DeepSeek
DeepSeek V4.1 FlashAperto
DeepSeek V4.1 Flash is an open-weights MoE reasoning model with 552B total (16B active) parameters, 1.0M context, and chain-of-thought problem solving.
Intelligenza
54.5 #379
AAQI 40.0
Velocità Uscita
214 t/s
TTFT 1.21 s · ITL 4.7 ms/tok
Prezzo combinato (3:1)
$0.18 /1M
Input $0.3/M · Output $1.2/M
Contesto
1.0M
≈ 2,500 pagine A4 · output max 32,768
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
DeepSeek V4.1 Flash
Claude Mythos Preview (#1)
Average (68)
Gli assi radar sono approssimati dai punteggi di categoria dove mancano misurazioni dirette — vedi Metodologia.
Punti di forza e compromessi
Dove vince
- Open weights — self-host, fine-tune and audit with no vendor lock-in
- 1.0M context — swallows whole repos, codebases or corpora in one pass
- 214 tok/s output — fast enough for real-time agents
- Very low blended price — $0.18/1M mixed tokens
Casi d'uso killer
Deep research
Synthesis across broad knowledge domains with citation-grade recall.
Math & proof work
Step-verified reasoning for olympiad math, theorem proving and formal verification.
SWE-bench agents
Issue-to-PR resolution across multi-file repos — refactor, test and verify autonomously.
Quickstart per sviluppatori e API
Imposta prima
DEEPSEEK_API_KEY — ottieni una chiave APIcurl https://api.deepseek.com/v1/chat/completions \
-H "Authorization: Bearer $DEEPSEEK_API_KEY" \
-H "content-type: application/json" \
-d '{
"model": "deepseek-v4-1-flash",
"messages": [{"role": "user", "content": "Refactor this function for O(n) time and explain the change."}]
}'import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.deepseek.com/v1",
api_key=os.environ["DEEPSEEK_API_KEY"],
)
resp = client.chat.completions.create(
model="deepseek-v4-1-flash",
messages=[{"role": "user", "content": "Refactor this function for O(n) time and explain the change."}],
)
print(resp.choices[0].message.content)const res = await fetch("https://api.deepseek.com/v1/chat/completions", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.DEEPSEEK_API_KEY}`,
"content-type": "application/json",
},
body: JSON.stringify({
model: "deepseek-v4-1-flash",
messages: [{ role: "user", content: "Refactor this function for O(n) time and explain the change." }],
}),
});
console.log((await res.json()).choices[0].message.content);Risorse e link ufficiali
Deployment API
Confronta host e latenza
Throughput, TTFT e prezzi cache su Groq, Cerebras, Together, DeepInfra, Fireworks, Bedrock e Azure.
GroqCerebrasTogether AIDeepInfraFireworks AIAWS BedrockAzure AI
Punteggi per Fonte
Punteggi per Categoria
Risultati dei Benchmark
Specifiche live e architettura
Sotto il cofano
Dense · 552B (16B active)
- Switchable reasoning effort — trading latency and tokens for accuracy on hard problems.
- Prompt caching supported — cached input reads at $0.03/1M tokens.
Data di Rilascio
10 set 2026
Architettura
Dense · 552B (16B active)
Parametri
552B (16B active)
Licenza
Open Weights
Finestra di Contesto
1,000,000 tokens
Output max
32,768 tokens
Modalità
text + image
Prezzi cache
lettura $0.03 · scrittura $0.35 /1M
Ragionamento
Livelli di sforzo
Prezzo Ingresso
$0.3/M
Prezzo Uscita
$1.2/M
Velocità Uscita
214 t/s
Tempo al Primo Token
1.21 s
Dati aggiornati
12 set 2026
Stima bolletta mensile
Spesa mensile stimata
$0.00
Calcolo risparmio vs GPT-4o…
Cita e incorpora
Badge Markdown GitHub
Loading badge markdown...BibTeX
@misc{llmpodium2026deepseekv41flash,
title = {Benchmark Analysis and Intelligence Evaluation of DeepSeek V4.1 Flash},
author = {LLMPodium Research},
year = {2026},
url = {https://llmpodium.com/models/deepseek-v4-1-flash}
}Incorpora il badge del punteggio
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