Black Forest Labs
FLUX.2 Klein 9BAperto
9B FLUX.2 klein tier — near-Pro quality on a single consumer GPU.
Intelligenza
88.2 #28
AAQI 88.0
Velocità Uscita
55 t/s
ITL 18.2 ms/tok
Prezzo combinato (3:1)
$0.02 /1M
Input $0.02/M · Output $0.02/M
Contesto
64K
≈ 160 pagine A4 · output max 4,096
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
FLUX.2 Klein 9B
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
- Best quality-per-GB in the FLUX.2 family
- Runs on RTX 3060-class hardware
- Open weights — fine-tune and self-host freely
A cosa fare attenzione
- Still trails Dev/Pro on typography and faces
- Half the speed of the 4B klein tier
- 2K output ceiling
Casi d'uso killer
Hobbyist & indie pipelines
Pro-adjacent images on consumer cards.
Fine-tuned niche styles
Small enough to iterate LoRAs overnight.
Local production tools
Self-hosted rendering for studios and agencies.
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-9b",
"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-9b",
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-9b",
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 9B — consumer-GPU diffusion
- 9B distilled transformer — the klein quality tier
- Fits 8GB VRAM at Q4
- Near-Pro prompt adherence on standard scenes
- Full LoRA fine-tuning support
Data di Rilascio
14 ott 2026
Architettura
FLUX.2 Klein 9B — consumer-GPU diffusion
Parametri
9B
Licenza
Open Weights
Finestra di Contesto
64,000 tokens
Output max
4,096 tokens
Modalità
text + image
Prezzi cache
lettura $0.008 · scrittura $0.025 /1M
Ragionamento
—
Prezzo Ingresso
$0.02/M
Prezzo Uscita
$0.02/M
Velocità Uscita
55 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)
6 GB
GPU min.
RTX 3060 8GB / any 8GB 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{llmpodium2026flux2klein9b,
title = {Benchmark Analysis and Intelligence Evaluation of FLUX.2 Klein 9B},
author = {LLMPodium Research},
year = {2026},
url = {https://llmpodium.com/models/flux-2-klein-9b}
}Incorpora il badge del punteggio
Aggiungi un badge Podium Score live al tuo README su GitHub o Hugging Face — rimanda alla classifica live.
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