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API DOCSPRICING
TEMPLATES
deepseekText Chat

DeepSeek V4 Pro API: reasoning-focused chat for code and analysis

Test DeepSeek V4 Pro online, review token pricing, and call it through AIReiter Messages API for code review, technical planning, and evidence-heavy analysis.

InputAIReiter $0.43 per 1M tokensOutputAIReiter $0.87 per 1M tokensCache readAIReiter $0.00 per 1M tokens
Run with API
PlaygroundReadmeAPI

INPUT

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Install the official Anthropic client — AIReiter speaks the same protocol, so only the base URL changes:

npm install @anthropic-ai/sdk

Set the AIREITER_API_KEY environment variable:

export AIREITER_API_KEY=<paste-your-key-here>

Point the client at AIReiter:

import Anthropic from "@anthropic-ai/sdk";

const client = new Anthropic({
  apiKey: process.env.AIREITER_API_KEY,
  baseURL: "https://aireiter.com/api",
});

Run deepseek-v4-pro:

const message = await client.messages.create({
    "model": "deepseek-v4-pro",
    "max_tokens": 2048,
    "messages": [
      {
        "role": "user",
        "content": "Compare these two architecture options and point out the hidden implementation risks."
      }
    ],
    "temperature": 0.7,
    "top_p": 1
  });

console.log(message.content);

Stream the response instead:

const stream = client.messages.stream({
    "model": "deepseek-v4-pro",
    "max_tokens": 2048,
    "messages": [
      {
        "role": "user",
        "content": "Compare these two architecture options and point out the hidden implementation risks."
      }
    ],
    "temperature": 0.7,
    "top_p": 1
  });

stream.on("text", (text) => process.stdout.write(text));
const message = await stream.finalMessage();

Install the official Anthropic client — AIReiter speaks the same protocol, so only the base URL changes:

pip install anthropic

Set the AIREITER_API_KEY environment variable:

export AIREITER_API_KEY=<paste-your-key-here>

Point the client at AIReiter:

import os
import anthropic

client = anthropic.Anthropic(
    api_key=os.environ["AIREITER_API_KEY"],
    base_url="https://aireiter.com/api",
)

Run deepseek-v4-pro:

message = client.messages.create(
      model = "deepseek-v4-pro",
      max_tokens = 2048,
      messages = [
        {
          role = "user",
          content = "Compare these two architecture options and point out the hidden implementation risks."
        }
      ],
      temperature = 0.7,
      top_p = 1
)

print(message.content)

Stream the response instead:

with client.messages.stream(
      model = "deepseek-v4-pro",
      max_tokens = 2048,
      messages = [
        {
          role = "user",
          content = "Compare these two architecture options and point out the hidden implementation risks."
        }
      ],
      temperature = 0.7,
      top_p = 1
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

Set the AIREITER_API_KEY environment variable:

export AIREITER_API_KEY=<paste-your-key-here>

Run deepseek-v4-pro against AIReiter's API:

curl -s -X POST \
  -H "x-api-key: $AIREITER_API_KEY" \
  -H "Content-Type: application/json" \
  "https://aireiter.com/api/v1/messages" \
  -d '{
  "model": "deepseek-v4-pro",
  "max_tokens": 2048,
  "messages": [
    {
      "role": "user",
      "content": "Compare these two architecture options and point out the hidden implementation risks."
    }
  ],
  "temperature": 0.7,
  "top_p": 1
}'

Add "stream": true to the body to receive the response as server-sent events.

OUTPUT

Example

A rate limit caps how many requests an API accepts from you in a given window. Once you exceed it, the server stops doing work for you and answers 429 Too Many Requests instead.

Handling 429

  1. Read the Retry-After response header. When present it tells you exactly how long to wait, in seconds.
  2. When it is absent, back off exponentially with jitter so retries from many clients do not line up.
  3. Cap the number of retries, then surface the failure instead of looping forever.
async function withRetry(request, maxRetries = 4) {
  for (let attempt = 0; ; attempt++) {
    const response = await request();
    if (response.status !== 429 || attempt === maxRetries) return response;
    const retryAfter = Number(response.headers.get("retry-after"));
    const backoff = Number.isFinite(retryAfter) ? retryAfter * 1000 : 2 ** attempt * 500 + Math.random() * 250;
    await new Promise((resolve) => setTimeout(resolve, backoff));
  }
}

Treat the limit as a budget you plan around, not an error you retry your way out of: batch requests where you can, cache repeated reads, and spread bulk work over time.

{
  "model": "deepseek-v4-pro",
  "input": {
    "model": "deepseek-v4-pro",
    "max_tokens": 2048,
    "messages": [
      {
        "role": "user",
        "content": "Compare these two architecture options and point out the hidden implementation risks."
      }
    ],
    "temperature": 0.7,
    "top_p": 1
  },
  "output": "A rate limit caps how many requests an API accepts from you in a given window. Once you exceed it, the server stops doing work for you and answers **429 Too Many Requests** instead.\n\n## Handling 429\n\n1. Read the `Retry-After` response header. When present it tells you exactly how long to wait, in seconds.\n2. When it is absent, back off exponentially with jitter so retries from many clients do not line up.\n3. Cap the number of retries, then surface the failure instead of looping forever.\n\n```js\nasync function withRetry(request, maxRetries = 4) {\n  for (let attempt = 0; ; attempt++) {\n    const response = await request();\n    if (response.status !== 429 || attempt === maxRetries) return response;\n    const retryAfter = Number(response.headers.get(\"retry-after\"));\n    const backoff = Number.isFinite(retryAfter) ? retryAfter * 1000 : 2 ** attempt * 500 + Math.random() * 250;\n    await new Promise((resolve) => setTimeout(resolve, backoff));\n  }\n}\n```\n\nTreat the limit as a budget you plan around, not an error you retry your way out of: batch requests where you can, cache repeated reads, and spread bulk work over time.",
  "metrics": {
    "input_tokens": 26,
    "output_tokens": 214,
    "generated_in_seconds": 4.1
  },
  "example": true
}
Generated in
4.1 seconds
Input tokens
26
Output tokens
214
Tokens per second
52.20 tokens / second
Time to first token
-

Model details

Use the same model key in Playground, API requests, and internal workflows.

Model ID
deepseek-v4-pro
Provider
deepseek
Protocol
Anthropic Messages
Context window
128,000 tokens
Max output
-
Input tokens
43.5 credits / 1M tokens
Output tokens
87 credits / 1M tokens
Cache read
0.3625 credits / 1M tokens
Cache write
-

A reasoning route for high-risk technical decisions

DeepSeek V4 Pro is the page to test when a prompt needs code understanding, tradeoff analysis, and a clear final recommendation rather than a short generic answer.

DeepSeek V4 Pro API cover

Should you choose DeepSeek V4 Pro?

Position it as an escalation model for tasks where a wrong answer creates engineering rework, customer risk, or expensive review cycles.

Choose it when

You need deeper code review, architecture comparison, root-cause analysis, or evidence-heavy technical planning.

Use another model when

The request is short, repetitive, latency-sensitive, or mainly classification/extraction; DeepSeek V4 Flash or another lightweight route is usually enough.

Public API protocol

Call POST https://aireiter.com/api/v1/messages with model "deepseek-v4-pro". Streaming is supported through the same Messages-compatible endpoint.

Token and cache usage

Pricing is based on input, cache-read, and output tokens. Cache-read tokens only count when the returned usage explicitly reports reused prompt context.

DeepSeek V4 Pro production workloads

Use it for the work where a slower, more deliberate answer can prevent rework.
01

Architecture and migration review

Compare implementation paths, expose hidden dependencies, and turn ambiguous plans into a sequence of engineering decisions.

02

Code risk analysis

Review pull requests, logs, and bug reports together to identify likely failure modes before editing files.

03

Technical decision support

Summarize tradeoffs into a recommendation that explains why one option should win.

04

Escalation tier for agents

Route only the failed or high-risk agent steps here after a faster model cannot resolve them reliably.

How DeepSeek V4 Pro fits your model stack

Do not route every request to the newest model. Pick the cheapest model that still passes your quality bar, then reserve deeper models for failures or high-risk tasks.

For fast batches

Use DeepSeek V4 Flash first for cheap technical batches, then escalate only difficult rows to V4 Pro.

For deeper reasoning

Use DeepSeek V4 Pro when the reasoning quality matters more than response time.

For long context

If the main bottleneck is repository-scale context, compare it with Kimi K2.7 Code or MiniMax M3.

For production rollout

Start with representative prompts, inspect usage, then create routing rules instead of replacing every model at once.

DeepSeek V4 Pro API questions

Questions developers usually check before moving a text model from playground testing to production API traffic.

/ 01

What model ID should I send for DeepSeek V4 Pro?

Use "deepseek-v4-pro" in the API request body. The internal DB key is only used by AIReiter routing.

/ 02

Which endpoint should DeepSeek V4 Pro use?

Use POST https://aireiter.com/api/v1/messages for public API calls. Keep x-api-key / Authorization authentication consistent with your AIReiter API key setup.

/ 03

Does DeepSeek V4 Pro support streaming?

Yes. Send stream=true and read server-sent events until the message completes. Test non-streaming first when debugging authentication or model ID issues.

/ 04

How do I confirm token and cache billing for DeepSeek V4 Pro?

Check the usage object returned by the API. Input, output, and cache-read token fields are the source of truth for settlement; a repeated prompt alone does not prove a cache hit.

/ 05

Should I always set max_tokens for DeepSeek V4 Pro?

Set max_tokens when you need a hard cost or response-length cap. For analysis prompts, avoid setting it too low or the answer may stop before the recommendation.

AIREITER

Questions? Contact us at
support@aireiter.com

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