Hey everyone. I'm usually deep in AWS Cost Explorer or Azure Cost Management, but this LLM API pricing feels like a whole new world of unit economics. I'm trying to wrap my head around comparing token prices across providers, and it's not as straightforward as comparing vCPU-hour rates.
The main hurdle I'm hitting is the lack of standardization. Some providers price per **1M tokens**, others per **1K tokens**. Input and output tokens are often priced differently. Then you have the model tier (e.g., GPT-4 vs Claude 3 Opus vs a smaller open-weight model), which drastically changes both cost and capability.
To even start a comparison, I think you need to:
- Define your **typical task**. Is it short Q&A, long document summarization, or code generation? This determines your expected input/output ratio.
- Know your **expected volume**. Are you testing or planning for production scale?
- Decide on **quality tolerance**. Do you need the "smartest" output, or is a "good enough" model sufficient for the task?
I threw together a simple script to at least normalize the pricing per 1K output tokens for a hypothetical task (10K input tokens, 1K output tokens). This is crude, but it's a start.
```python
# Example rates per 1M tokens (input, output)
provider_rates = {
"openai_gpt4": {"input": 10.00, "output": 30.00}, # $ per 1M tokens
"anthropic_claude3_sonnet": {"input": 3.00, "output": 15.00},
"groq_llama3_70b": {"input": 0.59, "output": 0.79},
}
def calculate_cost(provider_rates, input_tokens, output_tokens):
cost = {}
for provider, rates in provider_rates.items():
input_cost = (input_tokens / 1_000_000) * rates["input"]
output_cost = (output_tokens / 1_000_000) * rates["output"]
cost[provider] = round(input_cost + output_cost, 4)
return cost
# For 10K input, 1K output tokens
task_tokens = {"input": 10000, "output": 1000}
print(calculate_cost(provider_rates, **task_tokens))
```
This would output the cost for that specific call. But this misses so much:
- Latency differences (a cheaper, slower model might hurt user experience).
- Effective quality per token (does a more expensive model get you the right answer in fewer output tokens?).
- Reliability and rate limiting under load.
So my question is: **Beyond a simple per-token script, what framework or metrics do you use for a true cost/performance comparison?** Do you run the same prompt batch against different providers and measure both cost *and* outcome quality? How do you factor in latency?