Large Language Modeldeepseek-v4-pro

DeepSeek V4 Pro

Flagship Reasoning

DeepSeek next-gen MoE reasoning flagship for complex architectural design, autonomous coding loops, and math proofs.

AvailableMoE TransformerProprietary

Params

MoE 256K

Context

256K

Max Output

64K

API Surface/v1/chat/completions

Capabilities & Performance

  • SOTA multi-turn agent coding performance
  • Wholesale tier discount down to $0.429/M input, $0.857/M output

Transparent Pricing

LanePublic RateCached
Realtime API$0.56 / $1.11$0.111
Batch QueueBatch rates available on rollout$0.111

Prices per 1M tokens. Cached prompt rate applies on prefix hits.

100% OpenAI-Compatible Code

Drop-in API Examples

Python (OpenAI SDK)
from openai import OpenAI

client = OpenAI(
    base_url="https://api.batchin.tech/v1",
    api_key="BATCHIN_API_KEY"
)

resp = client.chat.completions.create(
    model="deepseek-v4-pro",
    messages=[{"role": "user", "content": "Benchmark system architecture performance and cost profile."}]
)

print(resp.choices[0].message.content)
JavaScript / Node.js
import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.batchin.tech/v1",
  apiKey: process.env.BATCHIN_API_KEY,
});

const resp = await client.chat.completions.create({
  model: "deepseek-v4-pro",
  messages: [{ role: "user", content: "Benchmark system architecture performance and cost profile." }],
});

console.log(resp.choices[0]?.message?.content);
cURL / Shell
curl https://api.batchin.tech/v1/chat/completions \
  -H "Authorization: Bearer $BATCHIN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-v4-pro",
    "messages": [{"role":"user","content":"Benchmark system architecture performance and cost profile."}]
  }'

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