Black Forest Labs
FLUX.2 Klein 4BAperto
Compact 4B FLUX.2 distilled for on-device and edge image generation.
Intelligenza
86.5 #38
AAQI 87.0
Velocità Uscita
90 t/s
ITL 11.1 ms/tok
Prezzo combinato (3:1)
$0.01 /1M
Input $0.01/M · Output $0.01/M
Contesto
32K
≈ 80 pagine A4 · output max 4,096
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
FLUX.2 Klein 4B
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
- Runs fully on-device — no network, no fees
- ~6GB VRAM at Q4 — fits entry GPUs
- Fastest FLUX.2 tier at 90 img/s-class throughput
A cosa fare attenzione
- Noticeably below Pro on fine detail and text
- Low-resolution ceiling vs. 32B tiers
- Struggles with long, complex prompts
Casi d'uso killer
On-device apps
Offline image generation inside mobile/desktop apps.
Edge & IoT rendering
Local synthesis on embedded hardware.
Drafting loops
Cheap instant previews before a Pro render.
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": "black-forest-labs/FLUX.2-klein-4b",
"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="black-forest-labs/FLUX.2-klein-4b",
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: "black-forest-labs/FLUX.2-klein-4b",
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
FLUX.2 Klein 4B — distilled on-device diffusion
- 4B distilled transformer — runs on phones and laptops
- Sub-second generation at 512–1024px
- Same prompt interface as Pro/Dev tiers
- Weights optimized for 6GB memory footprints
Data di Rilascio
14 ott 2026
Architettura
FLUX.2 Klein 4B — distilled on-device diffusion
Parametri
4B
Licenza
Open Weights
Finestra di Contesto
32,000 tokens
Output max
4,096 tokens
Modalità
text + image
Prezzi cache
lettura $0.004 · scrittura $0.0125 /1M
Ragionamento
—
Prezzo Ingresso
$0.01/M
Prezzo Uscita
$0.01/M
Velocità Uscita
90 t/s
Tempo al Primo Token
—
Dati aggiornati
22 ott 2026
Hardware e inferenza locale
Dati: Convly AI (CC BY 4.0) ↗VRAM (4-bit Q4)
3 GB
GPU min.
Any 6GB GPU
Stima bolletta mensile
Spesa mensile stimata
$0.00
Calcolo risparmio vs GPT-4o…
Cita e incorpora
Badge Markdown GitHub
Loading badge markdown...BibTeX
@misc{llmpodium2026flux2klein4b,
title = {Benchmark Analysis and Intelligence Evaluation of FLUX.2 Klein 4B},
author = {LLMPodium Research},
year = {2026},
url = {https://llmpodium.com/models/flux-2-klein-4b}
}Incorpora il badge del punteggio
Aggiungi un badge Podium Score live al tuo README su GitHub o Hugging Face — rimanda alla classifica live.
Anteprima
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