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O DeepSeek V4 Flash
StručněStav: 23. září 2026
DeepSeek V4 Flash je model od DeepSeek v kategorii Text a chat. Na Railwail stojí DeepSeek V4 Flash 0,36 US$ za 1M vstupních tokenů a 1,44 US$ za 1M výstupních tokenů. Kontextní okno obsahuje 1 048 575 tokenů, odpověď může být dlouhá až 384 000 tokenů. Novější verze: DeepSeek V4.1 Flash.
DeepSeek-V4-Flash is the cost-efficient sibling of V4-Pro, released April 2026 as part of the V4 Preview. 284B total / 13B active MoE parameters with the same 1M-token context window. Designed for high-throughput agentic loops, RAG and batch tasks where latency and cost matter more than raw capability. Recommended for production agents, classification at scale, large-scale data extraction.
Pozadí
O DeepSeek AI
Založeno 2023 · Hangzhou, China
DeepSeek AI is a Chinese AI research lab founded in 2023 by Liang Wenfeng, founder of the High-Flyer quantitative hedge fund. The lab is funded primarily by High-Flyer's profits. Its mission is open frontier AI, with all flagship models released with open weights. Major releases include DeepSeek LLM (2023), DeepSeek-V2 (May 2024), DeepSeek-V3 (December 2024), DeepSeek-R1 (January 2026), DeepSeek V3.1 (early 2026) and the DeepSeek V4 family (April 24, 2026), comprising V4-Pro and V4-Flash. DeepSeek is credited with popularising large-scale Reinforcement Learning from Verifiable Rewards and consistently tops open-weights leaderboards.
DeepSeek-V4-Flash was released April 24, 2026 as the efficiency-optimized sibling of V4-Pro. It is a Sparse MoE Transformer with 284B total parameters and 13B activated per token, retaining the full 1M-token native context window and 384K-token max output of the Pro variant at significantly lower inference cost. The model uses the same DeepSeek architectural stack: Multi-head Latent Attention (MLA), DeepSeekMoE with fine-grained expert specialization and shared experts, and FP8 mixed-precision training. Post-training combined supervised fine-tuning, RLVR on math/code/tool-use trajectories, and heavy distillation from the V4-Pro teacher model. V4 Flash is published with open weights under a permissive license and is designed for production-scale RAG, agentic loops and high-throughput workloads. At $0.112 input / $0.224 output per million tokens it undercuts every Western frontier model by an order of magnitude.
Parametry
284B total / 13B active per token
Kontext
1 048 575 tokenů
Schopnosti
1M token native context window with 384K max output
284B MoE / 13B active parameters
Ultra-low pricing ($0.112 / $0.224 per million tokens)
Distilled from DeepSeek V4-Pro teacher model
FP8-trained for compute efficiency
Multi-head Latent Attention for memory-efficient long context
Function calling and structured JSON output
Strong on math, STEM and coding for its size
Available via DeepSeek API, OpenRouter, Together and self-hosted with vLLM/SGLang
Open weights under a permissive license
Best for: production agents, RAG pipelines, high-throughput data extraction, on-premise inference under tight cost budgets.
Trénování a licence
Pretrained on the same multi-trillion-token mixture as V4-Pro. Post-training combines supervised fine-tuning, RLVR and distillation from the V4-Pro teacher model. Knowledge cutoff approximately early 2026.
Licence: Open weights under a permissive license that allows commercial use. Hosted API access via deepseek.com.
Bezpečnostní testy: DeepSeek publishes model cards but provides limited external red-teaming. Safety filters are lighter than Western frontier labs; deployers are responsible for downstream alignment.
Známá omezení
Below V4-Pro on the hardest reasoning and coding benchmarks
Light built-in safety alignment relative to Western frontier models
No native vision or audio input (text-only)
Older deepseek-chat / deepseek-reasoner endpoints will be deprecated July 24, 2026
curl https://railwail.com/api/v1/chat/completions \
-H "Authorization: Bearer $RAILWAIL_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "deepseek-v4-flash",
"messages": [
{
"role": "user",
"content": "Explain what a vector database is in two sentences."
}
],
"max_tokens": 1024
}'
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["RAILWAIL_API_KEY"],
base_url="https://railwail.com/api/v1",
)
completion = client.chat.completions.create(
model="deepseek-v4-flash",
messages=[
{
"role": "user",
"content": "Explain what a vector database is in two sentences.",
},
],
max_tokens=1024,
)
print(completion.choices[0].message.content)
import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.RAILWAIL_API_KEY,
baseURL: "https://railwail.com/api/v1",
});
const completion = await client.chat.completions.create({
model: "deepseek-v4-flash",
messages: [
{
role: "user",
content: "Explain what a vector database is in two sentences."
}
],
max_tokens: 1024
});
console.log(completion.choices[0].message.content);
// npm install railwail
import railwail from "railwail";
const rw = railwail(process.env.RAILWAIL_API_KEY);
const res = await rw.chat("deepseek-v4-flash", [
{ role: "user", content: "Explain what a vector database is in two sentences." },
], { max_tokens: 1024 });
console.log(res.choices[0].message.content);
DeepSeek V4 Flash je model od DeepSeek v kategorii Text a chat. Na Railwail jej můžete volat pomocí API klíče přes Railwail API.
Kolik stojí DeepSeek V4 Flash na Railwail?
Na Railwail stojí DeepSeek V4 Flash 0,36 US$ za 1M vstupních tokenů a 1,44 US$ za 1M výstupních tokenů. Platíte za to, co každý požadavek skutečně spotřebuje. Použití se platí z předplacených kreditů; 1 kredit se rovná 0,01 US$.
Jaké je kontextní okno DeepSeek V4 Flash?
Kontextní okno DeepSeek V4 Flash obsahuje 1 048 575 tokenů. Odpověď může být dlouhá až 384 000 tokenů.
Jak rychlý je DeepSeek V4 Flash?
Na Railwail byla medián doby běhu DeepSeek V4 Flash za posledních 90 dní 1,3 s, na základě 15 dokončených spuštění.
Je DeepSeek V4 Flash lepší než DeepSeek V4.1 Flash?
Záleží na úkolu. DeepSeek V4 Flash (DeepSeek) a DeepSeek V4.1 Flash (DeepSeek) jsou oba modely v kategorii Text a chat. Stránka porovnání zobrazuje jejich ceny a specifikace vedle sebe.
Vytvořte Railwail API klíč a pošlete svůj požadavek s ID modelu deepseek-v4-flash. Příklady kódu pro curl, Python a JavaScript jsou v sekci API na této stránce.
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