IBM
Granite 4.2 3B InstructOpen
IBM Granite 4.2 3B compact SLM optimized for on-device and low-latency inference.
Intelligence
42.0 #435
AAQI 36.0
Output Speed
148 t/s
TTFT 160 ms · ITL 6.8 ms/tok
Blended price (3:1)
$0.11 /1M
In $0.08/M · Out $0.2/M
Context
128K
≈ 320 A4 pages · max out 8,192
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
Granite 4.2 3B Instruct
Claude Mythos Preview (#1)
Average (68)
Radar axes are approximated from category scores where direct benchmark measurements are missing — see Methodology.
Strengths & Trade-offs
Where it wins
- Open weights — self-host, fine-tune and audit with no vendor lock-in
- 148 tok/s output — fast enough for real-time agents
- Sub-200ms time-to-first-token
- Very low blended price — $0.11/1M mixed tokens
Killer Use Cases
Real-time agents
Low-latency loops for voice, gaming and interactive copilots.
High-volume batch jobs
Classification, extraction and summarization at minimal cost per call.
On-prem deployment
Air-gapped and regulated-environment serving on your own hardware.
Developer Quickstart & API
Set
HF_TOKEN first — get an API keycurl https://router.huggingface.co/v1/chat/completions \
-H "Authorization: Bearer $HF_TOKEN" \
-H "content-type: application/json" \
-d '{
"model": "granite-4-2-3b",
"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-4-2-3b",
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-4-2-3b",
messages: [{ role: "user", content: "Refactor this function for O(n) time and explain the change." }],
}),
});
console.log((await res.json()).choices[0].message.content);Official Resources & Links
API deployment
Compare hosts & latency
Throughput, TTFT and cache pricing across Groq, Cerebras, Together, DeepInfra, Fireworks, Bedrock and Azure.
GroqCerebrasTogether AIDeepInfraFireworks AIAWS BedrockAzure AI
Scores by Source
Category Scores
Benchmark Results
Live Specs & Architecture
Under the hood
Dense · 3 billion
- Prompt caching supported — cached input reads at $0.01/1M tokens.
Release date
Jul 20, 2026
Architecture
Dense · 3 billion
Parameters
3 billion
License
Open Weights
Context Window
128,000 tokens
Max Output
8,192 tokens
Modalities
text
Cache Pricing
read $0.01 · write $0.1 /1M
Reasoning
—
Input Price
$0.08/M
Output Price
$0.2/M
Output Speed
148 t/s
Time to First Token
160 ms
Data Updated
Aug 28, 2026
Estimated monthly bill
Estimated monthly spend
$0.00
Calculating savings vs GPT-4o...
Cite & embed
GitHub Markdown badge
Loading badge markdown...BibTeX
@misc{llmpodium2026granite423b,
title = {Benchmark Analysis and Intelligence Evaluation of Granite 4.2 3B Instruct},
author = {LLMPodium Research},
year = {2026},
url = {https://llmpodium.com/models/granite-4-2-3b}
}Embed Score Badge
Add a live Podium Score badge to your GitHub or Hugging Face README — it links back to the live ranking.
Preview
Markdown
Loading badge snippet...HTML
Loading badge snippet...More from IBM
Alternative Models
Closest alternatives to Granite 4.2 3B Instruct based on capabilities, pricing, and benchmark scores.