Stop paying premium prices for simple AI tasks.

#ai #api #llm #programming

I analyzed 50,000 API calls from production. Here's what I found.

The breakdown

42% were classification tasks ("Is this positive or negative?")
31% were summarization ("Summarize this email thread")
18% were simple generation ("Write a tweet about X")
9% were complex reasoning ("Debug this race condition")

Every single one went to the same $7/M model.

A "yes/no" classification costs the same as debugging a 500-line function.
That's not just wasteful — it's lazy architecture.

The fix: two-line routing

import openai
client = openai.OpenAI(
    api_key="mb-xxx",
    base_url="https://aibridge-api.com/v1"
)

model = "deepseek-chat" if task in ["classify","summarize","simple"]
                         else "glm-4-plus"
client.chat.completions.create(model=model, messages=messages)

Same endpoint. Same code. Different model string for different workloads.

The numbers

Task	Calls/month	Old cost	New cost
Classify (42%)	21,000	$1,491	$56
Summarize (31%)	15,500	$1,101	$418
Generate (18%)	9,000	$639	$243
Reason (9%)	4,500	$320	$320
Total	50,000	$3,551	$1,037

71% cost reduction. Quality unchanged — because the cheap model handles classification,
summarization, and simple generation indistinguishably from the expensive one.

The one-line migration

curl https://aibridge-api.com/v1/chat/completions \
  -H "Authorization: Bearer mb-xxx" \
  -d '{"model":"deepseek-chat","messages":[...]}'

curl https://aibridge-api.com/v1/chat/completions \
  -H "Authorization: Bearer mb-xxx" \
  -d '{"model":"kimi-k3","messages":[...]}'

DeepSeek (0.27/M).Qwen(from0.40/M). GLM-4 Plus ($7.10/M). Kimi K3 1M context.
Same key. Same endpoint. Change one string.

Free playground — try all 15 models, no signup:
→ aibridge-api.com/playground.html