Spaces:
Running
on
CPU Upgrade
Running
on
CPU Upgrade
File size: 8,650 Bytes
4d16728 220d3b8 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 |
"""
Models registry for the Auto-Analyst application.
This file serves as the single source of truth for all model information.
"""
# Model providers
PROVIDERS = {
"openai": "OpenAI",
"anthropic": "Anthropic",
"groq": "GROQ",
"gemini": "Google Gemini"
}
# Cost per 1K tokens for different models
MODEL_COSTS = {
"openai": {
"gpt-4.1": {"input": 0.002, "output": 0.008},
"gpt-4.1-mini": {"input": 0.0004, "output": 0.0016},
"gpt-4.1-nano": {"input": 0.00010, "output": 0.0004},
"gpt-4.5-preview": {"input": 0.075, "output": 0.15},
"gpt-4o": {"input": 0.0025, "output": 0.01},
"gpt-4o-mini": {"input": 0.00015, "output": 0.0006},
"o1": {"input": 0.015, "output": 0.06},
"o1-pro": {"input": 0.015, "output": 0.6},
"o1-mini": {"input": 0.00011, "output": 0.00044},
"o3": {"input": 0.001, "output": 0.04},
"o3-mini": {"input": 0.00011, "output": 0.00044},
"gpt-3.5-turbo": {"input": 0.0005, "output": 0.0015},
},
"anthropic": {
"claude-3-opus-latest": {"input": 0.015, "output": 0.075},
"claude-3-7-sonnet-latest": {"input": 0.003, "output": 0.015},
"claude-3-5-sonnet-latest": {"input": 0.003, "output": 0.015},
"claude-3-5-haiku-latest": {"input": 0.0008, "output": 0.0004},
},
"groq": {
"deepseek-r1-distill-llama-70b": {"input": 0.00075, "output": 0.00099},
"llama-3.3-70b-versatile": {"input": 0.00059, "output": 0.00079},
"llama3-8b-8192": {"input": 0.00005, "output": 0.00008},
"llama3-70b-8192": {"input": 0.00059, "output": 0.00079},
"mistral-saba-24b": {"input": 0.00079, "output": 0.00079},
"gemma2-9b-it": {"input": 0.0002, "output": 0.0002},
"qwen-qwq-32b": {"input": 0.00029, "output": 0.00039},
"meta-llama/llama-4-maverick-17b-128e-instruct": {"input": 0.0002, "output": 0.0006},
"meta-llama/llama-4-scout-17b-16e-instruct": {"input": 0.00011, "output": 0.00034},
"deepseek-r1-distill-qwen-32b": {"input": 0.00075, "output": 0.00099},
"llama-3.1-70b-versatile": {"input": 0.00059, "output": 0.00079},
},
"gemini": {
"gemini-2.5-pro-preview-03-25": {"input": 0.00015, "output": 0.001}
}
}
# Tiers based on cost per 1K tokens
MODEL_TIERS = {
"tier1": {
"name": "Basic",
"credits": 1,
"models": [
"llama3-8b-8192",
"llama-3.1-8b-instant",
"gemma2-9b-it",
"meta-llama/llama-4-scout-17b-16e-instruct",
"llama-3.2-1b-preview",
"llama-3.2-3b-preview",
"llama-3.2-11b-text-preview",
"llama-3.2-11b-vision-preview",
"llama3-groq-8b-8192-tool-use-preview"
]
},
"tier2": {
"name": "Standard",
"credits": 3,
"models": [
"gpt-4.1-nano",
"gpt-4o-mini",
"o1-mini",
"o3-mini",
"qwen-qwq-32b",
"meta-llama/llama-4-maverick-17b-128e-instruct"
]
},
"tier3": {
"name": "Premium",
"credits": 5,
"models": [
"gpt-4.1",
"gpt-4.1-mini",
"gpt-4.5-preview",
"gpt-4o",
"o1",
"o1-pro",
"o3",
"gpt-3.5-turbo",
"claude-3-opus-latest",
"claude-3-7-sonnet-latest",
"claude-3-5-sonnet-latest",
"claude-3-5-haiku-latest",
"deepseek-r1-distill-llama-70b",
"llama-3.3-70b-versatile",
"llama3-70b-8192",
"mistral-saba-24b",
"deepseek-r1-distill-qwen-32b",
"llama-3.2-90b-text-preview",
"llama-3.2-90b-vision-preview",
"llama-3.3-70b-specdec",
"llama2-70b-4096",
"llama-3.1-70b-versatile",
"llama-3.1-405b-reasoning",
"llama3-groq-70b-8192-tool-use-preview",
"gemini-2.5-pro-preview-03-25"
]
}
}
# Model metadata (display name, context window, etc.)
MODEL_METADATA = {
# OpenAI
"gpt-4.1": {"display_name": "GPT-4.1", "context_window": 128000},
"gpt-4.1-mini": {"display_name": "GPT-4.1 Mini", "context_window": 128000},
"gpt-4.1-nano": {"display_name": "GPT-4.1 Nano", "context_window": 128000},
"gpt-4o": {"display_name": "GPT-4o", "context_window": 128000},
"gpt-4.5-preview": {"display_name": "GPT-4.5 Preview", "context_window": 128000},
"gpt-4o-mini": {"display_name": "GPT-4o Mini", "context_window": 128000},
"gpt-3.5-turbo": {"display_name": "GPT-3.5 Turbo", "context_window": 16385},
"o1": {"display_name": "o1", "context_window": 128000},
"o1-pro": {"display_name": "o1 Pro", "context_window": 128000},
"o1-mini": {"display_name": "o1 Mini", "context_window": 128000},
"o3": {"display_name": "o3", "context_window": 128000},
"o3-mini": {"display_name": "o3 Mini", "context_window": 128000},
# Anthropic
"claude-3-opus-latest": {"display_name": "Claude 3 Opus", "context_window": 200000},
"claude-3-7-sonnet-latest": {"display_name": "Claude 3.7 Sonnet", "context_window": 200000},
"claude-3-5-sonnet-latest": {"display_name": "Claude 3.5 Sonnet", "context_window": 200000},
"claude-3-5-haiku-latest": {"display_name": "Claude 3.5 Haiku", "context_window": 200000},
# GROQ
"deepseek-r1-distill-llama-70b": {"display_name": "DeepSeek R1 Distill Llama 70b", "context_window": 32768},
"llama-3.3-70b-versatile": {"display_name": "Llama 3.3 70b", "context_window": 8192},
"llama3-8b-8192": {"display_name": "Llama 3 8b", "context_window": 8192},
"llama3-70b-8192": {"display_name": "Llama 3 70b", "context_window": 8192},
"mistral-saba-24b": {"display_name": "Mistral Saba 24b", "context_window": 32768},
"gemma2-9b-it": {"display_name": "Gemma 2 9b", "context_window": 8192},
"qwen-qwq-32b": {"display_name": "Qwen QWQ 32b | Alibaba", "context_window": 32768},
"meta-llama/llama-4-maverick-17b-128e-instruct": {"display_name": "Llama 4 Maverick 17b", "context_window": 128000},
"meta-llama/llama-4-scout-17b-16e-instruct": {"display_name": "Llama 4 Scout 17b", "context_window": 16000},
"llama-3.1-70b-versatile": {"display_name": "Llama 3.1 70b Versatile", "context_window": 8192},
# Gemini
"gemini-2.5-pro-preview-03-25": {"display_name": "Gemini 2.5 Pro", "context_window": 1000000},
}
# Helper functions
def get_provider_for_model(model_name):
"""Determine the provider based on model name"""
if not model_name:
return "Unknown"
model_name = model_name.lower()
return next((provider for provider, models in MODEL_COSTS.items()
if any(model_name in model for model in models)), "Unknown")
def get_model_tier(model_name):
"""Get the tier of a model"""
for tier_id, tier_info in MODEL_TIERS.items():
if model_name in tier_info["models"]:
return tier_id
return "tier1" # Default to tier1 if not found
def calculate_cost(model_name, input_tokens, output_tokens):
"""Calculate the cost for using the model based on tokens"""
if not model_name:
return 0
# Convert tokens to thousands
input_tokens_in_thousands = input_tokens / 1000
output_tokens_in_thousands = output_tokens / 1000
# Get model provider
model_provider = get_provider_for_model(model_name)
# Handle case where model is not found
if model_provider == "Unknown" or model_name not in MODEL_COSTS.get(model_provider, {}):
return 0
return (input_tokens_in_thousands * MODEL_COSTS[model_provider][model_name]["input"] +
output_tokens_in_thousands * MODEL_COSTS[model_provider][model_name]["output"])
def get_credit_cost(model_name):
"""Get the credit cost for a model"""
tier_id = get_model_tier(model_name)
return MODEL_TIERS[tier_id]["credits"]
def get_display_name(model_name):
"""Get the display name for a model"""
return MODEL_METADATA.get(model_name, {}).get("display_name", model_name)
def get_context_window(model_name):
"""Get the context window size for a model"""
return MODEL_METADATA.get(model_name, {}).get("context_window", 4096)
def get_all_models_for_provider(provider):
"""Get all models for a specific provider"""
if provider not in MODEL_COSTS:
return []
return list(MODEL_COSTS[provider].keys())
def get_models_by_tier(tier_id):
"""Get all models for a specific tier"""
return MODEL_TIERS.get(tier_id, {}).get("models", []) |