DeepSeek V4 Flash Inference

DeepSeek V4 Flash, priced to win.
Tuned to maximum performance.

Dedicated, sovereign serving with a learning loop that keeps cache hit rates high, lowering effective token costs while keeping performance at peak.

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50%
Cheaper than DeepSeek
See the full comparison ↓
284B MoE
1M Context
0 Errors in production
ProviderInput $/1MOutput $/1MCache hit rate
PRIMALABS$0.016$0.26690%
DeepSeek$0.031$0.27979.6%
NovitaAI$0.053$0.27978.0%
Baidu Qianfan$0.037$0.18173.6%
Fireworks$0.100$0.27935.2%
PROVIDER PRICING: OPENROUTER EFFECTIVE PRICING, JULY 2026 · CACHE HIT RATE IS THE HIDDEN MULTIPLIER ON EVERY BILL
Why PrimaLabs

Static stacks tune once. Ours learns every workload.

Customization

Tuned to the workload, not the benchmark

The learning loop observes live prompts, context lengths, and load, then re-tunes batching, scheduling, and cache strategy for that exact traffic. DeepSeek V4 Flash serving gets faster and cheaper the longer it runs.

Cache

Hit rate is where the bill is won

Cached input tokens bill at a fraction of list price. One warm, dedicated stack keeps hit rates high where routed serverless traffic fragments them. Effective cost per token drops below any sticker price.

Sovereign

Dedicated capacity, fully controlled

Reserved throughput on one sovereign stack, in the PrimaLabs cloud, a customer VPC, or on customer GPUs, NVIDIA or AMD. Known data path, predictable latency, cost that falls with scale.

Where it fits

Built for workloads where inference is core COGS

Coding agents

Sequential calls, compounding gains

Agents stack first-token latency on every step. Faster TTFT and high cache hit rates turn a sluggish agent instant, and cut cost per session.

Voice

Sub-second or nothing

Voice agents live and die on latency. Dedicated capacity holds response times down under real production load.

High volume

Pipelines at scale

Document, data, and batch workloads where throughput per GPU is the unit economics. The loop keeps raising it.

Consumer AI

Chat and assistants

Perceived speed drives retention. Faster first tokens on every message, with effective cost falling as traffic grows.

FAQ

Common questions

How is PrimaLabs pricing structured?
Per million tokens on dedicated capacity, with volume tiers that step down as sustained throughput rises. Cache-aware pricing means the effective rate depends on hit rate, which the learning loop actively manages upward.
Do the optimizations change model output?
No. Model weights stay exactly as published. All gains come from the host-level serving layer: batching, scheduling, cache, and memory management that adapt to the live workload.
How hard is migration?
The API is OpenAI compatible. Change the base URL and the API key, keep the rest of the code. Most teams are serving production traffic the same week.
What does customization mean in practice?
Every deployment is tuned to its own traffic. Prompt shapes, context length distribution, request rate, and cache behavior all feed the loop, so two customers running the same model get two differently optimized stacks.

Get DeepSeek V4 Flash pricing for the workload

Share the traffic profile. Get dedicated pricing with a cache-aware cost model, and a benchmark on real prompts within days.

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