supports_reasoning": true,
"supports_computer_use": true
},
"snowflake/deepseek-r1": {
"litellm_provider": "snowflake",
"max_input_tokens": 32768,
@ -28196,6 +28339,120 @@
"max_tokens": 8192,
"mode": "chat"
},
"snowflake/openai-gpt-4.1": {
"litellm_provider": "snowflake",
"max_input_tokens": 1047576,
"max_output_tokens": 32768,
"max_tokens": 32768,
"mode": "chat",
"input_cost_per_token": 2.2e-06,
"output_cost_per_token": 8.8e-06,
"cache_read_input_token_cost": 5.5e-07,
"supports_function_calling": true,
"supports_vision": true
},
"snowflake/openai-gpt-5": {
"litellm_provider": "snowflake",
"max_input_tokens": 272000,
"max_output_tokens": 128000,
"max_tokens": 128000,
"mode": "chat",
"input_cost_per_token": 1.38e-06,
"output_cost_per_token": 1.1e-05,
"cache_read_input_token_cost": 1.4e-07,
"supports_function_calling": true,
"supports_vision": true,
"supports_reasoning": true
},
"snowflake/openai-gpt-5-mini": {
"litellm_provider": "snowflake",
"max_input_tokens": 200000,
"max_output_tokens": 100000,
"max_tokens": 100000,
"mode": "chat",
"input_cost_per_token": 2.8e-07,
"output_cost_per_token": 2.2e-06,
"cache_read_input_token_cost": 3e-08,
"supports_function_calling": true,
"supports_vision": true
},
"snowflake/openai-gpt-5-nano": {
"litellm_provider": "snowflake",
"max_input_tokens": 200000,
"max_output_tokens": 100000,
"max_tokens": 100000,
"mode": "chat",
"input_cost_per_token": 6e-08,
"output_cost_per_token": 4.4e-07,
"cache_read_input_token_cost": 1e-08,
"supports_function_calling": true,
"supports_vision": true
},
"snowflake/openai-gpt-5.1": {
"litellm_provider": "snowflake",
"max_input_tokens": 272000,
"max_output_tokens": 128000,
"max_tokens": 128000,
"mode": "chat",
"input_cost_per_token": 1.38e-06,
"output_cost_per_token": 1.1e-05,
"cache_read_input_token_cost": 1.4e-07,
"supports_function_calling": true,
"supports_vision": true,
"supports_reasoning": true
},
"snowflake/openai-gpt-5.2": {
"litellm_provider": "snowflake",
"max_input_tokens": 272000,
"max_output_tokens": 128000,
"max_tokens": 128000,
"mode": "chat",
"input_cost_per_token": 1.93e-06,
"output_cost_per_token": 1.54e-05,
"cache_read_input_token_cost": 1.9e-07,
"supports_function_calling": true,
"supports_vision": true,
"supports_reasoning": true
},
"snowflake/openai-gpt-5.4": {
"litellm_provider": "snowflake",
"max_input_tokens": 272000,
"max_output_tokens": 128000,
"max_tokens": 128000,
"mode": "chat",
"input_cost_per_token": 2.75e-06,
"output_cost_per_token": 1.65e-05,
"cache_read_input_token_cost": 2.8e-07,
"supports_function_calling": true,
"supports_vision": true,
"supports_reasoning": true
},
"snowflake/openai-gpt-5.4-long-context": {
"litellm_provider": "snowflake",
"max_input_tokens": 272000,
"max_output_tokens": 128000,
"max_tokens": 128000,
"mode": "chat",
"input_cost_per_token": 5.5e-06,
"output_cost_per_token": 2.475e-05,
"cache_read_input_token_cost": 5.5e-07,
"supports_function_calling": true,
"supports_vision": true,
"supports_reasoning": true
},
"snowflake/openai-o4-mini": {
"litellm_provider": "snowflake",
"max_input_tokens": 200000,
"max_output_tokens": 100000,
"max_tokens": 100000,
"mode": "chat",
"input_cost_per_token": 1.1e-06,
"output_cost_per_token": 4.4e-06,
"cache_read_input_token_cost": 2.8e-07,
"supports_function_calling": true,
"supports_vision": true,
"supports_reasoning": true
},
"snowflake/reka-core": {
"litellm_provider": "snowflake",
"max_input_tokens": 32000,

257
model_prices_and_context_window.json
View file

@ -28105,6 +28105,149 @@
"mode": "chat",
"supports_computer_use": true
},
"snowflake/claude-3-7-sonnet": {
"litellm_provider": "snowflake",
"max_input_tokens": 200000,
"max_output_tokens": 64000,
"max_tokens": 64000,
"mode": "chat",
"input_cost_per_token": 3e-06,
"output_cost_per_token": 1.5e-05,
"cache_creation_input_token_cost": 3.75e-06,
"cache_read_input_token_cost": 3e-07,
"supports_prompt_caching": true,
"supports_function_calling": true,
"supports_vision": true,
"supports_reasoning": true
},
"snowflake/claude-4-opus": {
"litellm_provider": "snowflake",
"max_input_tokens": 200000,
"max_output_tokens": 32000,
"max_tokens": 32000,
"mode": "chat",
"input_cost_per_token": 1.5e-05,
"output_cost_per_token": 7.5e-05,
"cache_creation_input_token_cost": 1.875e-05,
"cache_read_input_token_cost": 1.5e-06,
"supports_prompt_caching": true,
"supports_function_calling": true,
"supports_vision": true,
"supports_reasoning": true,
"supports_computer_use": true
},
"snowflake/claude-4-sonnet": {
"litellm_provider": "snowflake",
"max_input_tokens": 1000000,
"max_output_tokens": 64000,
"max_tokens": 64000,
"mode": "chat",
"input_cost_per_token": 3e-06,
"output_cost_per_token": 1.5e-05,
"cache_creation_input_token_cost": 3.75e-06,
"cache_read_input_token_cost": 3e-07,
"supports_prompt_caching": true,
"supports_function_calling": true,
"supports_vision": true,
"supports_reasoning": true,
"supports_computer_use": true
},
"snowflake/claude-haiku-4-5": {
"litellm_provider": "snowflake",
"max_input_tokens": 200000,
"max_output_tokens": 64000,
"max_tokens": 64000,
"mode": "chat",
"input_cost_per_token": 1.1e-06,
"output_cost_per_token": 5.5e-06,
"cache_creation_input_token_cost": 1.38e-06,
"cache_read_input_token_cost": 1.1e-07,
"supports_prompt_caching": true,
"supports_function_calling": true,
"supports_vision": true,
"supports_reasoning": true,
"supports_computer_use": true
},
"snowflake/claude-opus-4-5": {
"litellm_provider": "snowflake",
"max_input_tokens": 200000,
"max_output_tokens": 64000,
"max_tokens": 64000,
"mode": "chat",
"input_cost_per_token": 5.5e-06,
"output_cost_per_token": 2.75e-05,
"cache_creation_input_token_cost": 6.88e-06,
"cache_read_input_token_cost": 5.5e-07,
"supports_prompt_caching": true,
"supports_function_calling": true,
"supports_vision": true,
"supports_reasoning": true,
"supports_computer_use": true
},
"snowflake/claude-opus-4-6": {
"litellm_provider": "snowflake",
"max_input_tokens": 1000000,
"max_output_tokens": 128000,
"max_tokens": 128000,
"mode": "chat",
"input_cost_per_token": 5.5e-06,
"output_cost_per_token": 2.75e-05,
"cache_creation_input_token_cost": 6.88e-06,
"cache_read_input_token_cost": 5.5e-07,
"supports_prompt_caching": true,
"supports_function_calling": true,
"supports_vision": true,
"supports_reasoning": true,
"supports_computer_use": true
},
"snowflake/claude-sonnet-4-5": {
"litellm_provider": "snowflake",
"max_input_tokens": 200000,
"max_output_tokens": 64000,
"max_tokens": 64000,
"mode": "chat",
"input_cost_per_token": 3.3e-06,
"output_cost_per_token": 1.65e-05,
"cache_creation_input_token_cost": 4.13e-06,
"cache_read_input_token_cost": 3.3e-07,
"supports_prompt_caching": true,
"supports_function_calling": true,
"supports_vision": true,
"supports_reasoning": true,
"supports_computer_use": true
},
"snowflake/claude-sonnet-4-5-long-context": {
"litellm_provider": "snowflake",
"max_input_tokens": 200000,
"max_output_tokens": 64000,
"max_tokens": 64000,
"mode": "chat",
"input_cost_per_token": 6.6e-06,
"output_cost_per_token": 2.475e-05,
"cache_creation_input_token_cost": 8.25e-06,
"cache_read_input_token_cost": 6.6e-07,
"supports_prompt_caching": true,
"supports_function_calling": true,
"supports_vision": true,
"supports_reasoning": true,
"supports_computer_use": true
},
"snowflake/claude-sonnet-4-6": {
"litellm_provider": "snowflake",
"max_input_tokens": 1000000,
"max_output_tokens": 64000,
"max_tokens": 64000,
"mode": "chat",
"input_cost_per_token": 3.3e-06,
"output_cost_per_token": 1.65e-05,
"cache_creation_input_token_cost": 4.13e-06,
"cache_read_input_token_cost": 3.3e-07,
"supports_prompt_caching": true,
"supports_function_calling": true,
"supports_vision": true,
"supports_reasoning": true,
"supports_computer_use": true
},
"snowflake/deepseek-r1": {
"litellm_provider": "snowflake",
"max_input_tokens": 32768,
@ -28232,6 +28375,120 @@
"max_tokens": 8192,
"mode": "chat"
},
"snowflake/openai-gpt-4.1": {
"litellm_provider": "snowflake",
"max_input_tokens": 1047576,
"max_output_tokens": 32768,
"max_tokens": 32768,
"mode": "chat",
"input_cost_per_token": 2.2e-06,
"output_cost_per_token": 8.8e-06,
"cache_read_input_token_cost": 5.5e-07,
"supports_function_calling": true,
"supports_vision": true
},
"snowflake/openai-gpt-5": {
"litellm_provider": "snowflake",
"max_input_tokens": 272000,
"max_output_tokens": 128000,
"max_tokens": 128000,
"mode": "chat",
"input_cost_per_token": 1.38e-06,
"output_cost_per_token": 1.1e-05,
"cache_read_input_token_cost": 1.4e-07,
"supports_function_calling": true,
"supports_vision": true,
"supports_reasoning": true
},
"snowflake/openai-gpt-5-mini": {
"litellm_provider": "snowflake",
"max_input_tokens": 200000,
"max_output_tokens": 100000,
"max_tokens": 100000,
"mode": "chat",
"input_cost_per_token": 2.8e-07,
"output_cost_per_token": 2.2e-06,
"cache_read_input_token_cost": 3e-08,
"supports_function_calling": true,
"supports_vision": true
},
"snowflake/openai-gpt-5-nano": {
"litellm_provider": "snowflake",
"max_input_tokens": 200000,
"max_output_tokens": 100000,
"max_tokens": 100000,
"mode": "chat",
"input_cost_per_token": 6e-08,
"output_cost_per_token": 4.4e-07,
"cache_read_input_token_cost": 1e-08,
"supports_function_calling": true,
"supports_vision": true
},
"snowflake/openai-gpt-5.1": {
"litellm_provider": "snowflake",
"max_input_tokens": 272000,
"max_output_tokens": 128000,
"max_tokens": 128000,
"mode": "chat",
"input_cost_per_token": 1.38e-06,
"output_cost_per_token": 1.1e-05,
"cache_read_input_token_cost": 1.4e-07,
"supports_function_calling": true,
"supports_vision": true,
"supports_reasoning": true
},
"snowflake/openai-gpt-5.2": {
"litellm_provider": "snowflake",
"max_input_tokens": 272000,
"max_output_tokens": 128000,
"max_tokens": 128000,
"mode": "chat",
"input_cost_per_token": 1.93e-06,
"output_cost_per_token": 1.54e-05,
"cache_read_input_token_cost": 1.9e-07,
"supports_function_calling": true,
"supports_vision": true,
"supports_reasoning": true
},
"snowflake/openai-gpt-5.4": {
"litellm_provider": "snowflake",
"max_input_tokens": 272000,
"max_output_tokens": 128000,
"max_tokens": 128000,
"mode": "chat",
"input_cost_per_token": 2.75e-06,
"output_cost_per_token": 1.65e-05,
"cache_read_input_token_cost": 2.8e-07,
"supports_function_calling": true,
"supports_vision": true,
"supports_reasoning": true
},
"snowflake/openai-gpt-5.4-long-context": {
"litellm_provider": "snowflake",
"max_input_tokens": 272000,
"max_output_tokens": 128000,
"max_tokens": 128000,
"mode": "chat",
"input_cost_per_token": 5.5e-06,
"output_cost_per_token": 2.475e-05,
"cache_read_input_token_cost": 5.5e-07,
"supports_function_calling": true,
"supports_vision": true,
"supports_reasoning": true
},
"snowflake/openai-o4-mini": {
"litellm_provider": "snowflake",
"max_input_tokens": 200000,
"max_output_tokens": 100000,
"max_tokens": 100000,
"mode": "chat",
"input_cost_per_token": 1.1e-06,
"output_cost_per_token": 4.4e-06,
"cache_read_input_token_cost": 2.8e-07,
"supports_function_calling": true,
"supports_vision": true,
"supports_reasoning": true
},
"snowflake/reka-core": {
"litellm_provider": "snowflake",
"max_input_tokens": 32000,

128
tests/test_litellm/llms/snowflake/test_snowflake_pricing.py Normal file
View file

@ -0,0 +1,128 @@
import json
import os
# All 18 Snowflake models added from Table 6(b) of the Snowflake Service
# Consumption Table (REST API with Prompt Caching, Regional pricing).
SNOWFLAKE_CLAUDE_MODELS = [
"snowflake/claude-3-7-sonnet",
"snowflake/claude-4-opus",
"snowflake/claude-4-sonnet",
"snowflake/claude-haiku-4-5",
"snowflake/claude-opus-4-5",
"snowflake/claude-opus-4-6",
"snowflake/claude-sonnet-4-5",
"snowflake/claude-sonnet-4-5-long-context",
"snowflake/claude-sonnet-4-6",
]
SNOWFLAKE_OPENAI_MODELS = [
"snowflake/openai-gpt-4.1",
"snowflake/openai-gpt-5",
"snowflake/openai-gpt-5-mini",
"snowflake/openai-gpt-5-nano",
"snowflake/openai-gpt-5.1",
"snowflake/openai-gpt-5.2",
"snowflake/openai-gpt-5.4",
"snowflake/openai-gpt-5.4-long-context",
"snowflake/openai-o4-mini",
]
ALL_SNOWFLAKE_MODELS = SNOWFLAKE_CLAUDE_MODELS + SNOWFLAKE_OPENAI_MODELS
def _load_pricing_data():
json_path = os.path.join(
os.path.dirname(__file__), "../../../../model_prices_and_context_window.json"
)
assert os.path.exists(json_path), f"Could not find pricing JSON at {json_path}"
with open(json_path, "r") as f:
return json.load(f)
def test_snowflake_models_exist():
"""All 18 new Snowflake REST API models must be present in the pricing JSON."""
data = _load_pricing_data()
missing = [m for m in ALL_SNOWFLAKE_MODELS if m not in data]
assert not missing, f"Missing Snowflake models: {missing}"
def test_snowflake_models_have_correct_provider():
"""Every new Snowflake model must declare litellm_provider = 'snowflake'."""
data = _load_pricing_data()
errors = []
for model in ALL_SNOWFLAKE_MODELS:
if model in data:
provider = data[model].get("litellm_provider")
if provider != "snowflake":
errors.append(
f"{model}: litellm_provider={provider!r}, expected 'snowflake'"
)
assert not errors, "\n".join(errors)
def test_snowflake_models_have_positive_pricing():
"""All new Snowflake models must have positive input and output costs."""
data = _load_pricing_data()
errors = []
for model in ALL_SNOWFLAKE_MODELS:
info = data.get(model, {})
for field in ("input_cost_per_token", "output_cost_per_token"):
val = info.get(field)
if val is None:
errors.append(f"{model}: missing {field}")
elif val <= 0:
errors.append(f"{model}: {field}={val} is not positive")
assert not errors, "\n".join(errors)
def test_snowflake_claude_models_have_prompt_caching_fields():
"""Claude models on Snowflake support prompt caching and must include both
cache_creation_input_token_cost and cache_read_input_token_cost."""
data = _load_pricing_data()
errors = []
for model in SNOWFLAKE_CLAUDE_MODELS:
info = data.get(model, {})
for field in ("cache_creation_input_token_cost", "cache_read_input_token_cost"):
val = info.get(field)
if val is None:
errors.append(f"{model}: missing {field}")
elif val <= 0:
errors.append(f"{model}: {field}={val} is not positive")
if not info.get("supports_prompt_caching"):
errors.append(f"{model}: supports_prompt_caching should be True")
assert not errors, "\n".join(errors)
def test_snowflake_openai_models_have_cache_read_but_no_cache_write():
"""OpenAI models on Snowflake (Azure) have cache read pricing but no cache
write cost (Table 6b shows '-' for cache write on OpenAI models)."""
data = _load_pricing_data()
errors = []
for model in SNOWFLAKE_OPENAI_MODELS:
info = data.get(model, {})
# Must have cache read cost
val = info.get("cache_read_input_token_cost")
if val is None:
errors.append(f"{model}: missing cache_read_input_token_cost")
elif val <= 0:
errors.append(f"{model}: cache_read_input_token_cost={val} is not positive")
# Must NOT have cache creation cost
if "cache_creation_input_token_cost" in info:
errors.append(
f"{model}: unexpected cache_creation_input_token_cost "
f"(OpenAI models have no cache write pricing)"
)
assert not errors, "\n".join(errors)
def test_snowflake_models_have_context_window():
"""All new Snowflake models must define max_input_tokens and max_output_tokens."""
data = _load_pricing_data()
errors = []
for model in ALL_SNOWFLAKE_MODELS:
info = data.get(model, {})
for field in ("max_input_tokens", "max_output_tokens", "max_tokens"):
if info.get(field) is None:
errors.append(f"{model}: missing {field}")
assert not errors, "\n".join(errors)