mirror of
https://github.com/BerriAI/litellm.git
synced 2026-09-07 08:26:10 +00:00
Merge 575f576822 into 2d0c9eed4d
This commit is contained in:
Divya Narahari
GitHub
commit
9fe0d8fd09
4 changed files with 130 additions and 64 deletions
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@ -10113,13 +10113,13 @@
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"supports_web_search": true
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},
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"azure_ai/jais-30b-chat": {
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"input_cost_per_token": 0.0032,
|
||||
"input_cost_per_token": 3.2e-06,
|
||||
"litellm_provider": "azure_ai",
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"max_input_tokens": 8192,
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"max_output_tokens": 8192,
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"max_tokens": 8192,
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||||
"mode": "chat",
|
||||
"output_cost_per_token": 0.00971,
|
||||
"output_cost_per_token": 9.71e-06,
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"source": "https://azure.microsoft.com/en-us/products/ai-services/ai-foundry/models/jais-30b-chat"
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},
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"azure_ai/jamba-instruct": {
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@ -43044,8 +43044,8 @@
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"max_tokens": 131072,
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"max_input_tokens": 131072,
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"max_output_tokens": 131072,
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"input_cost_per_token": 0.015,
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"output_cost_per_token": 0.06,
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||||
"input_cost_per_token": 1.5e-07,
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"output_cost_per_token": 6e-07,
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"litellm_provider": "wandb",
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"mode": "chat"
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},
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@ -43053,8 +43053,8 @@
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"max_tokens": 131072,
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"max_input_tokens": 131072,
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"max_output_tokens": 131072,
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"input_cost_per_token": 0.005,
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"output_cost_per_token": 0.02,
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"input_cost_per_token": 5e-08,
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"output_cost_per_token": 2e-07,
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"litellm_provider": "wandb",
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"mode": "chat"
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},
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@ -43062,8 +43062,8 @@
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"max_tokens": 131072,
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"max_input_tokens": 131072,
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"max_output_tokens": 131072,
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||||
"input_cost_per_token": 0.055,
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"output_cost_per_token": 0.2,
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"input_cost_per_token": 5.5e-07,
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"output_cost_per_token": 2e-06,
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"litellm_provider": "wandb",
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"mode": "chat"
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},
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@ -43071,8 +43071,8 @@
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"max_tokens": 262144,
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"max_input_tokens": 262144,
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"max_output_tokens": 262144,
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||||
"input_cost_per_token": 0.01,
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"output_cost_per_token": 0.01,
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"input_cost_per_token": 1e-07,
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"output_cost_per_token": 1e-07,
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"litellm_provider": "wandb",
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"mode": "chat"
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},
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@ -43080,8 +43080,8 @@
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"max_tokens": 262144,
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"max_input_tokens": 262144,
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"max_output_tokens": 262144,
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"input_cost_per_token": 0.1,
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"output_cost_per_token": 0.15,
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"input_cost_per_token": 1e-06,
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"output_cost_per_token": 1.5e-06,
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"litellm_provider": "wandb",
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"mode": "chat"
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},
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@ -43089,8 +43089,8 @@
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"max_tokens": 262144,
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"max_input_tokens": 262144,
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"max_output_tokens": 262144,
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"input_cost_per_token": 0.01,
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"output_cost_per_token": 0.01,
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"input_cost_per_token": 1e-07,
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"output_cost_per_token": 1e-07,
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"litellm_provider": "wandb",
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"mode": "chat"
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},
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@ -43135,8 +43135,8 @@
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"max_tokens": 128000,
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"max_input_tokens": 128000,
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"max_output_tokens": 128000,
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"input_cost_per_token": 0.022,
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"output_cost_per_token": 0.022,
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"input_cost_per_token": 2.2e-07,
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"output_cost_per_token": 2.2e-07,
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"litellm_provider": "wandb",
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"mode": "chat"
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},
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@ -43144,8 +43144,8 @@
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"max_tokens": 128000,
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"max_input_tokens": 128000,
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"max_output_tokens": 128000,
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"input_cost_per_token": 0.055,
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"output_cost_per_token": 0.165,
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"input_cost_per_token": 5.5e-07,
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"output_cost_per_token": 1.65e-06,
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"litellm_provider": "wandb",
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"mode": "chat"
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},
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@ -43153,8 +43153,8 @@
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"max_tokens": 161000,
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"max_input_tokens": 161000,
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"max_output_tokens": 161000,
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"input_cost_per_token": 0.135,
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"output_cost_per_token": 0.54,
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"input_cost_per_token": 1.35e-06,
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"output_cost_per_token": 5.4e-06,
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"litellm_provider": "wandb",
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"mode": "chat"
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},
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@ -43162,8 +43162,8 @@
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"max_tokens": 161000,
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"max_input_tokens": 161000,
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"max_output_tokens": 161000,
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"input_cost_per_token": 0.114,
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"output_cost_per_token": 0.275,
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"input_cost_per_token": 1.14e-06,
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"output_cost_per_token": 2.75e-06,
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"litellm_provider": "wandb",
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"mode": "chat"
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},
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@ -43171,8 +43171,8 @@
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"max_tokens": 128000,
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"max_input_tokens": 128000,
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"max_output_tokens": 128000,
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"input_cost_per_token": 0.071,
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"output_cost_per_token": 0.071,
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"input_cost_per_token": 7.1e-07,
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"output_cost_per_token": 7.1e-07,
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"litellm_provider": "wandb",
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"mode": "chat"
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},
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@ -43180,8 +43180,8 @@
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"max_tokens": 64000,
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"max_input_tokens": 64000,
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"max_output_tokens": 64000,
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"input_cost_per_token": 0.017,
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"output_cost_per_token": 0.066,
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"input_cost_per_token": 1.7e-07,
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"output_cost_per_token": 6.6e-07,
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"litellm_provider": "wandb",
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"mode": "chat"
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},
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@ -43189,8 +43189,8 @@
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"max_tokens": 128000,
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"max_input_tokens": 128000,
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"max_output_tokens": 128000,
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"input_cost_per_token": 0.008,
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"output_cost_per_token": 0.035,
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"input_cost_per_token": 8e-08,
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"output_cost_per_token": 3.5e-07,
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"litellm_provider": "wandb",
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"mode": "chat"
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},
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@ -43234,8 +43234,8 @@
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"max_tokens": 8192,
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"max_input_tokens": 8192,
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"max_output_tokens": 8192,
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"input_cost_per_token": 0.0005,
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"output_cost_per_token": 0.002,
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"input_cost_per_token": 1.8e-06,
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"output_cost_per_token": 1.8e-06,
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"litellm_provider": "watsonx",
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"mode": "chat",
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"supports_function_calling": false,
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@ -43246,8 +43246,8 @@
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"max_tokens": 8192,
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"max_input_tokens": 8192,
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"max_output_tokens": 8192,
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"input_cost_per_token": 0.0005,
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"output_cost_per_token": 0.002,
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"input_cost_per_token": 1.8e-06,
|
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"output_cost_per_token": 1.8e-06,
|
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"litellm_provider": "watsonx",
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"mode": "chat",
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"supports_function_calling": false,
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@ -10113,13 +10113,13 @@
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"supports_web_search": true
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},
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"azure_ai/jais-30b-chat": {
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"input_cost_per_token": 0.0032,
|
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"input_cost_per_token": 3.2e-06,
|
||||
"litellm_provider": "azure_ai",
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||||
"max_input_tokens": 8192,
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"max_output_tokens": 8192,
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"max_tokens": 8192,
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"mode": "chat",
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||||
"output_cost_per_token": 0.00971,
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"output_cost_per_token": 9.71e-06,
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"source": "https://azure.microsoft.com/en-us/products/ai-services/ai-foundry/models/jais-30b-chat"
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},
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"azure_ai/jamba-instruct": {
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@ -43044,8 +43044,8 @@
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"max_tokens": 131072,
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"max_input_tokens": 131072,
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"max_output_tokens": 131072,
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"input_cost_per_token": 0.015,
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"output_cost_per_token": 0.06,
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"input_cost_per_token": 1.5e-07,
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"output_cost_per_token": 6e-07,
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"litellm_provider": "wandb",
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"mode": "chat"
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},
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@ -43053,8 +43053,8 @@
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"max_tokens": 131072,
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"max_input_tokens": 131072,
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"max_output_tokens": 131072,
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"input_cost_per_token": 0.005,
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"output_cost_per_token": 0.02,
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"input_cost_per_token": 5e-08,
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"output_cost_per_token": 2e-07,
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"litellm_provider": "wandb",
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"mode": "chat"
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},
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@ -43062,8 +43062,8 @@
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"max_tokens": 131072,
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"max_input_tokens": 131072,
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"max_output_tokens": 131072,
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"input_cost_per_token": 0.055,
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"output_cost_per_token": 0.2,
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"input_cost_per_token": 5.5e-07,
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"output_cost_per_token": 2e-06,
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"litellm_provider": "wandb",
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"mode": "chat"
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},
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@ -43071,8 +43071,8 @@
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"max_tokens": 262144,
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"max_input_tokens": 262144,
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"max_output_tokens": 262144,
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"input_cost_per_token": 0.01,
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"output_cost_per_token": 0.01,
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"input_cost_per_token": 1e-07,
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"output_cost_per_token": 1e-07,
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"litellm_provider": "wandb",
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"mode": "chat"
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},
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@ -43080,8 +43080,8 @@
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"max_tokens": 262144,
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"max_input_tokens": 262144,
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"max_output_tokens": 262144,
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"input_cost_per_token": 0.1,
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"output_cost_per_token": 0.15,
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"input_cost_per_token": 1e-06,
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"output_cost_per_token": 1.5e-06,
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"litellm_provider": "wandb",
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"mode": "chat"
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},
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@ -43089,8 +43089,8 @@
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"max_tokens": 262144,
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"max_input_tokens": 262144,
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"max_output_tokens": 262144,
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"input_cost_per_token": 0.01,
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"output_cost_per_token": 0.01,
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"input_cost_per_token": 1e-07,
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"output_cost_per_token": 1e-07,
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"litellm_provider": "wandb",
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"mode": "chat"
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},
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@ -43135,8 +43135,8 @@
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"max_tokens": 128000,
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"max_input_tokens": 128000,
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"max_output_tokens": 128000,
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"input_cost_per_token": 0.022,
|
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"output_cost_per_token": 0.022,
|
||||
"input_cost_per_token": 2.2e-07,
|
||||
"output_cost_per_token": 2.2e-07,
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"litellm_provider": "wandb",
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"mode": "chat"
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},
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@ -43144,8 +43144,8 @@
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"max_tokens": 128000,
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"max_input_tokens": 128000,
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"max_output_tokens": 128000,
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"input_cost_per_token": 0.055,
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"output_cost_per_token": 0.165,
|
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"input_cost_per_token": 5.5e-07,
|
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"output_cost_per_token": 1.65e-06,
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"litellm_provider": "wandb",
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"mode": "chat"
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},
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@ -43153,8 +43153,8 @@
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"max_tokens": 161000,
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"max_input_tokens": 161000,
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"max_output_tokens": 161000,
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"input_cost_per_token": 0.135,
|
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"output_cost_per_token": 0.54,
|
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"input_cost_per_token": 1.35e-06,
|
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"output_cost_per_token": 5.4e-06,
|
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"litellm_provider": "wandb",
|
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"mode": "chat"
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},
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@ -43162,8 +43162,8 @@
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"max_tokens": 161000,
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"max_input_tokens": 161000,
|
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"max_output_tokens": 161000,
|
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"input_cost_per_token": 0.114,
|
||||
"output_cost_per_token": 0.275,
|
||||
"input_cost_per_token": 1.14e-06,
|
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"output_cost_per_token": 2.75e-06,
|
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"litellm_provider": "wandb",
|
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"mode": "chat"
|
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},
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@ -43171,8 +43171,8 @@
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"max_tokens": 128000,
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"max_input_tokens": 128000,
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"max_output_tokens": 128000,
|
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"input_cost_per_token": 0.071,
|
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"output_cost_per_token": 0.071,
|
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"input_cost_per_token": 7.1e-07,
|
||||
"output_cost_per_token": 7.1e-07,
|
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"litellm_provider": "wandb",
|
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"mode": "chat"
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},
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@ -43180,8 +43180,8 @@
|
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"max_tokens": 64000,
|
||||
"max_input_tokens": 64000,
|
||||
"max_output_tokens": 64000,
|
||||
"input_cost_per_token": 0.017,
|
||||
"output_cost_per_token": 0.066,
|
||||
"input_cost_per_token": 1.7e-07,
|
||||
"output_cost_per_token": 6.6e-07,
|
||||
"litellm_provider": "wandb",
|
||||
"mode": "chat"
|
||||
},
|
||||
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@ -43189,8 +43189,8 @@
|
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"max_tokens": 128000,
|
||||
"max_input_tokens": 128000,
|
||||
"max_output_tokens": 128000,
|
||||
"input_cost_per_token": 0.008,
|
||||
"output_cost_per_token": 0.035,
|
||||
"input_cost_per_token": 8e-08,
|
||||
"output_cost_per_token": 3.5e-07,
|
||||
"litellm_provider": "wandb",
|
||||
"mode": "chat"
|
||||
},
|
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@ -43234,8 +43234,8 @@
|
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"max_tokens": 8192,
|
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"max_input_tokens": 8192,
|
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"max_output_tokens": 8192,
|
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"input_cost_per_token": 0.0005,
|
||||
"output_cost_per_token": 0.002,
|
||||
"input_cost_per_token": 1.8e-06,
|
||||
"output_cost_per_token": 1.8e-06,
|
||||
"litellm_provider": "watsonx",
|
||||
"mode": "chat",
|
||||
"supports_function_calling": false,
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||||
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@ -43246,8 +43246,8 @@
|
|||
"max_tokens": 8192,
|
||||
"max_input_tokens": 8192,
|
||||
"max_output_tokens": 8192,
|
||||
"input_cost_per_token": 0.0005,
|
||||
"output_cost_per_token": 0.002,
|
||||
"input_cost_per_token": 1.8e-06,
|
||||
"output_cost_per_token": 1.8e-06,
|
||||
"litellm_provider": "watsonx",
|
||||
"mode": "chat",
|
||||
"supports_function_calling": false,
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||||
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@ -172,9 +172,26 @@ def test_baseten_model_api_pricing_entries(_local_model_cost_map):
|
|||
|
||||
def test_wandb_model_api_pricing_entries(_local_model_cost_map):
|
||||
|
||||
# W&B publishes per 1M tokens (https://wandb.ai/site/pricing/inference); these are
|
||||
# those figures divided by 1e6. Entries added before this was checked carried the
|
||||
# published number scaled by 1e5 too little, billing $15,000 per 1M tokens instead
|
||||
# of $0.15.
|
||||
expected_pricing = {
|
||||
"wandb/moonshotai/Kimi-K2.5": (6e-07, 3e-06),
|
||||
"wandb/MiniMaxAI/MiniMax-M2.5": (3e-07, 1.2e-06),
|
||||
"wandb/openai/gpt-oss-120b": (1.5e-07, 6e-07),
|
||||
"wandb/openai/gpt-oss-20b": (5e-08, 2e-07),
|
||||
"wandb/zai-org/GLM-4.5": (5.5e-07, 2e-06),
|
||||
"wandb/Qwen/Qwen3-235B-A22B-Instruct-2507": (1e-07, 1e-07),
|
||||
"wandb/Qwen/Qwen3-235B-A22B-Thinking-2507": (1e-07, 1e-07),
|
||||
"wandb/Qwen/Qwen3-Coder-480B-A35B-Instruct": (1e-06, 1.5e-06),
|
||||
"wandb/meta-llama/Llama-3.1-8B-Instruct": (2.2e-07, 2.2e-07),
|
||||
"wandb/meta-llama/Llama-3.3-70B-Instruct": (7.1e-07, 7.1e-07),
|
||||
"wandb/meta-llama/Llama-4-Scout-17B-16E-Instruct": (1.7e-07, 6.6e-07),
|
||||
"wandb/deepseek-ai/DeepSeek-V3.1": (5.5e-07, 1.65e-06),
|
||||
"wandb/deepseek-ai/DeepSeek-V3-0324": (1.14e-06, 2.75e-06),
|
||||
"wandb/deepseek-ai/DeepSeek-R1-0528": (1.35e-06, 5.4e-06),
|
||||
"wandb/microsoft/Phi-4-mini-instruct": (8e-08, 3.5e-07),
|
||||
}
|
||||
|
||||
for model_name, (input_cost, output_cost) in expected_pricing.items():
|
||||
|
|
|
|||
|
|
@ -173,3 +173,52 @@ def test_dated_variants_carry_base_alias_service_tier_pricing(prices: dict):
|
|||
"sync the tier keys so service-tier requests against pinned snapshots are not "
|
||||
"billed at standard rates:\n" + "\n".join(drifted)
|
||||
)
|
||||
|
||||
|
||||
# Fields quoting USD per token, including the audio/image/reasoning token variants and the
|
||||
# tier and threshold suffixes ("_flex", "_batches", "_above_200k_tokens"). Deliberately
|
||||
# excludes per-character/per-second/per-1k fields, whose "_above_128k_tokens" suffix names
|
||||
# a threshold rather than the unit, and Databricks' DBU fields, which are not dollars.
|
||||
PER_TOKEN_COST_FIELD = re.compile(r"(?:per_token|token_cost)(?:_|$)|_token$")
|
||||
|
||||
# $1000 per million tokens. The priciest model in the map is o1-pro at $600 per million
|
||||
# output tokens, so this leaves room for a genuinely expensive future model while still
|
||||
# being far below what a per-1k or per-million figure looks like once it lands in a
|
||||
# per-token field.
|
||||
MAX_USD_PER_TOKEN = 1e-3
|
||||
|
||||
|
||||
def per_token_costs(entry: dict) -> list[tuple[str, float]]:
|
||||
return [
|
||||
(field, value)
|
||||
for field, value in entry.items()
|
||||
if "dbu" not in field
|
||||
and PER_TOKEN_COST_FIELD.search(field)
|
||||
and isinstance(value, (int, float))
|
||||
and not isinstance(value, bool)
|
||||
]
|
||||
|
||||
|
||||
@pytest.mark.parametrize("path", (PRICES_PATH, BACKUP_PRICES_PATH), ids=("main", "backup"))
|
||||
def test_per_token_costs_are_priced_per_token(path: Path):
|
||||
"""Catch a vendor's per-1k or per-million price landing in a per-token field.
|
||||
|
||||
Vendors quote prices per 1,000 or per 1,000,000 tokens, litellm stores dollars per
|
||||
single token, and pasting the quoted number straight in overcharges by 3 to 6 orders
|
||||
of magnitude on every request through that model. Spend logs, budgets and the
|
||||
x-litellm-response-cost header all read these fields, so a budget trips on the first
|
||||
call instead of the ten-thousandth.
|
||||
"""
|
||||
mispriced = [
|
||||
f"{name}.{field}={value} (${value * 1_000_000:,.2f} per 1M tokens)"
|
||||
for name, entry in json.loads(path.read_text()).items()
|
||||
if isinstance(entry, dict) and name not in ("sample_spec", "fallback_generalizations")
|
||||
for field, value in per_token_costs(entry)
|
||||
if value > MAX_USD_PER_TOKEN
|
||||
]
|
||||
assert mispriced == [], (
|
||||
f"{path.name} prices these above ${MAX_USD_PER_TOKEN} per token, which is what a "
|
||||
"price quoted per 1k or per 1M tokens looks like when it is copied into a per-token "
|
||||
"field. Divide by the vendor's unit, or raise MAX_USD_PER_TOKEN if a model really is "
|
||||
"this expensive:\n" + "\n".join(mispriced)
|
||||
)
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue