IBM
Granite 4.2 30B InstructAperto
IBM Granite 4.2 30B enterprise open foundation model optimized for coding and tool calling.
Intelligenza
58.6 #350
AAQI 51.0
Velocità Uscita
78 t/s
TTFT 280 ms · ITL 12.8 ms/tok
Prezzo combinato (3:1)
$0.6 /1M
Input $0.4/M · Output $1.2/M
Contesto
128K
≈ 320 pagine A4 · output max 8,192
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
Granite 4.2 30B Instruct
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
- Sub-400ms time-to-first-token
- Very low blended price — $0.6/1M mixed tokens
- Cheap prompt-cache reads at $0.05/1M
Casi d'uso killer
SWE-bench agents
Issue-to-PR resolution across multi-file repos — refactor, test and verify autonomously.
Terminal & computer-use agents
Plans and executes multi-tool workflows — shells, browsers and desktop control.
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": "granite-4-2-30b",
"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="granite-4-2-30b",
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: "granite-4-2-30b",
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 · 30 billion
- Prompt caching supported — cached input reads at $0.05/1M tokens.
Data di Rilascio
20 lug 2026
Architettura
Dense · 30 billion
Parametri
30 billion
Licenza
Open Weights
Finestra di Contesto
128,000 tokens
Output max
8,192 tokens
Modalità
text + image
Prezzi cache
lettura $0.05 · scrittura $0.5 /1M
Ragionamento
—
Prezzo Ingresso
$0.4/M
Prezzo Uscita
$1.2/M
Velocità Uscita
78 t/s
Tempo al Primo Token
280 ms
Dati aggiornati
28 ago 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{llmpodium2026granite4230b,
title = {Benchmark Analysis and Intelligence Evaluation of Granite 4.2 30B Instruct},
author = {LLMPodium Research},
year = {2026},
url = {https://llmpodium.com/models/granite-4-2-30b}
}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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