How to Cut AI Inference Costs With DeepSeek V5
Routing routine traffic to DeepSeek V5 at roughly $0.30 per million input tokens can cut inference spend by 80-90% versus flagship models, provided you keep hard tasks on the expensive tier. The saving is real; the risk is silently degrading output quality on jobs that needed the stronger model.
Cost engineering here is less about finding the cheapest model and more about deciding which requests deserve the premium one.
How do you decide which requests to route?
Classify by task type, not by user sentiment. Summarization, classification, extraction, formatting and short code edits are safe on a cheaper model. Multi-step reasoning, long-context analysis and anything where a wrong answer is expensive stay on the premium model.
| Task | Route to |
|---|---|
| Classification / tagging | DeepSeek V5 |
| Text extraction | DeepSeek V5 |
| Short code edits | DeepSeek V5 |
| Complex agent planning | Premium model |
| Legal / medical advice | Premium model |
How do you implement the routing?
Since V5 exposes an OpenAI-compatible API, a router is a few dozen lines. Send every request through a gateway that inspects the prompt or a task tag, then dispatches to the right endpoint.
- Tag each request type at the call site — do not guess from prompt text alone.
- Log token counts and cost per request from day one.
- Sample 5% of cheap-model outputs and review quality weekly.
- Move a task type back to premium the moment the sample shows degradation.
Where does caching multiply the saving?
If you send the same system prompt or document repeatedly, prompt caching cuts the input cost dramatically. A support bot with a 5,000-token instruction block re-sent on every message benefits far more from caching than from model choice.
Structure prompts so the stable part comes first. That is what the cache keys on. Reordering the same content can invalidate the cache and erase the gain.
What are the hidden costs of going cheap?
Retries. A cheaper model that fails 10% more often and needs a second pass can cost more in engineering time than it saves in tokens. Latency matters too: reasoning mode is slower, and a slow free-ish model can be worse for user experience than a fast expensive one.
Output quality on edge cases is the real exposure. Test on your worst inputs, not your average ones.
What realistic saving should you expect?
Teams that move 70-80% of traffic to a cheap tier and keep 20-30% premium typically see total spend drop 60-85%. If your workload is mostly complex reasoning, expect far less. Measure before you promise a number to anyone.
Key Takeaways
- Route classification, extraction and short edits to DeepSeek V5; keep complex reasoning premium.
- The OpenAI-compatible API makes a routing gateway straightforward.
- Prompt caching heavily rewards stable system-prompt prefixes.
- Retries and latency can erase token savings; review sampled outputs weekly.
Last updated: September 2026.
