Open Source
Quasar 438BTerbuka
Massive Mixture-of-Experts open-weight model with 38B active parameters per token.
Inteligensi
0.0 #767
Tingkat 100% teratas
Kecepatan output
95 t/s
ITL 10.5 ms/tok
Harga gabungan (3:1)
$0.7 /1M
Input $0.4/M · Output $1.6/M
Konteks
128K
≈ 328 halaman A4 · output maks 32,768
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
Quasar 438B
Claude Mythos Preview (#1)
Average (68)
Sumbu radar diaproksimasi dari skor kategori jika pengukuran benchmark langsung tidak tersedia — lihat Metodologi.
Kelebihan & Trade-off
Di mana ia menang
- 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
Kasus Penggunaan 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 Developer & API
Atur
HF_TOKEN dulu — dapatkan kunci 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);Sumber Daya & Tautan Resmi
Deployment API
Bandingkan host & latensi
Throughput, TTFT, dan harga cache di Groq, Cerebras, Together, DeepInfra, Fireworks, Bedrock, dan Azure.
GroqCerebrasTogether AIDeepInfraFireworks AIAWS BedrockAzure AI
Skor per sumber
Skor per kategori
Hasil benchmark
Spesifikasi Live & Arsitektur
Di balik kap
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.
Tanggal rilis
1 Agu 2026
Arsitektur
Dense · 438B (38B active)
Parameter
438B (38B active)
Lisensi
Open Weights
Jendela konteks
131,072 tokens
Output maks
32,768 tokens
Modalitas
text
Harga cache
baca $0.04 · tulis $0.5 /1M
Penalaran
Level effort
Harga input
$0.4/M
Harga output
$1.6/M
Kecepatan output
95 t/s
Waktu ke token pertama
—
Data diperbarui
26 Okt 2026
Estimasi tagihan bulanan
Estimasi pengeluaran bulanan
$0.00
Menghitung penghematan vs GPT-4o…
Kutip & sematkan
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}
}Sematkan Lencana Skor
Tambahkan lencana Podium Score langsung ke README GitHub atau Hugging Face Anda — menautkan ke peringkat langsung.
Pratinjau
Markdown
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