Comparison
QuickSilver Pro vs Azure OpenAI Service
Azure OpenAI Service runs the closed OpenAI catalog (GPT-4o, o1, o3-mini) on Microsoft infrastructure with Azure-native compliance, Private Link, and Entra ID auth. For workloads where an open-source model is quality-equivalent, QuickSilver Pro serves the DeepSeek V4 wave (V4 Flash + Pro) at 6x–30x lower output cost and exposes them through the same OpenAI SDK — no resource group provisioning, no AAD setup, no Cognitive Services quota requests.
At a glance
| Feature | QuickSilver Pro | Azure OpenAI |
|---|---|---|
| Model catalog | Open-source LLMs (DeepSeek V4 Flash + Pro, Qwen 3.7 Max + 3.6 Plus + 3.6, Kimi K2.6) | Azure OpenAIGPT-4o, o1, o3-mini, GPT-4o-mini (closed) |
| Model weights | Open (MIT / Apache) | Closed |
| Low-cost chat output (GPT-4o-mini / V4 Flash) | $0.18 / 1M | $0.60 / 1M |
| Premium reasoning output (o3-mini / V4 Pro) | $2.10 / 1M | $4.40 / 1M |
| API setup | Sign up, paste key | Azure OpenAIProvision resource, request quota, AAD |
| Private Link / Entra ID / Sentinel | No | Yes |
Pricing (per million tokens, USD)
Competitor list prices as published by each provider.No check date
| Model | QSP input | QSP output | Azure OpenAI input | Azure OpenAI output | vs. list | QSP vs Azure OpenAI (Azure) · in / out | |
|---|---|---|---|---|---|---|---|
| deepseek-v4-flash vs gpt-4o-mini | $0.06 | $0.18 | $0.15 | $0.60 | compare token rates | QSP $0.06 / $0.18 | Azure $0.15 / $0.60 |
| deepseek-v4-pro vs o3-mini | $0.70 | $2.10 | $1.10 | $4.40 | ~52% output | QSP $0.70 / $2.10 | Azure $1.10 / $4.40 |
| qwen3.6-35b vs gpt-4o | $0.05 | $0.70 | $2.50 | $10.00 | compare token rates | QSP $0.05 / $0.70 | Azure $2.50 / $10.00 |
QSP columns come from the live catalog at build time; Azure OpenAI columns are the rates Azure OpenAI publishes. No check date is recorded for them.
Migration - two lines
import os
from openai import OpenAI
# Was: AzureOpenAI(azure_endpoint=..., api_version=..., api_key=...)
client = OpenAI(
base_url="https://api.quicksilverpro.io/v1",
api_key=os.environ["QSP_KEY"],
)
r = client.chat.completions.create(
model="deepseek-v4-pro", # or deepseek-v4.1-flash, qwen3.8-flash-next, ...
messages=[{"role": "user", "content": "Hi"}],
)The two changed lines are marked.
FAQ
Yes — the OpenAI SDK is the same shape Azure exposes. Change azure_endpoint to a plain base_url=https://api.quicksilverpro.io/v1, drop the deployment-name indirection (use the model ID directly: deepseek-v4-flash, deepseek-v4-pro, etc.), and supply your QSP key. Streaming, tool calling, and usage accounting all work. JSON schema strict mode is model-dependent — the Claude models do not support it, so use a tool with strict: true there.
When AAD auth, Private Link, Sentinel logging, or Microsoft Compliance Manager mappings are non-negotiable. Also when you need closed-model capabilities (real-time audio, DALL-E, the Assistants API) or when GPT-4 measurably beats DeepSeek V4 on your evals. QuickSilver Pro is for the chat / coding / RAG slice where open-source matches.
On the direct quality maps: GPT-4o-mini→V4 Flash is ~71% lower on output. o3-mini→V4 Pro is ~6× lower on output. Real-world bills typically land at 10–20% of Azure OpenAI for traffic that re-routes cleanly to open-source.
QuickSilver Pro infrastructure is hosted on dedicated bare-metal in Europe (OVH) with US edge. Region pinning is available on teams plans for data-residency requirements. For full Azure-region-locked inference with sovereign-cloud controls, Azure OpenAI is the right tool.
Start with your own key
Change two lines. Compare current per-token prices before your first call.