QuickSilver Pro vs OpenAI
For workloads where an open-source model is quality-equivalent, QuickSilver Pro is 10-35x cheaper than OpenAI. DeepSeek V3 replaces GPT-4o at ~10x lower cost; DeepSeek R1 replaces o1 at ~35x lower cost. For vision, audio, image generation, and the Assistants API — stay on OpenAI. This page is honest about which parts of OpenAI are worth their premium and which aren't.
At a glance
| Feature | QuickSilver Pro | openai |
|---|---|---|
| Catalog | 3 open-source LLMs | GPT-4, o1, o-series, DALL-E, Whisper, TTS |
| Model weights | Open (MIT / Apache) | Closed |
| Text chat cost (GPT-4o / DeepSeek V3) | $0.24 / $0.70 | $2.50 / $10.00 |
| Reasoning cost (o1 / DeepSeek R1) | $0.40 / $1.70 | $15.00 / $60.00 |
| Vision (image input) | No | Yes (GPT-4o) |
| Audio (Whisper / TTS) | No | Yes |
| Image generation (DALL-E) | No | Yes |
| Assistants API + built-in tools | No | Yes |
| OpenAI-compatible chat + tools + JSON | Yes | Yes (original) |
| Minimum top-up | $5 | $5 |
Pricing (per million tokens, USD)
Public list prices as of April 2026.
| Model | QSP input | QSP output | openai input | openai output | Savings |
|---|---|---|---|---|---|
| deepseek-v3 | $0.24 | $2.50 | ~90% | ||
| deepseek-v3 | $0.70 | $10.00 | ~93% | ||
| deepseek-r1 | $0.40 | $15.00 | ~97% | ||
| deepseek-r1 | $1.70 | $60.00 | ~97% | ||
| qwen3.5-35b | $0.13 | $2.50 | ~95% |
Migration — two lines
from openai import OpenAI
client = OpenAI(
base_url="https://api.quicksilverpro.io/v1",
api_key=os.environ["QSP_KEY"],
)
r = client.chat.completions.create(
model="deepseek-v3",
messages=[{"role": "user", "content": "Hi"}],
)FAQ
How much cheaper is QSP than OpenAI?
DeepSeek V3 vs GPT-4o: ~10x on input, ~14x on output. DeepSeek R1 vs o1: ~37x on input, ~35x on output. Same underlying task quality on most text-only benchmarks.
Can I keep using the OpenAI SDK?
Yes, unchanged. Only the base_url + api_key + model change. Streaming, tool calling, json_schema strict mode, usage accounting — all supported.
When should I stay on OpenAI?
Vision inputs, Whisper / TTS, DALL-E, the Assistants API, embeddings, and any task where GPT-4 measurably beats DeepSeek V3 on your evals. For text-only chat that passes your evals, QSP.
Can I mix OpenAI and QSP in one app?
Yes — run two OpenAI SDK instances, one per provider, and route per-request by task. Many teams do exactly this: OpenAI for vision / audio / Assistants, QSP for the 80% of traffic that's plain text. The hybrid bill is typically 10-30% of the all-OpenAI bill.