1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174"""Centralized LLM cost configuration and calculation.
This module provides a single source of truth for LLM pricing and cost calculations,
making it easy to update pricing when AWS Bedrock rates change.
Supported Models (as of January 2025):
Claude Haiku 4.5 via AWS Bedrock:
- Input: $1.00 per 1M tokens
- Output: $5.00 per 1M tokens
Claude Sonnet 4.5 via AWS Bedrock:
- Input: $3.00 per 1M tokens
- Output: $15.00 per 1M tokens
Prices are stored in cents per token for precision and to avoid floating point errors
when working with small per-token costs.
"""
from dataclasses import dataclass
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from src.agents.model_tiers import ModelTier
@dataclass
class LLMPricing:
"""LLM pricing configuration in cents per token.
Stores pricing at the per-token level in cents to maintain precision
and avoid floating point errors in cost calculations.
Attributes:
input_cost_per_token_cents: Cost per input token in cents
output_cost_per_token_cents: Cost per output token in cents
model_name: Human-readable model identifier for reference
Example:
>>> # Claude Haiku 4.5 pricing
>>> pricing = LLMPricing(
... input_cost_per_token_cents=0.0001, # $1.00/1M tokens
... output_cost_per_token_cents=0.0005, # $5.00/1M tokens
... model_name="Claude Haiku 4.5 (Bedrock)"
... )
"""
input_cost_per_token_cents: float
output_cost_per_token_cents: float
model_name: str = "Unknown Model"
# Default pricing for Claude Haiku 4.5 via AWS Bedrock (EFFICIENT tier)
# Updated: January 2025
# Source: https://aws.amazon.com/bedrock/pricing/
DEFAULT_PRICING = LLMPricing(
input_cost_per_token_cents=0.0001, # $1.00 per 1M tokens = $0.000001/token = 0.0001 cents/token
output_cost_per_token_cents=0.0005, # $5.00 per 1M tokens = $0.000005/token = 0.0005 cents/token
model_name="Claude Haiku 4.5 (Bedrock)",
)
# Pricing for Claude Sonnet 4.5 via AWS Bedrock (REASONING tier)
# Updated: January 2025
# Source: https://aws.amazon.com/bedrock/pricing/
SONNET_PRICING = LLMPricing(
input_cost_per_token_cents=0.0003, # $3.00 per 1M tokens = $0.000003/token = 0.0003 cents/token
output_cost_per_token_cents=0.0015, # $15.00 per 1M tokens = $0.000015/token = 0.0015 cents/token
model_name="Claude Sonnet 4.5 (Bedrock)",
)
# Alias for clarity
HAIKU_PRICING = DEFAULT_PRICING
def get_pricing_for_tier(tier: "ModelTier") -> LLMPricing:
"""Get pricing configuration for a model tier.
Args:
tier: The model tier (REASONING or EFFICIENT)
Returns:
LLMPricing configuration for the tier
Example:
>>> from src.agents.model_tiers import ModelTier
>>> pricing = get_pricing_for_tier(ModelTier.REASONING)
>>> print(pricing.model_name)
'Claude Sonnet 4.5 (Bedrock)'
"""
# Import here to avoid circular imports
from src.agents.model_tiers import ModelTier
if tier == ModelTier.REASONING:
return SONNET_PRICING
return HAIKU_PRICING
def calculate_estimated_cost(
input_tokens: int,
output_tokens: int,
pricing: LLMPricing = DEFAULT_PRICING,
) -> float:
"""Calculate estimated LLM cost in cents based on token usage.
Uses the provided pricing configuration (or defaults to Claude Haiku 4.5 pricing)
to calculate the total cost in cents.
Args:
input_tokens: Number of input tokens consumed
output_tokens: Number of output tokens generated
pricing: LLM pricing configuration (defaults to Claude Haiku 4.5)
Returns:
Estimated cost in cents (float)
Example:
>>> # Calculate cost for 1000 input tokens and 100 output tokens
>>> cost = calculate_estimated_cost(1000, 100)
>>> print(f"Cost: ${cost/100:.6f}")
Cost: $0.001500
>>> # Calculate with custom pricing
>>> custom_pricing = LLMPricing(
... input_cost_per_token_cents=0.0002,
... output_cost_per_token_cents=0.001,
... model_name="Custom Model"
... )
>>> cost = calculate_estimated_cost(1000, 100, custom_pricing)
>>> print(f"Cost: ${cost/100:.6f}")
Cost: $0.003000
>>> # Calculate with Sonnet pricing
>>> cost = calculate_estimated_cost(1000, 100, SONNET_PRICING)
>>> print(f"Cost: ${cost/100:.6f}")
Cost: $0.004500
"""
input_cost = input_tokens * pricing.input_cost_per_token_cents
output_cost = output_tokens * pricing.output_cost_per_token_cents
return input_cost + output_cost
def format_cost_dollars(cost_cents: float) -> str:
"""Format cost in cents as dollar string with appropriate precision.
Args:
cost_cents: Cost in cents
Returns:
Formatted cost string (e.g., "$0.001500" or "$1.23")
Example:
>>> format_cost_dollars(0.15)
'$0.0015'
>>> format_cost_dollars(150.0)
'$1.50'
"""
dollars = cost_cents / 100
if dollars < 0.01:
# Use more precision for very small costs
return f"${dollars:.6f}"
else:
return f"${dollars:.2f}"
__all__ = [
"LLMPricing",
"DEFAULT_PRICING",
"SONNET_PRICING",
"HAIKU_PRICING",
"get_pricing_for_tier",
"calculate_estimated_cost",
"format_cost_dollars",
]