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 ~71% 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: 38 of our 83 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 | Gemini 4 Argon, Muse Wonder, GPT-6.1 Sol, GPT-6 Luna, Claude Opus 5.5, Claude Sonnet 5.5, Claude Haiku 5.5, Gemini 3.8 Flash, Mistral Large 4, DeepSeek V4.1 Flash, Qwen3.8 Max, Kimi K3, GLM 5.3 and Grok 4.7 | 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) | $0.06 / $0.18 | $0.15 / $0.60 |
| Premium reasoning cost (o3-mini / DeepSeek V4 Pro) | $0.70 / $2.10 | $1.10 / $4.40 |
| Vision (image input) | 38 of 83 models | Yes (GPT-4o) |
| Audio (Whisper / TTS) | No | Yes |
| Image generation | Nano Banana Pro (Gemini 3 Pro Image), Nano Banana 2.1, FLUX.2 Pro, FLUX.1 Schnell, SDXL Turbo, FLUX.2 Klein, Qwen Image 3.0, Seedream 5.0 Pro, Seedream 4, Bria FIBO 1.5, GPT Image 2, GPT Image 2.5 Flare and GPT Image 2.5 Sunburst | Yes (DALL-E) |
| Assistants API + built-in tools | No | Yes |
| OpenAI-compatible chat + tools + JSON | Yes — json_schema varies by model | 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 | $0.06 | $0.18 | $0.15 | $0.60 | compare token rates |
| deepseek-v4-pro vs o3-mini | $0.70 | $2.10 | $1.10 | $4.40 | ~52% output |
| qwen3.6-35b vs gpt-4o | $0.05 | $0.70 | $2.50 | $10.00 | compare token rates |
| kimi-k2.6 | $0.45 | $1.95 | — | — | 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: ~43% on input, ~71% on output. DeepSeek V4 Pro vs o3-mini: ~1.6x on input, ~2x 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 $0.70 / $2.10 vs $1.10/$4.40 — about 2× 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 $0.45 / $1.95 is the open-source comparable.
Yes, unchanged. Only the base_url + api_key + model change. Streaming, tool calling, usage accounting — all supported. json_schema strict mode is model-dependent: the Claude models do not support it, so a schema there is advisory and the enforced path is a tool with `strict: true`. 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: 38 of our 83 models take image input, and Nano Banana Pro (Gemini 3 Pro Image), Nano Banana 2.1, FLUX.2 Pro, FLUX.1 Schnell, SDXL Turbo, FLUX.2 Klein, Qwen Image 3.0, Seedream 5.0 Pro, Seedream 4, Bria FIBO 1.5, GPT Image 2, GPT Image 2.5 Flare and GPT Image 2.5 Sunburst 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.
Start with your own key
Change two lines. Compare current per-token prices before your first call.
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