QuickSilver Pro vs Together AI
Together AI lists DeepSeek R1 at $3.00 / $7.00 per 1M tokens — a pricing tier they set for their own GPUs. QuickSilver Pro serves the same model at $0.40 / $1.70, which is ~76% cheaper on output. For reasoning workloads that consume R1's long chain-of-thought, the gap compounds fast.
At a glance
| Feature | QuickSilver Pro | Together AI |
|---|---|---|
| Catalog focus | 3 open-source models | 50+ open models + fine-tuning |
| DeepSeek R1 output price | $1.70 / 1M | $7.00 / 1M |
| DeepSeek V3 output price | $0.70 / 1M | $1.10 / 1M |
| Fine-tuning | No | Yes |
| Dedicated inference endpoints | No | Yes |
| Embeddings · images | No | Yes |
| OpenAI-compatible chat | Yes | Yes |
| Minimum top-up | $5 | $25 |
Pricing (per million tokens, USD)
Public list prices as of April 2026 on the shared open-source models.
| Model | QSP input | QSP output | Together input | Together output | Output savings |
|---|---|---|---|---|---|
| DeepSeek V3 | $0.24 | $0.70 | $0.27 | $1.10 | ~36% |
| DeepSeek R1 | $0.40 | $1.70 | $3.00 | $7.00 | ~76% |
| Qwen3.5-35B-A3B | $0.13 | $1.00 | Comparable | — | |
On a reasoning workload dominated by R1 — say 200k input + 3M output tokens per day (R1's long CoT burns output) — the daily bill is $5.18 on QuickSilver Pro vs $21.06 on Together AI. The gap on R1 output is the largest comparative saving we're aware of among resellers.
Migration — two lines
from openai import OpenAI
client = OpenAI(
base_url="https://api.together.xyz/v1",
api_key=os.environ["TOGETHER_KEY"],
)
r = client.chat.completions.create(
model="deepseek-ai/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"}],
)deepseek-ai/DeepSeek-V3 → deepseek-v3deepseek-ai/DeepSeek-R1 → deepseek-r1Qwen/Qwen3.5-35B-A3B → qwen3.5-35bHonest tradeoffs
- ›Your workload is dominated by DeepSeek R1 output — the savings are dramatic.
- ›You only need chat completions on DeepSeek V3, R1, or Qwen3.5-35B-A3B.
- ›You want a $5 minimum top-up with pay-as-you-go pricing.
- ›You fine-tune custom models or reserve dedicated GPU endpoints.
- ›You use Llama, Mistral, or their wider open-model catalog.
- ›You need embeddings, image generation, or non-chat modalities.
- ›You require a contractual enterprise SLA with penalties — Together sells one, we don't at bridge-phase.
- ›You want a fine-tuning service with their training stack and LoRA adapter hosting.
- ›You're building with Mixture of Agents multi-model routing (MoA) where Together orchestrates several open models in one call.
Together is a full inference platform with fine-tuning, dedicated endpoints, and multi-modal. QuickSilver Pro is intentionally narrower — three models, OpenAI-compatible chat, lowest per-token price.
FAQ
How much cheaper is QuickSilver Pro on DeepSeek R1?
On DeepSeek R1, ~87% cheaper on input and ~76% cheaper on output. Together charges $3.00/$7.00 per 1M tokens; QuickSilver Pro charges $0.40/$1.70.
How do I migrate from Together AI?
Change base_url from api.together.xyz/v1 to api.quicksilverpro.io/v1, swap API key, drop the deepseek-ai/ or Qwen/ prefix from model IDs.
When should I stay on Together AI?
If you fine-tune custom models, reserve dedicated GPU endpoints, use Llama or Mistral, or need embeddings/image generation. QuickSilver Pro is chat completions only on three models.
Same OpenAI features?
Yes for chat: streaming, tools, json_schema, usage.cost all work through the official OpenAI SDK.
Monthly cost walkthrough
A reasoning-heavy workload where DeepSeek R1's 4× Together markup really bites — say, a math-tutor or formal-verification agent generating long chain-of-thought. Monthly footprint: 5M input tokens and 2M output tokens on R1 alone.
5M × $0.40 = $2.00
2M × $1.70 = $3.40
————————————————
Total = $5.40/mo
5M × $3.00 = $15.00 2M × $7.00 = $14.00 ———————————————— Total = $29.00/mo
That's $23.60 saved each month, ~81% off. Scale that to a production reasoning API handling 10× the volume and the annual delta is ~$2,832 — enough that the finance team will ask where the savings came from. R1's output cost is the sharpest place to sanity-check a bill.
Uptime & reliability
QuickSilver Pro is currently in a bridge phase: requests are routed across multiple upstream inference providers serving the same open-source weights. If one upstream degrades or hits capacity, the router falls back to the next. Per-model availability and p50 / p95 latency are published on our status page. We're standing up our own GPU capacity in Q2 2026, at which point the routing model changes and SLAs get firmer.
Together AI runs its own GPU fleet and publishes a public status page at status.together.ai with incident history. They offer contractual enterprise SLAs on reserved-capacity and dedicated-endpoint deployments — something worth a real conversation if your workload is latency-sensitive or compliance-bound. On default serverless chat, both platforms rely on shared inference infrastructure and publish transparent operational data; the meaningful difference on this comparison is per-token pricing, not SLA class at the entry tier.
How the rest stack up
Try it on $1 free credits
If DeepSeek R1 is in your stack, the output savings alone pay for migration in one day.
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