QuickSilver Pro vs OpenAI
For workloads where an open-source model is quality-equivalent, QuickSilver Pro is up to 30x cheaper than OpenAI. DeepSeek V4 Flash replaces GPT-4o-mini at ~73% lower cost; V3 replaces GPT-4o at ~16x lower output cost; V4 Pro replaces o3-mini at ~6x lower output cost; R1 replaces o1 at ~30x lower output 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 | 9 open-source LLMs (V4 Flash + Pro, V3, R1, Qwen 3.7 Max + 3.6 Plus + 3.6 + 3.5, Kimi K2.6) | GPT-4, o1/o3-mini, DALL-E, Whisper, TTS |
| Model weights | Open (MIT / Apache) | Closed |
| Cheap chat cost (GPT-4o-mini / DeepSeek V4 Flash) | $0.08 / $0.16 | $0.15 / $0.60 |
| General chat cost (GPT-4o / DeepSeek V3) | $0.16 / $0.616 | $2.50 / $10.00 |
| Premium reasoning cost (o3-mini / DeepSeek V4 Pro) | $0.348 / $0.696 | $1.10 / $4.40 |
| Top reasoning cost (o1 / DeepSeek R1) | $0.56 / $2.00 | $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 May 2026.
| Model | QSP input | QSP output | openai input | openai output | Savings |
|---|---|---|---|---|---|
| deepseek-v4-flash vs gpt-4o-mini | $0.08 | $0.16 | $0.15 | $0.60 | ~73% |
| deepseek-v3 vs gpt-4o | $0.16 | $0.616 | $2.50 | $10.00 | ~94% |
| deepseek-v4-pro vs o3-mini | $0.348 | $0.696 | $1.10 | $4.40 | ~84% |
| deepseek-r1 vs o1 | $0.56 | $2.00 | $15.00 | $60.00 | ~97% |
| qwen3.6-35b vs gpt-4o | $0.12 | $0.80 | $2.50 | $10.00 | ~92% |
| qwen3.5-35b vs gpt-4o | $0.111 | $0.80 | $2.50 | $10.00 | ~92% |
| kimi-k2.6 | $0.584 | $2.79 | — | — | specialist tier |
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
DeepSeek V4 Flash vs GPT-4o-mini: ~47% on input, ~73% on output. DeepSeek V3 vs GPT-4o: ~16x on input, ~16x on output. DeepSeek V4 Pro vs o3-mini: ~3x on input, ~6x on output. DeepSeek R1 vs o1: ~27x on input, ~30x on output. Same underlying task quality on most text-only benchmarks.
DeepSeek V4 Pro maps cleanly to o3-mini for premium reasoning workloads with long context (1M tokens vs o3-mini’s 200K), at $0.348/$0.696 vs $1.10/$4.40 — about 6x cheaper on output. Kimi K2.6 is in an Opus-class agentic / planning niche where OpenAI doesn’t have a clean analog — if your evals are picking Claude Opus, K2.6 at $0.584/$2.79 is the open-source comparable.
Yes, unchanged. Only the base_url + api_key + model change. Streaming, tool calling, json_schema strict mode, usage accounting — all supported. V4-wave models (V4 Flash, V4 Pro, Kimi K2.6) think by default; pass `reasoning: { enabled: false }` for V3-style chat.
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.
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.