DeepSeek
DeepSeek R1legacyAperto
The open reasoning model that reshaped the frontier in early 2025.
Intelligenza
5.4 #704
Fascia top 92%
Velocità Uscita
60 t/s
ITL 16.7 ms/tok
Prezzo combinato (3:1)
$0.478 /1M
Input $0.27/M · Output $1.1/M
Contesto
163.8K
≈ 410 pagine A4 · output max 16,384
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
DeepSeek R1
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
- Very low blended price — $0.478/1M mixed tokens
- Cheap prompt-cache reads at $0.027/1M
- Reasoning mode with adjustable effort levels
A cosa fare attenzione
- Legacy tier — a newer successor is available
Casi d'uso killer
Whole-repo analysis
Single-pass ingestion of large codebases, contracts or research corpora.
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-r1",
"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-r1",
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-r1",
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
Mixture-of-Experts · 671B (37B active MoE)
- Mixture-of-Experts routing — only a fraction of total parameters is active per token.
- Switchable reasoning effort — trading latency and tokens for accuracy on hard problems.
- Prompt caching supported — cached input reads at $0.027/1M tokens.
Data di Rilascio
27 mag 2025
Architettura
Mixture-of-Experts · 671B (37B active MoE)
Parametri
671B (37B active MoE)
Licenza
Open Weights
Finestra di Contesto
163,800 tokens
Output max
16,384 tokens
Modalità
text
Prezzi cache
lettura $0.027 · scrittura $0.3375 /1M
Ragionamento
Livelli di sforzo
Prezzo Ingresso
$0.27/M
Prezzo Uscita
$1.1/M
Velocità Uscita
60 t/s
Tempo al Primo Token
—
Dati aggiornati
5 ago 2026
Hardware e inferenza locale
Dati: Convly AI (CC BY 4.0) ↗VRAM (4-bit Q4)
400 GB
GPU min.
Multi-GPU server
Stima bolletta mensile
Spesa mensile stimata
$0.00
Calcolo risparmio vs GPT-4o…
Cita e incorpora
Badge Markdown GitHub
Loading badge markdown...BibTeX
@misc{llmpodium2026deepseekr1,
title = {Benchmark Analysis and Intelligence Evaluation of DeepSeek R1},
author = {LLMPodium Research},
year = {2026},
url = {https://llmpodium.com/models/deepseek-r1}
}Incorpora il badge del punteggio
Aggiungi un badge Podium Score live al tuo README su GitHub o Hugging Face — rimanda alla classifica live.
Anteprima
Markdown
Loading badge snippet...HTML
Loading badge snippet...Altri modelli di DeepSeek
Modelli alternativi
Migliori alternative a DeepSeek R1 in base a capacità, prezzi e benchmark.