Se factureaza tokenurile efectiv utilizate; partea nefolosita a rezervării este rambursată.
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10 credite gratuite (0,10 USD) când te înregistrezi cu Google
Utilizabil 24 ore după înregistrare, până la 5 rulări pe zi și maximum 2 credite pe rulare. Alte metode de conectare încep fără credite. Suficient pentru 66 de rulări ale acestui model.
02
Despre DeepSeek V4 Flash
Pe scurtDin 23 septembrie 2026
DeepSeek V4 Flash este un model de DeepSeek din categoria Text și chat. Pe Railwail, DeepSeek V4 Flash costă 0,36 USD per 1M tokeni de intrare și 1,44 USD per 1M tokeni de ieșire. Fereastra de context conține 1.048.575 token-uri, iar un răspuns poate fi lung de până la 384.000 token-uri. Versiune mai nouă: 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.
Fundal
Despre DeepSeek AI
Fondat 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.
Parametri
284B total / 13B active per token
Context
1.048.575 tokeni
Capabilități
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.
Antrenament & licență
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.
Licență: Open weights under a permissive license that allows commercial use. Hosted API access via deepseek.com.
Teste de siguranță: DeepSeek publishes model cards but provides limited external red-teaming. Safety filters are lighter than Western frontier labs; deployers are responsible for downstream alignment.
Limitări cunoscute
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 este un model de DeepSeek din categoria Text și chat. Pe Railwail îl poți apela cu o cheie API prin Railwail API.
Cât costă DeepSeek V4 Flash pe Railwail?
Pe Railwail, DeepSeek V4 Flash costă 0,36 USD per 1M tokeni de intrare și 1,44 USD per 1M tokeni de ieșire. Ți se percepe taxa pentru ceea ce fiecare cerere folosește efectiv. Utilizarea se plătește din credite prepay; 1 credit egal cu 0,01 USD.
Care este fereastra de context a DeepSeek V4 Flash?
Fereastra de context a DeepSeek V4 Flash conține 1.048.575 token-uri. Un răspuns poate fi lung de până la 384.000 token-uri.
Cât de rapid este DeepSeek V4 Flash?
Pe Railwail, timpul mediu de rulare a DeepSeek V4 Flash în ultimele 90 zile a fost 1,3 s, pe baza 15 rulări completate.
Este DeepSeek V4 Flash mai bun decât DeepSeek V4.1 Flash?
Depinde de sarcină. DeepSeek V4 Flash (DeepSeek) și DeepSeek V4.1 Flash (DeepSeek) sunt ambele modele din categoria Text și chat. Pagina de comparație arată prețurile și specificațiile lor una lângă alta.
Creează o cheie Railwail API și trimite cererea cu ID-ul modelului deepseek-v4-flash. Exemple de cod pentru curl, Python și JavaScript sunt în secțiunea API a acestei pagini.
Anthropic's model for the most demanding reasoning and long-horizon agentic work. 1M-token context window, up to 128K output tokens, adaptive thinking that is always on.
The most capable model of Anthropic's Opus 4 series. State of the art on long-horizon agentic work, coding and knowledge tasks, with a 1M-token context window at standard pricing.
Anthropic's current Opus model for long-running agentic coding and knowledge work. 1M-token context window, up to 128K output tokens, adaptive thinking that is always on.
