How much cheaper is QuickSilver Pro than OpenAI?

For workloads where an open-source model is quality-equivalent, QuickSilver Pro is 10-35x cheaper. DeepSeek V3 on QSP is $0.24 input / $0.70 output per 1M tokens vs GPT-4o at $2.50 / $10.00 — roughly 10x and 14x cheaper. DeepSeek R1 on QSP is $0.40 / $1.70 vs OpenAI o1 at $15 / $60 — 37x and 35x cheaper. For closed-model capabilities (vision, tool-using assistants, GPT-4-class creative writing), OpenAI still wins.

Can I migrate from OpenAI to QuickSilver Pro without rewriting my code?

Yes, if you only use text chat completions. Change the OpenAI SDK base_url from https://api.openai.com/v1 to https://api.quicksilverpro.io/v1, swap the API key, and change model IDs from gpt-4o to deepseek-v3 (or o1 to deepseek-r1). Streaming, tool calling, and json_schema strict mode all work. If you use vision, embeddings, Whisper, TTS, DALL-E, or the Assistants API, those are OpenAI-exclusive — keep OpenAI for those calls.

Will DeepSeek V3 match GPT-4 quality on my task?

On published benchmarks (MMLU, HumanEval, MATH, GPQA), DeepSeek V3 is within a few points of GPT-4o on most. Real-world performance varies by task. For general chat, coding, structured JSON output, and multilingual text, DeepSeek V3 is competitive. For nuanced creative writing or domain-specific tasks where GPT-4 has been fine-tuned in production, differences can appear. Always benchmark on your own evals before committing.

Comparison

QuickSilver Pro vs OpenAI

Q: When should I stay on OpenAI?

Stay on OpenAI when you need: vision inputs (GPT-4o image understanding), audio (Whisper transcription, TTS voices), image generation (DALL-E 3, gpt-image-1), the Assistants API with built-in tools (code interpreter, file search), or workloads where GPT-4-class generation quality is genuinely better than DeepSeek V3 for your specific tasks. Benchmark on your own evals before switching.

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)

Model-for-task comparison. OpenAI pricing from platform.openai.com as of April 2026.

Task	QSP model	QSP $/M	OpenAI model	OpenAI $/M	QSP saves
General chat / coding (input)	deepseek-v3	$0.24	gpt-4o	$2.50	~90%
General chat / coding (output)	deepseek-v3	$0.70	gpt-4o	$10.00	~93%
Reasoning / math (input)	deepseek-r1	$0.40	o1	$15.00	~97%
Reasoning / math (output)	deepseek-r1	$1.70	o1	$60.00	~97%
Long-context RAG (input)	qwen3.5-35b	$0.13	gpt-4o	$2.50	~95%

For a reasoning-heavy workload (200k input + 2M output R1 tokens per day), the bill is $3.48 on QuickSilver Pro vs $123 on OpenAI o1 — roughly $3,600/month saved on a single workload.

Migration — two lines

The OpenAI Python / Node / Swift SDK works unchanged. Swap the base URL and API key, rename model IDs, done.

Before · OpenAI

from openai import OpenAI

client = OpenAI(
    # default base_url is api.openai.com/v1
    api_key=os.environ["OPENAI_API_KEY"],
)

r = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hi"}],
)

After · QuickSilver Pro

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"}],
)

Model ID mapping (when task maps):

gpt-4o, gpt-4-turbo → deepseek-v3 (general chat, coding, JSON)

o1, o1-preview → deepseek-r1 (reasoning, math)

gpt-4o (long context) → qwen3.5-35b (262K window, cheaper input)

Do NOT migrate: vision requests, Whisper, TTS, DALL-E, Assistants API, embeddings — keep OpenAI for those.

Honest tradeoffs

Migrate to QuickSilver Pro when

›Your workload is primarily text chat completions (coding assistants, structured output, RAG summarization).
›DeepSeek V3 or R1 benchmarks within a few points of GPT-4o / o1 on your own evals.
›Cost matters — especially for reasoning workloads where the 35x gap compounds into real money.
›You want open-weight models (auditability, no sudden deprecation, portability).

Stay on OpenAI when

›Vision — image input via GPT-4o is OpenAI-exclusive.
›Audio — Whisper ASR, TTS voices.
›Image generation — DALL-E 3, gpt-image-1.
›Assistants API — code interpreter, file search, built-in tool execution.
›GPT-4 produces measurably better output on your specific task (benchmark before switching).
›Embeddings (text-embedding-3-small/large). QSP doesn't serve embeddings yet.

Most teams find a hybrid pattern works: OpenAI for the closed-model-only features, QuickSilver Pro for the 80% of traffic that's plain text chat. The hybrid bill is often a small fraction of the all-OpenAI bill.

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.

Open-source alternatives too

OpenRouter's open-source: 5-10× cheaper → Together AI — open models, no GPT-4o tax → Fireworks — same models, 10× less → Every OpenAI alternative compared →