Disclosure: I built AiCredits to solve my own payment friction. This post is a transparent breakdown of the problem, the trade-offs, and why a proxy might make sense for testing. Not sponsored. Not hiding the markup.
DeepSeek has arguably the best price-to-performance ratio among major LLM APIs. At $0.28 per million input tokens for deepseek-chat, it's roughly 10x cheaper than GPT-4o.
But here's the friction most tutorials gloss over:
The Problem: Friction Before the First Token
Minimum top-up threshold — The official platform may require a higher minimum deposit than you want for testing. Sometimes you just want to spend $1 to verify the full flow works.
Payment method variability — DeepSeek accepts credit cards, PayPal, Apple Pay, and Google Pay, but what's actually available depends on your region. Users in Southeast Asia, Africa, or the Middle East often find some options missing or get hit with currency conversion delays.
No "try before you buy" — Most developers want to spend pocket change first, then scale up. Official platforms aren't designed for that micro-testing workflow.
The Options: An Honest Comparison
I've tried three approaches. Here's how they actually stack up:
| Metric | DeepSeek Official | OpenRouter | AiCredits (my proxy) |
|---|---|---|---|
| Minimum Top-up | Varies (often ≥$5) | $5 | $0.99 |
| Price per 1M input tokens | $0.28 (unbeatable) | ~$0.70–$1.00 | ~$5.00* |
| Payment Methods | Region-dependent | Credit Card, Crypto | Credit Card (Paddle) |
| Data Flow | Direct to DeepSeek | Proxy | Proxy |
| Best For | Production workloads | Multi-model projects | Testing & one-off scripts |
*The math: $0.99 gets you 200K tokens, so $4.95 per 1M tokens. That's roughly **18x the official price. You're paying for convenience and low minimums, not for high volume.
The Code (Drop-in Replacement)
If you're already using the OpenAI Python SDK, the switch is one line:
python
from openai import OpenAI
client = OpenAI(
api_key="your-aicredits-key",
base_url="https://aicreditsapi.com/v1" # <--- only this changes
)
response = client.chat.completions.create(
model="deepseek-chat", # or deepseek-reasoner
messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)
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