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02
Om DeepSeek V4 Flash
Kort sagtFrån och med 23 september 2026
DeepSeek V4 Flash är en modell av DeepSeek i kategorin Text & chatt. På Railwail kostar DeepSeek V4 Flash 0,36 US$ per 1M inmatningstoken och 1,44 US$ per 1M utmatningstoken. Kontextfönstret innehåller 1 048 575 tokens, och ett svar kan vara upp till 384 000 tokens långt. Nyare version: 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.
Bakgrund
Om DeepSeek AI
Grundat 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.
Parameter
284B total / 13B active per token
Kontext
1 048 575 tokens
Funktioner
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.
Träning & licens
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.
Licens: Open weights under a permissive license that allows commercial use. Hosted API access via deepseek.com.
Säkerhetstestning: DeepSeek publishes model cards but provides limited external red-teaming. Safety filters are lighter than Western frontier labs; deployers are responsible for downstream alignment.
Kända begränsningar
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 $RAILWAIL_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "deepseek-v4-flash",
"messages": [
{
"role": "user",
"content": "Explain what a vector database is in two sentences."
}
],
"max_tokens": 1024
}'
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["RAILWAIL_API_KEY"],
base_url="https://railwail.com/api/v1",
)
completion = client.chat.completions.create(
model="deepseek-v4-flash",
messages=[
{
"role": "user",
"content": "Explain what a vector database is in two sentences.",
},
],
max_tokens=1024,
)
print(completion.choices[0].message.content)
import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.RAILWAIL_API_KEY,
baseURL: "https://railwail.com/api/v1",
});
const completion = await client.chat.completions.create({
model: "deepseek-v4-flash",
messages: [
{
role: "user",
content: "Explain what a vector database is in two sentences."
}
],
max_tokens: 1024
});
console.log(completion.choices[0].message.content);
// npm install railwail
import railwail from "railwail";
const rw = railwail(process.env.RAILWAIL_API_KEY);
const res = await rw.chat("deepseek-v4-flash", [
{ role: "user", content: "Explain what a vector database is in two sentences." },
], { max_tokens: 1024 });
console.log(res.choices[0].message.content);
DeepSeek V4 Flash är en modell av DeepSeek i kategorin Text & chatt. På Railwail kan du anropa den med en API-nyckel via Railwail API.
Vad kostar DeepSeek V4 Flash på Railwail?
På Railwail kostar DeepSeek V4 Flash 0,36 US$ per 1M inmatningstoken och 1,44 US$ per 1M utmatningstoken. Du debiteras för det som varje begäran faktiskt använder. Användningen betalas från förbetald kredit; 1 kredit motsvarar 0,01 US$.
Hur stort är kontextfönstret för DeepSeek V4 Flash?
Kontextfönstret för DeepSeek V4 Flash innehåller 1 048 575 tokens. Ett svar kan vara upp till 384 000 tokens långt.
Hur snabb är DeepSeek V4 Flash?
På Railwail var mediankörningstiden för DeepSeek V4 Flash under de senaste 90 dagarna 1,3 s, baserat på 15 slutförda körningar.
Är DeepSeek V4 Flash bättre än DeepSeek V4.1 Flash?
Det beror på uppgiften. DeepSeek V4 Flash (DeepSeek) och DeepSeek V4.1 Flash (DeepSeek) är båda modeller i kategorin Text & chatt. Jämförelsesidan visar deras priser och specifikationer sida vid sida.
Skapa en Railwail API-nyckel och skicka din begäran med modell-ID:t deepseek-v4-flash. Kodexempel för curl, Python och JavaScript finns i API-avsnittet på denna sida.
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