DeepSeek V4 Flash

deepseek-v4-flash

DeepSeek V4 Flash is DeepSeek's efficiency-optimized mixture-of-experts model, with 284B total parameters, 13B activated parameters, and support for a 1M token context window. It is designed for fast inference and high-throughput workloads while maintaining strong reasoning and coding performance. The model includes hybrid attention for efficient long-context processing and supports configurable inference modes. It is ideal for applications such as coding assistants, chat systems, and agent workflows, where responsiveness and cost efficiency are critical.

Context window1.0M

ProviderDeepSeek

Released2026/04/25

Playground

Pricing

The same model is available through multiple service channels — choose based on latency, reliability and cost.

Prices in $ / 1M tokens

To pick a channel, add a provider field to the request body, for example "provider": { "channel": "direct" }. Valid values are direct / stable / economical; omit it to use the default channel.

Direct

Direct upstream connection — best when you need native behavior and the full context window.

Input	Output	Cache read
0.44/M	1.32/M	0.01/M

Overview

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Output

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DeepSeek V4 Flash API: 284B-Parameter Efficient Open-Source Model

DeepSeek V4 Flash is DeepSeek's efficiency-optimized mixture-of-experts model, with 284 billion total parameters and 13B active parameters, supporting a 1 million token context window. It is designed for fast inference and high-throughput workloads while maintaining strong reasoning and coding performance.

The Flash version shares the same architectural innovations as the Pro version — hybrid attention for efficient long-context processing, and support for configurable reasoning modes. Benchmarks show that Flash is on par with Pro on simple tasks, with gaps appearing only on high-difficulty tasks. For the vast majority of production scenarios, this means you can get near-flagship results at Flash pricing.

SeaWhale AI offers DeepSeek V4 Flash through an OpenAI-compatible interface, with support for tool calling, streaming output, and long-context processing.

Get API Key · Model ID: deepseek-v4-flash

Why Choose DeepSeek V4 Flash

On par with Pro on simple tasks — most production scenarios don't need to pay extra for Pro
284 billion total parameters / 13B active — efficient sparse MoE, low inference cost
1 million token context — the same long-context capability as the Pro version
384K max output — long-form content generated in a single pass
Hybrid attention architecture — efficient long-context processing
Open-source and controllable — public weights, deployable on-premises

Core Capabilities

01 High-Throughput Inference

The Flash version is designed for fast inference and high throughput. With 13B active parameters, the compute per inference is far lower than the Pro version, allowing it to handle several times the concurrency on the same resources.

High-concurrency production deployment
Low-latency response
Large-scale offline processing

02 Coding Assistant Scenarios

Flash is officially positioned as the ideal choice for scenarios such as coding assistants and chat systems — scenarios that need stable mid-difficulty output plus fast responses.

Code completion and refactoring
Code explanation and documentation generation
Unit test generation

03 1 Million Token Long Context

Flash shares the same 1 million token context capability and hybrid attention architecture as Pro, so long-document and large-repository scenarios don't require a tier upgrade for this.

Complete code repository analysis
Long-document understanding and summarization
Up to 384K output tokens

04 Configurable Reasoning Modes

Supports configurable reasoning modes, letting you adjust reasoning depth by task difficulty to find the right balance between quality and speed.

Use shallow reasoning for speed on simple tasks
Increase reasoning depth for complex tasks
Configure flexibly by scenario

Best Use Cases

Scenario	Description
Coding assistant	Code completion, refactoring, explanation, test generation
Chat systems	High-concurrency conversational products
Long-document processing	Analysis and summarization within a 1 million token window
Batch processing pipelines	Cleaning and transformation of large-scale corpora
Cost-sensitive agents	Automated workflows requiring a high volume of calls
On-premises deployment	Moderate parameter count, lower barrier to local deployment

DeepSeek V4 Flash vs. V4 Pro

Capability	DeepSeek V4 Flash	DeepSeek V4 Pro
Model ID	`deepseek-v4-flash`	`deepseek-v4-pro`
Total parameters	284 billion	1.6 trillion
Active parameters	13B	49B
Context window	1 million tokens	1 million tokens
Max output	384K tokens	384K tokens
World knowledge	Slightly weaker	Stronger
Reasoning capability	Close to Pro	Frontier
Simple tasks	On par with Pro	Excellent
Difficult tasks	Some gap	Clearly stronger

For specific billing, refer to the real-time price card at the top of the page.

How to Use the DeepSeek V4 Flash API

1. Create a SeaWhale AI API key Generate a key in the console and top up your balance.

2. Start with Flash by default Since Flash and Pro perform comparably on simple tasks, the sensible approach is to use Flash by default and upgrade to Pro only if testing shows the quality isn't sufficient.

3. Call the API

curl -X POST https://api.atalk-ai.com/v1/chat/completions \
  -H 'Authorization: Bearer YOUR_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "deepseek-v4-flash",
    "messages": [
      {"role": "user", "content": "Add type annotations and docstrings to this Python module."}
    ],
    "stream": true
  }'

FAQ

How much difference is there between Flash and Pro? Flash is slightly weaker in world knowledge, but its reasoning capability is close to Pro. In agent evaluations, the two are evenly matched on simple tasks, with a clear gap only on difficult tasks.

When should I upgrade to Pro? When you need broad world knowledge, difficult mathematical reasoning, or the most complex repository-level agent tasks. Flash is fully sufficient for everyday coding and conversational scenarios.

What are the context and output limits? 1 million token context and up to 384,000 output tokens — identical to the Pro version.

Will 284B parameters make inference slow? No. It's a sparse MoE with only 13B active parameters, so inference speed and cost are close to those of a medium-sized dense model.

Can it be deployed on-premises? Yes. The DeepSeek V4 series weights are public, and the Flash version's moderate parameter count means local deployment is easier than with the Pro version.

What is hybrid attention? An architectural upgrade introduced in the V4 series for efficient long-context processing. It's the key to making a 1 million token context window economically viable.

Why Use DeepSeek V4 Flash API on SeaWhale AI

No self-built cluster required — call it without provisioning your own GPU resources
OpenAI-compatible interface — connect with a two-line change to existing code
Flexible Flash / Pro switching — route by task difficulty for optimal cost
RMB settlement — domestic models priced in RMB

API

API integration

Model IDUse this value as the model in inference requests

deepseek-v4-flash

API KeyBearer token used to authenticate inference requests

Manage API keys

Base URLOpenAI compatible · /chat/completions

https://api.atalk-ai.com/v2/

provider OptionalSelects a service channel; omit it and the system picks the default

"provider": { "channel": "direct" }

deepseek-v4-flash usage examples

SeaWhale AI is compatible with the OpenAI API protocol, so you can call it with the OpenAI SDK or plain HTTP requests. Streaming is enabled by default.

About the provider parameter (optional, a SeaWhale AI extension): most models are served over several channels that differ slightly in price and reliability. Add a provider field to the request body to pick one; omit it and the system selects the default channel — normal calls are unaffected.

provider is not part of the official OpenAI protocol — it is a SeaWhale AI extension that only takes effect on this platform. The OpenAI SDK allows custom fields like this to pass through; see the examples below.

Value	Channel	Best for
`direct`	Direct	The official upstream link, for native behavior and the full context window
`stable`	Preferred	Balanced availability and speed — a good default for production traffic
`economical`	Economy	Cost first, well suited to batch processing and price-sensitive workloads

Available channels and their prices are listed under "Pricing" above (channels vary by model). Additional notes:

Syntax: "provider": { "channel": "direct" }.
If the requested channel is not enabled for that model, the request automatically falls back to the default channel and returns normally — it does not error.
With the official SDKs: in Python pass it via extra_body; in Node.js put it directly on the request object and it passes through. In TypeScript projects, add a // @ts-expect-error line to skip the type check.

cURLPythonNode.js

curl https://api.atalk-ai.com/v2/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer <API_KEY>" \
  -d '{
    "model": "deepseek-v4-flash",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "Hello!"}
    ],
    "provider": { "channel": "direct" },
    "stream": true
  }'
# provider is optional — remove this line to use the default channel

from openai import OpenAI

client = OpenAI(
    base_url="https://api.atalk-ai.com/v2",
    api_key="<API_KEY>",
)

stream = client.chat.completions.create(
    model="deepseek-v4-flash",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello!"},
    ],
    stream=True,
    # Optional: pick a service channel; omit to use the default
    extra_body={"provider": {"channel": "direct"}},
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

import OpenAI from 'openai'

const client = new OpenAI({
  baseURL: 'https://api.atalk-ai.com/v2',
  apiKey: '<API_KEY>',
})

const stream = await client.chat.completions.create({
  model: 'deepseek-v4-flash',
  messages: [
    { role: 'system', content: 'You are a helpful assistant.' },
    { role: 'user', content: 'Hello!' },
  ],
  stream: true,
  // Optional: pick a service channel; omit to use the default
  // @ts-expect-error provider is a SeaWhale AI extension, not in the OpenAI SDK types
  provider: { channel: 'direct' },
})

for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content ?? '')
}