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def estimate_price(num_of_episodes, in_context_len,tokens_per_step, n_steps, last_n, model, feed_episode_history):
max_context_size = 10e10
price_per_1k_big_context = 0
if model == "text-ada-001":
price_per_1k = 0.0004
elif model == "text-curie-001":
price_per_1k = 0.003
elif model == "text-davinci-003":
price_per_1k = 0.02
elif model == "gpt-3.5-turbo-0301":
price_per_1k = 0.0015
max_context_size = 4000
price_per_1k_big_context = 0.003
elif model == "gpt-3.5-turbo-instruct-0914":
price_per_1k = 0.0015
max_context_size = 4000
price_per_1k_big_context = 0.003
elif model == "gpt-3.5-turbo-0613":
price_per_1k = 0.0015
max_context_size = 4000
price_per_1k_big_context = 0.003
elif model == "gpt-4-0314":
price_per_1k = 0.03
max_context_size = 8000
price_per_1k_big_context = 0.06
elif model == "gpt-4-0613":
price_per_1k = 0.03
max_context_size = 8000
price_per_1k_big_context = 0.06
else:
print(f"Price for model {model} not found.")
price_per_1k = 0
# check if the maximum context size if bigger than default (4k gpt-3.5;8k gpt-4) and update the price accordingly
if (
feed_episode_history and in_context_len+n_steps*tokens_per_step
) or (
not feed_episode_history and in_context_len+last_n * tokens_per_step > max_context_size
):
# context is bigger, update the price
assert "gpt-4" in model or "gpt-3.5" in model
price_per_1k = price_per_1k_big_context
if feed_episode_history:
total_tokens = num_of_episodes*(in_context_len + tokens_per_step*sum(range(n_steps)))
else:
total_tokens = num_of_episodes*n_steps*(in_context_len + last_n*tokens_per_step)
price = (total_tokens/1000)*price_per_1k
return total_tokens, price
if __name__ == "__main__":
total_tokens, price = estimate_price()
print("tokens:", total_tokens)
print("price:", price) |