wuenlp_tools.utils.model_cost

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 1from loguru import logger
 2
 3model_cost_query = {
 4    "gpt-5": 1.25 / 1000000,
 5    "gpt-5-mini": 0.25 / 1000000,
 6    "gpt-5-nano": 0.05 / 1000000,
 7    "o4-mini": 1.1 / 1000000,
 8    "o4-mini-deep-research": 2 / 1000000,
 9    "gpt-4.1": 2 / 1000000,
10    "gpt-4.1-mini": 0.4 / 1000000,
11    "gpt-4.1-nano": 0.1 / 1000000,
12    "gpt-4o": 2.5 / 1000000,
13    "o1-preview": 15 / 1000000,
14    "o1-mini": 1.1 / 1000000,
15    "o3-mini": 1.1 / 1000000,
16    "gpt-4o-2024-08-06": 2.5 / 1000000,
17    "gpt-4o-mini": 0.15 / 1000000,
18    "gpt-4o-mini-search-preview": 0.15 / 1000000,
19    "gpt-3.5-turbo": 0.5 / 1000000
20}
21model_cost_output = {
22    "gpt-5": 10 / 1000000,
23    "gpt-5-mini": 2 / 1000000,
24    "gpt-5-nano": 0.4 / 1000000,
25    "o4-mini": 4.4 / 1000000,
26    "o4-mini-deep-research": 8 / 1000000,
27    "gpt-4.1": 8 / 1000000,
28    "gpt-4.1-mini": 1.6 / 1000000,
29    "gpt-4.1-nano": 0.4 / 1000000,
30    "gpt-4o": 10 / 1000000,
31    "o1-preview": 60 / 1000000,
32    "o1-mini": 4.4 / 1000000,
33    "o3-mini": 4.4 / 1000000,
34    "gpt-4o-2024-08-06": 10 / 1000000,
35    "gpt-4o-mini": 0.6 / 1000000,
36    "gpt-4o-mini-search-preview": 0.6 / 1000000,
37    "gpt-3.5-turbo": 1.5 / 1000000
38}
39
40model_cost_image = {
41    "normal": 0.04,
42    "high": 0.08,
43}
44
45
46def get_cost(response, model):
47    response_message = response.choices[0].message
48    completion_tokens = response.usage.completion_tokens
49    prompt_tokens = response.usage.prompt_tokens
50    try:
51        cost = model_cost_query[model] * prompt_tokens + model_cost_output[model] * completion_tokens
52    except Exception as e:
53        logger.error(f"Error calculating cost for model {model}: {e}")
54        cost = 0
55
56    return cost

model_cost_query = {'gpt-5': 1.25e-06, 'gpt-5-mini': 2.5e-07, 'gpt-5-nano': 5.0000000000000004e-08, 'o4-mini': 1.1e-06, 'o4-mini-deep-research': 2e-06, 'gpt-4.1': 2e-06, 'gpt-4.1-mini': 4.0000000000000003e-07, 'gpt-4.1-nano': 1.0000000000000001e-07, 'gpt-4o': 2.5e-06, 'o1-preview': 1.5e-05, 'o1-mini': 1.1e-06, 'o3-mini': 1.1e-06, 'gpt-4o-2024-08-06': 2.5e-06, 'gpt-4o-mini': 1.5e-07, 'gpt-4o-mini-search-preview': 1.5e-07, 'gpt-3.5-turbo': 5e-07}

dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2)

model_cost_output = {'gpt-5': 1e-05, 'gpt-5-mini': 2e-06, 'gpt-5-nano': 4.0000000000000003e-07, 'o4-mini': 4.4e-06, 'o4-mini-deep-research': 8e-06, 'gpt-4.1': 8e-06, 'gpt-4.1-mini': 1.6000000000000001e-06, 'gpt-4.1-nano': 4.0000000000000003e-07, 'gpt-4o': 1e-05, 'o1-preview': 6e-05, 'o1-mini': 4.4e-06, 'o3-mini': 4.4e-06, 'gpt-4o-2024-08-06': 1e-05, 'gpt-4o-mini': 6e-07, 'gpt-4o-mini-search-preview': 6e-07, 'gpt-3.5-turbo': 1.5e-06}

model_cost_image = {'normal': 0.04, 'high': 0.08}

def get_cost(response, model): View Source

47def get_cost(response, model):
48    response_message = response.choices[0].message
49    completion_tokens = response.usage.completion_tokens
50    prompt_tokens = response.usage.prompt_tokens
51    try:
52        cost = model_cost_query[model] * prompt_tokens + model_cost_output[model] * completion_tokens
53    except Exception as e:
54        logger.error(f"Error calculating cost for model {model}: {e}")
55        cost = 0
56
57    return cost