Most AI startups obsess over which model is best. The smart ones route to all of them.
Here's why.
Every AI model has a sweet spot:
deepseek-chat — $0.27/M, 64K context, fastest
qwen3-235b — 128K context, best multilingual
glm-4-plus — complex reasoning, Chinese native
kimi-k3 — 1M context, always thinks before answering
Pick one, and you're great at one thing. Use all four, and you're
great at everything — without paying premium prices for every request.
The routing pattern
import openai
client = openai.OpenAI(
api_key="mb-xxx",
base_url="https://aibridge-api.com/v1"
)
def route(task_type):
if task_type == "simple": return "deepseek-chat" # 90% of traffic
if task_type == "translate": return "qwen-max" # multilingual
if task_type == "reason": return "glm-4-plus" # complex
if task_type == "review": return "kimi-k3" # codebase
return "deepseek-chat"
client.chat.completions.create(
model=route(task),
messages=messages
)
Same library. Same response format. Same API key.
Switching models is changing one string.
Why this matters
A single-model stack has two failure modes:
Paying $15/M for yes/no answers
Crashing because your provider rate-limited you at 2 AM
Routing fixes both. Cheap model for classification, summaries, and
simple chat. Expensive model for the 10% of tasks that actually need
deep reasoning. And if any provider goes down — swap the model string
and redeploy. No downtime.
The cost math
Say your app does 1M requests/month:
- 900K simple → deepseek-chat at 0.27/M = 243
- 80K translate → qwen-plus at 0.40/M=32
- 20K reason → glm-4-plus at 7.10/M=142
Total: ~$420/month
If all 1M went to glm-4-plus: $7,100/month.
The routing pattern saves 94% and delivers the same quality where it
counts — because the cheap model is indistinguishable from the expensive
one on 90% of tasks.
Try it without committing
15 Chinese AI models. One OpenAI-compatible endpoint. Free playground —
no signup, 10 requests/day, all models including kimi-k3. GitHub OAuth
login if you want a key.





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