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DeepSeek · Released 2026-04-24

DeepSeek V4 Flash LLM

Context 1M Max output 384K Type LLM Provider Ali
deepseek/deepseek-v4-flash

DeepSeek V4 Flash is DeepSeek's high-speed, cost-efficient Mixture-of-Experts (MoE) model, featuring 284 billion total parameters, 13 billion activated parameters, and a 1 million-token context window. It delivers fast inference, high throughput, and strong performance in reasoning, coding, and general-purpose AI tasks. Built with a hybrid attention architecture for efficient long-context processing, it supports High and XHigh reasoning modes (with XHigh corresponding to maximum reasoning). It is ideal for coding assistants, conversational AI, real-time applications, and agent workflows where speed, scalability, and cost efficiency are essential.

Pricing

USD · live rate
Pricing USD / M
Input $0.14
Output $0.29
Cache hit $0.0029
Image input

Providers

Same model, multi-channel live comparison · best values highlighted
Provider Context Max output Input /M Output /M Cache /M Latency Throughput
Ali 1M 384K $0.14 $0.29 $0.0029 11197ms 120 t/s
Tokenpony 1M 384K $0.14 $0.29 $0.03 1311ms 84 t/s
DeepSeek 1M 384K $0.14 $0.29 $0.0029 287ms 65 t/s

API example

Model Center normalizes requests and responses across providers behind one OpenAI-compatible API.

Call this model directly or through the OpenAI SDK — one API key for every model in the catalog.

Get API Key Get API Key
from openai import OpenAI

client = OpenAI(
    base_url="https://router-integration.test.cogfoundry.ai/api/v1",
    api_key="$MODEL_CENTER_API_KEY",
)

completion = client.chat.completions.create(
    model="deepseek/deepseek-v4-flash",
    messages=[{"role": "user", "content": "Which number is larger, 9.11 or 9.8?"}],
    stream=True,
)

for chunk in completion:
    if chunk.choices and chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end="", flush=True)

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