QuickSilver Pro vs OpenAI
For workloads where an open-source model is quality-equivalent, QuickSilver Pro is up to 30× lower than OpenAI. DeepSeek V4 Flash replaces GPT-4o-mini at ~63% lower cost; V4 Pro replaces o3-mini at ~6x lower output cost. For audio and the Assistants API — stay on OpenAI. Vision we do have: 23 of our 40 models accept image input. 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 | Open-source LLMs (DeepSeek V4 Flash + Pro, Qwen 3.7 Max + 3.6 Plus + 3.6, Kimi K2.6 + K2.7 Code, GLM 5.2) | GPT-4, o1/o3-mini, DALL-E, Whisper, TTS |
| Model weights | Open (MIT / Apache) | Closed |
| Low-cost chat cost (GPT-4o-mini / DeepSeek V4 Flash) | $0.112 / $0.224 | $0.14 / $0.60 |
| Premium reasoning cost (o3-mini / DeepSeek V4 Pro) | $0.435 / $0.87 | $1.10 / $4.40 |
| Vision (image input) | 23 of 40 models | Yes (GPT-4o) |
| Audio (Whisper / TTS) | No | Yes |
| Image generation | Gemini 3 Pro Image and FLUX.2 Pro | Yes (DALL-E) |
| 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)
Competitor list prices as published by each provider.
| Model | QSP input | QSP output | openai input | openai output | vs. list |
|---|---|---|---|---|---|
| deepseek-v4-flash vs gpt-4o-mini | $0.112 | $0.224 | $0.14 | $0.60 | ~63% |
| deepseek-v4-pro vs o3-mini | $0.435 | $0.87 | $1.10 | $4.40 | ~84% |
| qwen3.6-35b vs gpt-4o | $0.112 | $0.80 | $2.50 | $10.00 | ~92% |
| kimi-k2.6 | $0.5472 | $2.728 | — | — | 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-v4-pro",
messages=[{"role": "user", "content": "Hi"}],
)FAQ
DeepSeek V4 Flash vs GPT-4o-mini: ~25% on input, ~63% on output. DeepSeek V4 Pro vs o3-mini: ~3x on input, ~6x 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 $0.435 / $0.87 vs $1.10/$4.40 — about 6× lower 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 $0.5472 / $2.728 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 non-thinking chat.
Whisper / TTS, the Assistants API, embeddings, and any task where GPT-4 measurably beats DeepSeek V4 on your evals. Vision and image generation are not reasons to stay: 23 of our 40 models take image input, and Gemini 3 Pro Image and FLUX.2 Pro generate images.
Yes — run two OpenAI SDK instances, one per provider, and route per-request by task. Many teams do exactly this: OpenAI for audio and Assistants, QSP for chat, vision and image generation. The hybrid bill is typically 10-30% of the all-OpenAI bill.