QuickSilver Pro vs Fireworks AI
Fireworks AI runs its own GPU fleet and sets premium prices for DeepSeek — $3.00 / $8.00 per 1M tokens on R1. QuickSilver Pro serves the same model at $0.40 / $1.70. On DeepSeek V3 we're ~20% cheaper; on R1, ~79% cheaper on output. Same OpenAI-compatible surface, two-line migration.
At a glance
| Feature | QuickSilver Pro | Fireworks AI |
|---|---|---|
| Catalog focus | 3 open-source models | Many open models + vision + fine-tuning |
| DeepSeek R1 output price | $1.70 / 1M | $8.00 / 1M |
| DeepSeek V3 output price | $0.70 / 1M | $0.90 / 1M |
| Fine-tuning · deployments | No | Yes |
| FireFunction V2 (tool calling model) | No | Yes |
| Image · audio models | No | Yes |
| OpenAI-compatible chat | Yes | Yes |
| Minimum top-up | $5 | Varies |
Pricing (per million tokens, USD)
Public list prices as of April 2026 on the shared open-source models.
| Model | QSP input | QSP output | Fireworks input | Fireworks output | Output savings |
|---|---|---|---|---|---|
| DeepSeek V3 | $0.24 | $0.70 | $0.30 | $0.90 | ~22% |
| DeepSeek R1 | $0.40 | $1.70 | $3.00 | $8.00 | ~79% |
| Qwen3.5-35B-A3B | $0.13 | $1.00 | Comparable | — | |
For an agentic workload running DeepSeek R1 at 500k input + 2M output tokens per day, the daily bill is $3.60 on QuickSilver Pro vs $17.50 on Fireworks AI.
Migration — two lines
from openai import OpenAI
client = OpenAI(
base_url="https://api.fireworks.ai/inference/v1",
api_key=os.environ["FIREWORKS_KEY"],
)
r = client.chat.completions.create(
model="accounts/fireworks/models/deepseek-r1",
messages=[{"role": "user", "content": "Hi"}],
)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-r1",
messages=[{"role": "user", "content": "Hi"}],
)accounts/fireworks/models/deepseek-v3 → deepseek-v3accounts/fireworks/models/deepseek-r1 → deepseek-r1accounts/fireworks/models/qwen3.5-35b-a3b → qwen3.5-35bHonest tradeoffs
- ›You need DeepSeek R1 at a cost-at-scale price point.
- ›Chat completions on DeepSeek V3, R1, or Qwen3.5-35B-A3B is your whole workload.
- ›You want pay-as-you-go with a $5 minimum.
- ›You use FireFunction V2 or their fine-tuned tool-calling models.
- ›Dedicated deployments or fine-tuning are part of your stack.
- ›You need image, audio, or Llama-family models.
- ›You use their first-party Whisper or Stable Diffusion endpoints — we don't serve ASR or image generation.
- ›You host LoRA adapters or use their fine-tuning service to ship task-specialised variants at serverless prices.
- ›You're building compound AI systems (f1 / compound models) where multiple models get orchestrated server-side in a single call.
FAQ
How much cheaper on DeepSeek R1?
~87% on input, ~79% on output. Fireworks charges $3.00/$8.00 per 1M tokens for R1; QuickSilver Pro charges $0.40/$1.70.
How do I migrate?
Two lines: change base_url to api.quicksilverpro.io/v1, swap API key, drop the accounts/fireworks/models/ prefix from model IDs.
Is latency comparable?
Within 10% on p50 for V3 and Qwen; slightly higher on R1. Live per-model latency is at quicksilverpro.io/status.
Do you support FireFunction V2?
No. FireFunction V2 is Fireworks' proprietary fine-tuned model; it is not in the QuickSilver Pro catalog. For tool calling, DeepSeek V3 and Qwen3.5-35B-A3B both support the OpenAI tools / function calling API.
Monthly cost walkthrough
A long-context RAG pipeline — document Q&A with large retrieved-chunk prompts, mostly DeepSeek V3 for generation, with R1 bursts for the hardest questions. Monthly footprint: 80M input tokens and 12M output tokens on V3, plus 2M input / 0.5M output on R1.
V3 80M × $0.24 = $19.20
V3 12M × $0.70 = $ 8.40
R1 2M × $0.40 = $ 0.80
R1 0.5M × $1.70 = $ 0.85
—————————————————————
Total = $29.25/mo
V3 80M × $0.30 = $24.00 V3 12M × $0.90 = $10.80 R1 2M × $3.00 = $ 6.00 R1 0.5M × $8.00 = $ 4.00 ————————————————————— Total = $44.80/mo
That's $15.55 saved each month, ~35% off. The V3 input line dominates at this volume (high input : output ratio is typical for RAG), but the R1 bursts still contribute outsized savings per token. Over a year this pipeline saves ~$186 without changing retrieval quality or prompt structure.
Uptime & reliability
QuickSilver Pro is in a bridge phase: requests route across multiple upstream inference providers serving the same open-source weights. If one upstream degrades, the router falls back. Per-model availability and p50 / p95 latency are published on our status page. Q2 2026 we move to our own GPU capacity, at which point we'll publish firmer SLOs.
Fireworks AI runs its own GPU fleet and publishes a status page at status.fireworks.ai with uptime and incident history. They're a first-party operator end to end — good for latency tuning and dedicated-deployment predictability. For workloads where p99 tail latency or a contractual SLA is a hard requirement, running on a first-party fleet is the conservative choice. Our bet is that for most developer teams on serverless chat, the pricing delta outweighs the phase difference — but be honest about your own requirements before switching.
Other DeepSeek R1 resellers
Try it on $1 free credits
OpenAI SDK unchanged. Change the base URL, change the key, ship.
Get API Key