יחויבו הטוקנים בפועל שנעשה בהם שימוש; החלק שלא נעשה בו שימוש מהסכום המסוכן יוחזר.
חדש כאן?
10 קרדיטים חינם (0.10 $) כשאתה נרשם עם Google
ניתן להשתמש 24 שעות לאחר ההרשמה, עד 5 ריצות ביום ולכל היותר 2 קרדיטים לריצה. שיטות כניסה אחרות מתחילות ללא קרדיטים. מספיק ל-66 הרצות של מודל זה.
02
אודות DeepSeek V4 Flash
בקצרהנכון ל-23 בספטמבר 2026
DeepSeek V4 Flash הוא מודל של DeepSeek בקטגוריית טקסט וצ'אט. ב-Railwail, DeepSeek V4 Flash עולה 0.36 $ ל-1M טוקנים קלט ו-1.44 $ ל-1M טוקנים פלט. חלון ההקשר מכיל 1,048,575 טוקנים, והתשובה יכולה להיות עד 384,000 טוקנים. גרסה חדשה יותר: 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.
רקע
אודות DeepSeek AI
הוקם בשנת 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.
פרמטרים
284B total / 13B active per token
הקשר
1,048,575 טוקנים
יכולות
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.
הדרכה ורישיון
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.
רישיון: Open weights under a permissive license that allows commercial use. Hosted API access via deepseek.com.
בדיקות בטיחות: DeepSeek publishes model cards but provides limited external red-teaming. Safety filters are lighter than Western frontier labs; deployers are responsible for downstream alignment.
מגבלות ידועות
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 הוא מודל של DeepSeek בקטגוריית טקסט וצ'אט. ב-Railwail אתה יכול להשתמש בו עם מפתח API דרך ה-Railwail API.
כמה עולה DeepSeek V4 Flash ב-Railwail?
ב-Railwail, DeepSeek V4 Flash עולה 0.36 $ ל-1M טוקנים קלט ו-1.44 $ ל-1M טוקנים פלט. אתה משלם על מה שכל בקשה בעצם משתמשת. השימוש משולם מקרדיטים שנרכשו מראש; 1 קרדיט שווה 0.01 $.
מה גודל חלון ההקשר של DeepSeek V4 Flash?
חלון ההקשר של DeepSeek V4 Flash מכיל 1,048,575 טוקנים. התשובה יכולה להיות עד 384,000 טוקנים.
כמה מהיר DeepSeek V4 Flash?
עדיין אין מספיק הרצות שנמדדו של DeepSeek V4 Flash ב-Railwail כדי לציין זמן הרצה. זה תלוי בקלט, בהגדרות ובעומס אצל הספק.
האם DeepSeek V4 Flash טוב יותר מ-DeepSeek V4.1 Flash?
זה תלוי במשימה. DeepSeek V4 Flash (DeepSeek) ו-DeepSeek V4.1 Flash (DeepSeek) הם שניהם מודלים בקטגוריית טקסט וצ'אט. דף ההשוואה מציג את המחירים והמפרטים שלהם זה לצד זה.
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.
