Open Source
Quasar 438BAperto
Massive Mixture-of-Experts open-weight model with 38B active parameters per token.
Intelligenza
0.0 #767
Fascia top 100%
Velocità Uscita
95 t/s
ITL 10.5 ms/tok
Prezzo combinato (3:1)
$0.7 /1M
Input $0.4/M · Output $1.6/M
Contesto
128K
≈ 328 pagine A4 · output max 32,768
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
Quasar 438B
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 — $0.7/1M mixed tokens
- Cheap prompt-cache reads at $0.04/1M
- Reasoning mode with adjustable effort levels
Casi d'uso killer
Math & proof work
Step-verified reasoning for olympiad math, theorem proving and formal verification.
On-prem deployment
Air-gapped and regulated-environment serving on your own hardware.
Quickstart per sviluppatori e API
Imposta prima
HF_TOKEN — ottieni una chiave APIcurl https://router.huggingface.co/v1/chat/completions \
-H "Authorization: Bearer $HF_TOKEN" \
-H "content-type: application/json" \
-d '{
"model": "quasar-438b",
"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://router.huggingface.co/v1",
api_key=os.environ["HF_TOKEN"],
)
resp = client.chat.completions.create(
model="quasar-438b",
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://router.huggingface.co/v1/chat/completions", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.HF_TOKEN}`,
"content-type": "application/json",
},
body: JSON.stringify({
model: "quasar-438b",
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 · 438B (38B active)
- Switchable reasoning effort — trading latency and tokens for accuracy on hard problems.
- Prompt caching supported — cached input reads at $0.04/1M tokens.
Data di Rilascio
1 ago 2026
Architettura
Dense · 438B (38B active)
Parametri
438B (38B active)
Licenza
Open Weights
Finestra di Contesto
131,072 tokens
Output max
32,768 tokens
Modalità
text
Prezzi cache
lettura $0.04 · scrittura $0.5 /1M
Ragionamento
Livelli di sforzo
Prezzo Ingresso
$0.4/M
Prezzo Uscita
$1.6/M
Velocità Uscita
95 t/s
Tempo al Primo Token
—
Dati aggiornati
26 ott 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{llmpodium2026quasar438b,
title = {Benchmark Analysis and Intelligence Evaluation of Quasar 438B},
author = {LLMPodium Research},
year = {2026},
url = {https://llmpodium.com/models/quasar-438b}
}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...Modelli alternativi
Migliori alternative a Quasar 438B in base a capacità, prezzi e benchmark.