DeepSeek V3 inference speed: which API is fastest

A useful DeepSeek V3 inference speed comparison has to look past vendor claims and measure time-to-first-token and sustained throughput from your own region. The model’s 671B-parameter mixture-of-experts architecture makes serving expensive, so provider optimizations and physical location dominate raw silicon speed.

What “fast” actually measures

DeepSeek V3 is a 671B MoE model with ~37B active parameters per forward pass. That means inference servers must shuffle large weight shards across GPUs while only a fraction is used per token. The bottleneck is almost always interconnect bandwidth and batch scheduling, not peak FLOPS.

Three metrics matter:

• TTFT (time to first token): latency before generation starts.
• Inter-token latency: milliseconds between subsequent tokens.
• Aggregate throughput: tokens/sec under concurrent load.

A chatbot cares about TTFT and inter-token latency. A batch job cares about throughput per dollar.

The APIs in play

We compare four ways to call the same weights:

1. DeepSeek official API (api.deepseek.com) – first-party hosting.
2. OpenRouter – aggregator routing to multiple upstreams.
3. Fireworks AI – optimized serving with custom CUDA kernels and edge regions.
4. Together AI – high-throughput distributed serving.

A gateway such as n4n.ai that exposes one OpenAI-compatible endpoint across 240+ models can sit in front of these and automatically fallback when a provider is degraded, but the underlying latency is still the route you get.

Measurement methodology

Before trusting a vendor, run your own DeepSeek V3 inference speed comparison using the script below. Never trust a provider’s “tokens/sec” headline. Write a 20-line script and run it from your production region.

from openai import OpenAI
import time, os

client = OpenAI(
    base_url="https://api.fireworks.ai/inference/v1",
    api_key=os.environ["FW_KEY"],
)

start = time.time()
stream = client.chat.completions.create(
    model="deepseek-v3",
    messages=[{"role": "user", "content": "Summarize the TCP spec in 200 words."}],
    stream=True,
)
first_token, n = None, 0
for chunk in stream:
    delta = chunk.choices[0].delta.content
    if delta:
        if first_token is None:
            first_token = time.time()
        n += 1
end = time.time()

ttft = first_token - start
decode = (end - first_token) / max(n, 1)
print(f"TTFT={ttft*1000:.0f}ms tok/s={1/decode:.1f}")

Run this against each base URL with the same prompt and region. Swap model identifiers per provider (deepseek/deepseek-v3 on OpenRouter, deepseek-ai/DeepSeek-V3 on Together, etc.).

Interpreting the numbers

You’ll see TTFT variance of 2–3× across attempts due to cold starts. Discard the first run. Illustrative ranges from public latency reports look like this:

{
  "fireworks_us": {"ttft_ms": [280, 410], "tok_s": [70, 110]},
  "deepseek_official": {"ttft_ms": [900, 1400], "tok_s": [55, 90]},
  "together_us": {"ttft_ms": [350, 500], "tok_s": [80, 120]}
}

These are not a substitute for your own test, but they show the shape of the gap.

Geography beats marketing

Any honest DeepSeek V3 inference speed comparison shows that physical distance overwhelms model optimizations. DeepSeek’s official service is hosted in Asia. From a us-east-1 instance, you eat ~150ms of one-way fiber latency before any compute. That alone pushes TTFT past 300ms before the model runs.

Fireworks and Together both provision US and EU inference nodes. If your users are in North America, their TTFT will be half of official DeepSeek’s before accounting for kernel differences.

OpenRouter routes to whichever upstream has capacity; you might land on a US-hosted DeepSeek fork or a slower fallback. That variance is the cost of aggregation.

Throughput under load

For offline extraction jobs, sustained tokens/sec matters more than TTFT. Official DeepSeek runs full-precision weights on large clusters; it can sustain high batch throughput if you stay under rate limits.

Fireworks often uses FP8 quantization. That trades a fraction of accuracy for markedly higher decode speed and lower memory bandwidth per token. Together uses tensor-parallelism with continuous batching that scales well at 32+ concurrent streams.

If you send 50 requests concurrently, the aggregator or first-party API with generous concurrency limits wins; a single edge node from a smaller provider may queue.

Cache hints change the game

DeepSeek V3 supports prefix caching. If you send the same system prompt and few-shot examples repeatedly, a provider that honors cache-control can skip recomputing the KV cache.

OpenAI-compatible headers work:

curl https://api.example.com/v1/chat/completions \
  -H "Authorization: Bearer $KEY" \
  -d '{
    "model": "deepseek-v3",
    "messages": [{"role":"system","content":"...long context..."}],
    "cache_control": {"type": "ephemeral"}
  }'

n4n.ai forwards these provider cache-control hints, so a cached prefix on the upstream speeds your subsequent calls without extra client logic.

Tradeoffs you cannot avoid

• Official DeepSeek: best weight fidelity, possible compliance friction, higher trans-Pacific latency for Western clients.
• Fireworks: low TTFT in US, quantization may matter for some eval suites.
• Together: strong throughput, less edge presence.
• OpenRouter: resilience via multi-provider, but you don’t control the route.

Cost tracks speed loosely. Faster decode often means more expensive hardware amortized over fewer requests.

Verdict

For interactive applications serving users in the US or EU, Fireworks or Together will win a DeepSeek V3 inference speed comparison on latency every time, because physical distance and optimized kernels beat first-party weights. For bulk processing where you can tolerate higher TTFT, the official API or an aggregator with fallback gives better throughput stability.

If you need one integration that survives provider outages, put a gateway in front and measure the routed latency monthly. The fastest API is the one closest to your users with the batch scheduler that matches your traffic shape.