[whatmodel.sh](../../README.md) / Granite 4.2 3B
# Granite 4.2 3B
IBM · released 2026-08 · 3.66B params · apache-2.0
**score 57.1** ±0.8 · #243 of 732 · from 35 benchmark results across 3 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Granite 4.2 3B among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆●▄▅▅▄▄▂▁ #191 of 313 13% 6
coding ▁▁▁▃▅▇●▇▅▄▃▁ #238 of 469 21% 6
chat & writing ▁▁▁▃▆▄▆●▆▇▇▃ #235 of 402 10% 3
math ▁▁▃▅█▆●█▄▂▁▁ #158 of 431 31% 5
web search ▅·▅▅●·▃▃▃·▅▃ #7 of 15* 2.1% 1
agent tasks · #191 of 313 · 13% of its score
Beats [o1 (2024-12-17)](o1-2024-12-17.md), [o3-mini](o3-mini.md), [DeepSeek R1 0528](deepseek-r1-0528.md) here, though they rank higher overall. Trails [SmolLM3 3B](smollm3-3b.md), [Jamba 1.5 Mini](jamba-1-5-mini.md), [Mellum2 12B-A2.5B Instruct](mellum2-12b-a2-5b-instruct.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
BFCL V4 (December 2025 archive) 52.2% ▄·█·▄▄●▄·▄·▄ beats 5/8 medium · avg 43% 4.1% at its level · several runs
SWE-bench Verified 36.8% ▄··▄▄█··▄·▄● beats 7/8 very hard · avg 11% 2.5% sharp · noisy
BrowseComp-ZH 21.1% ▄▄·▄▄▄·●···█ beats 6/8 very hard · avg 13% 2.1% noisy
SWE-bench Pro (split unspecified) 12.3% █▂···▂···●·▂ beats 6/8 very hard · avg 3.9% 1.8% sharp · noisy
τ²-bench Telecom 40.9% ▁·▅█●▅▂▃▃▂▃▁ beats 34/89 medium · avg 35% 1.3% sharp · at its level · noisy · small run
τ³-bench (maker) 45.8% ▁▁▂▁·▁▁·●▁▂█ beats 8/25 medium · avg 38% 1.2% at its level · small run
coding · #238 of 469 · 21% of its score
Beats [Qwen3.5 4B](qwen3-5-4b.md), [Qwen3-VL 32B Instruct](qwen3-vl-32b-instruct.md), [Solar Open 100B (102B-A12B)](solar-open-100b.md) here, though they rank higher overall. Trails [QwQ-32B-Preview](qwq-32b-preview.md), [InternLM2.5 20B Chat](internlm2-5-20b-chat.md), [Ministral 3 3B Reasoning 2512](ministral-3-3b-reasoning.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LiveCodeBench v6 (window unspecif… 58.9% ▃····█·▃▅●·▃ beats 7/8 medium · avg 38% 8.3% at its level · several runs
LiveCodeBench Pro 2025Q2 (Easy) 54.6% ▄···█▄··▄●▄▄ beats 6/8 hard · avg 33% 3.4% at its level
LiveCodeBench Pro 2025Q2 (Medium) 5.3% █···▂··●▂··▂ beats 6/8 very hard · avg 2.0% 3.1% sharp · too hard for it · noisy
SWE-bench Verified 36.8% ▄··▄▄█··▄·▄● beats 7/8 very hard · avg 11% 2.5% sharp · noisy
SWE-bench Pro (split unspecified) 12.3% █▂···▂···●·▂ beats 6/8 very hard · avg 3.9% 1.8% sharp · noisy
SciCode (level unspecified) (make… 24.1% ▁····▃●▄▅█▆▂ beats 7/40 hard · avg 18% 1.7% at its level · small run
chat & writing · #235 of 402 · 10% of its score
Beats [Mistral Small 4 (119B-A6.5B)](mistral-small-4.md), [Olmo 3.1 32B Think](olmo-3-1-32b-think.md), [Qwen2.5 Coder 32B Instruct](qwen2-5-coder-32b.md) here, though they rank higher overall. Trails [Flan-T5 XXL](flan-t5-xxl.md), [LFM2.5 1.2B Instruct](lfm2-5-1-2b-instruct.md), [Gemma 3 4B](gemma-3-4b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1290.0 ▁▁▁▂▃▃▃●▆▇█▃ beats 123/385 avg 1260 6.2% blunt
Arena-Hard v2 aggregate (maker) 35.0% ▁▁··●▃▁▃█▁▁▇ beats 3/22 hard · avg 28% 3.2% sharp · at its level · small run
IFEval (aggregation unspecified) 93.7% ▄▄····▄··██● beats 8/8 easy · avg 76% 0.2% noisy
math · #158 of 431 · 31% of its score
Beats [MiMo-V2.5 (310B-A15B)](mimo-v2-5.md), [MiniMax-M2.7](minimax-m2-7.md), [MiniMax-M2.1](minimax-m2-1.md) here, though they rank higher overall. Trails [Olmo 3 7B Think](olmo-3-7b-think.md), [Qwen3-VL 4B Thinking](qwen3-vl-4b-thinking.md), [Nemotron 3 Nano 30B-A3B](nemotron-3-nano-30b-a3b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
AIME 2025 79.4% █▄▄▄▄····●·▄ beats 7/8 medium · avg 59% 12% sharp · small run · several runs
HMMT February 2026 60.8% ▅··▃▃·▃··▃·● beats 6/8 hard · avg 30% 8.2% sharp · at its level
AIME 2026 83.5% ▄·▄█·▄▄···●▄ beats 7/8 medium · avg 48% 5.1% sharp · at its level
HMMT February 2025 (maker) 66.7% ▁▁▁▃▁●▅▃██▁▄ beats 8/31 medium · avg 58% 2.7% at its level · small run
MATH-500 97.0% ██▄▄▄··●···▄ beats 7/8 very easy · avg 89% 2.3% maxed out
web search · #7 of 15 · 2.1% of its score
Beats [Qwen3.5 9B](qwen3-5-9b.md), [Gemma 4 12B](gemma-4-12b.md) here, though they rank higher overall. Trails [Spark-X2.5 1.7B](spark-x2-5-1-7b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
BrowseComp-ZH 21.1% ▄▄·▄▄▄·●···█ beats 6/8 very hard · avg 13% 2.1% noisy
other tests (13)
test result vs the others in its run difficulty counts why
MMLU-Pro 65.8% ▂··▂●·▂▂···▂ beats 5/8 medium · avg 54% 6.4% at its level · small run · several runs
GPQA (subset unspecified) (maker) 54.8% ▂▁▂▂▄▃▆▃●█▃▃ beats 57/106 hard · avg 41% 5.1% sharp · at its level · small run
OJBench 21.8% ▃····▃▅··●▃▃ beats 6/8 very hard · avg 12% 2.7% typical
MMLU-Redux 78.9% ▂···●··▂▂··▂ beats 1/8 easy · avg 76% 2.7% typical
LongBench v2 36.0% ▃······▅▅●·█ beats 5/8 very hard · avg 32% 2.3% typical
SuperGPQA 39.9% ▃·▃·▃▅●····▃ beats 6/8 hard · avg 32% 2.1% at its level
RULER 128k (maker) 55.3% ▃··▅●█▅█▇·▃▃ beats 7/33 medium · avg 57% 1.5% at its level · small run
IFBench (aggregation unspecified) 73.0% ▅····▃▃·█·▃● beats 8/8 medium · avg 44% 1.4% at its level
IFBench prompt-level (maker) 74.3% █▄●··▄·▄···▄ beats 3/6 medium · avg 63% 1.4% at its level · small run
ProfBench (maker) 32.1% ●·········██ beats 0/2 hard · avg 25% 1.4% at its level · small run
RULER 64k (maker) 67.5% ▁▁·●·▂▃▄█▂▁▁ beats 2/26 easy · avg 69% 1.3% small run
MMLU-ProX-Lite (maker) 27.8% ●·········██ beats 0/2 hard · avg 33% 0.9% at its level · small run
Multi-IF (aggregation unspecified) 75.9% ▄▄···▄·▄·▄█● beats 7/8 medium · avg 60% 0.9% at its level
source results share of the score
openbmb/MiniCPM5-2B model card 20 58% ▇▇▇▇▇▇▇
ibm-granite/granite-4.2-30b model card … 14 35% ▇▇▇▇
LMArena leaderboard dataset 1 6% ▇
prior: pull toward the middle 0% ▇
weighted average = 57.1
Licences: openbmb/MiniCPM5-2B model card ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · ibm-granite/granite-4.2-30b model card ([Apache-2.0](../../licenses/ibm-granite--granite-4.2-30b.txt)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/))
## How to run it
### Through an API
api (DeepInfra) · $0.052/M blended · $0.03 in / $0.12 out per 1M tokens · [deepinfra.com](https://deepinfra.com/ibm-granite/granite-4.2-3b)
[price source](https://bertrande.com/en/ai/deepinfra/ibm-granite--granite-4.2-3b): USD per 1M text tokens. DeepInfra standard synchronous API, model ibm-granite/granite-4.2-3b; uncached input, no batch. Tools, cache storage and other modalities excluded. Provider does not specify quantization. Bertrande, https://bertrande.com/en/ai/deepinfra/ibm-granite--granite-4.2-3b; observed 2026-09-22T10:15:02Z. CC BY 4.0; units normalized and model identifier mapped. · [context source](https://bertrande.com/en/ai/deepinfra/ibm-granite--granite-4.2-3b): DeepInfra serving context limit; source parameter context\_length corroborated by provider max\_tokens. This is not an intrinsic training-length claim.
### llama.cpp (recommended)
1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md)
```sh
# macOS / Linux
curl -LsSf https://llama.app/install.sh | sh
# Windows
irm https://llama.app/install.ps1 | iex
```
2. Run Granite 4.2 3B (Q4_K_M, needs ~2.2 GB, from [huggingface.co/ibm-granite](https://huggingface.co/ibm-granite/granite-4.2-3b-GGUF/tree/c40945d71cd90f249a56985e8155551a9188dc30))
```sh
llama cli -hf ibm-granite/granite-4.2-3b-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf ibm-granite/granite-4.2-3b-GGUF:Q4_K_M
```
Other versions: Q8_0 (~3.9 GB) `llama cli -hf ibm-granite/granite-4.2-3b-GGUF:Q8_0`
### LM Studio
1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download)
2. Run Granite 4.2 3B (Q4_K_M, needs ~2.2 GB, from [huggingface.co/ibm-granite](https://huggingface.co/ibm-granite/granite-4.2-3b-GGUF/tree/c40945d71cd90f249a56985e8155551a9188dc30))
```sh
lms get https://huggingface.co/ibm-granite/granite-4.2-3b-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q8_0 (~3.9 GB) `lms get https://huggingface.co/ibm-granite/granite-4.2-3b-GGUF@Q8_0 -y` · 4bit (~2.1 GB, Mac only) `lms get https://huggingface.co/ibm-granite/granite-4.2-3b-q4-mlx -y` · 8bit (~3.9 GB, Mac only) `lms get https://huggingface.co/ibm-granite/granite-4.2-3b-q8-mlx -y`
### Ollama
1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download)
```sh
# Linux
curl -fsSL https://ollama.com/install.sh | sh
```
2. Run Granite 4.2 3B (Q4_K_M, needs ~2.7 GB, from [ollama.com](https://ollama.com/library/granite4.2/tags))
```sh
ollama run granite4.2:3b
```
### MLX (Mac)
1. Install MLX (Mac). Apple silicon Macs only, text only.
```sh
# with uv
uv tool install mlx-lm
# or
pip install mlx-lm
```
2. Run Granite 4.2 3B (4bit, needs ~2.1 GB, Mac only, from [huggingface.co/ibm-granite](https://huggingface.co/ibm-granite/granite-4.2-3b-q4-mlx/tree/0c6f39b1827afd5eb2c1c3b13751929857434953))
```sh
mlx_lm.chat --model ibm-granite/granite-4.2-3b-q4-mlx
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model ibm-granite/granite-4.2-3b-q4-mlx
```
Other versions: 8bit (~3.9 GB, Mac only) `mlx_lm.chat --model ibm-granite/granite-4.2-3b-q8-mlx`
Model repository and documentation: [huggingface.co/ibm-granite](https://huggingface.co/ibm-granite/granite-4.2-3b)
[interactive version](https://whatmodel.sh/model/granite-4-2-3b) · [JSON](https://whatmodel.sh/api?format=json&all=1)