DeepSeek V4 Pro API
The DeepSeek V4 flagship (official 0813 release) for the hardest work: repository-scale analysis, long-document reasoning and reliable function calling over a 1,048,576-token window — behind one OpenAI-compatible endpoint, hosted on Cloudflare.
Specs
- Context window
- 1,048,576 tokens (input + max_tokens)
- Function calling
- Yes — calls tools with correct schemas in our contract tests
- Reasoning
- Yes — reasoning_effort accepted; default low via the gateway
- Upstream ID (public)
- @cf/deepseek-ai/deepseek-v4-pro-0813
- Privacy
- Cloudflare-hosted; metadata-only logging on our side
Pricing (40% off list)
- Input
$1.32$0.792- Output
$3.96$2.376
@cf/deepseek-ai/deepseek-v4-pro-0813
Struck price = list. Full live pricing, including cached input, on the models page.
Measured behavior
Our measurements, 2026-08-16. Pro trades latency for depth — plan around it.
~2.5 s
time to first token on short prompts (p50)
~44 tok/s
streaming throughput (p50)
31.5 s
first token at 300K input tokens
76.5 s
first token at 600K input tokens — always stream large jobs
Quickstart
OpenAI SDK unchanged — set base_url, use model id deepseek-v4-pro.
from openai import OpenAI
client = OpenAI(
base_url="https://api.clfaigateway.dev/v1",
api_key="sk-gw-...", # app.clfaigateway.dev
)
resp = client.chat.completions.create(
model="deepseek-v4-pro",
messages=[{"role": "user", "content": "Summarize this repository..."}],
stream=True,
)
for chunk in resp:
print(chunk.choices[0].delta.content or "", end="")deepseek-v4-pro — common questions
When the answer quality on hard reasoning matters more than latency and price: multi-step analysis over huge context, tricky refactors, long legal or research documents. For high-volume agent loops and extraction, Flash at a third of the price is usually the better trade.
Compare with Flash →Prefill on hundreds of thousands of tokens is real compute — we measured 76.5 s to first token on a 600K-token prompt, and the model can queue under load. The gateway budgets for this (it will not cut a large request off as "hung"); your side should stream and keep the connection open.
Streaming docs →No. Reasoning streams as reasoning_content and its tokens are part of completion_tokens, billed once at the output rate. Usage logs show the reasoning share separately so long thinking is never a billing surprise.
Billing docs →