[whatmodel.sh](../../README.md) / Granite 4.2 30B # Granite 4.2 30B IBM · released 2026-08 · 29.3B params · apache-2.0 **score 62.6** ±1.1 · #175 of 732 · from 19 benchmark results across 2 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 30B among every model on that task or test, worst to best.}

task            vs the others          rank  counts  tests
agent tasks     ▁▁▃▆█▄●▅▄▄▂▁    #144 of 313     30%      6
coding          ▁▁▁▃▅▇█●▅▄▃▁    #156 of 469     29%      5
chat & writing  ▁▁▁▃▆▄▆█●▇▇▃   #176 of 402*     18%      2
math            ▁▁▃▅█▆▄●▄▂▁▁    #72 of 431*     14%      2

agent tasks · #144 of 313 · 30% of its score

Beats [o3](o3.md), [Gemini 2.5 Pro](gemini-2-5-pro.md), [DeepSeek V3.2 Exp](deepseek-v3-2-exp.md) here, though they rank higher overall. Trails [SmolLM3 3B](smollm3-3b.md), [Jamba 1.5 Large](jamba-1-5-large.md), [GPT-4o (2024-05-13)](gpt-4o-2024-05-13.md) here, though they rank lower overall.

test                                result  vs the others in its run   difficulty            counts  why
SWE-bench Pro (split unspecified)…   33.3%  ▂·▁▁▁▁▂●█▇▂▁ beats 16/58   very hard · avg 7.8%    7.3%  sharp · at its level · small run
SWE-bench Verified (maker)           57.0%  ▁▁▁▁▁▁▂●█▆▁▁ beats 49/138  hard · avg 22%          6.9%  sharp · at its level · small run
Terminal-Bench 2.1 (maker)           29.2%  ▁·▁·▁▁●▃▆█▅█ beats 9/63    very hard · avg 10%     6.0%  sharp · at its level · small run
SWE-bench Multilingual (maker)       41.9%  ▁··▁▃●▅██▂▂▁ beats 9/65    hard · avg 19%          4.4%  at its level · small run
BFCL V4 (December 2025 archive) (…   61.4%  ▁▁▁▂▆▆▅▆█●▄▃ beats 95/114  medium · avg 39%        3.1%  at its level · small run
τ³-bench (maker)                     62.0%  ▁▁▂▁·▁▁·▁▁●█ beats 12/25   medium · avg 38%        2.4%  at its level · small run

coding · #156 of 469 · 29% of its score

Beats [Qwen3-Coder-Next (80B-A3B)](qwen3-coder-next.md), [Gemini 2.5 Pro](gemini-2-5-pro.md), [Gemini 3.1 Flash-Lite](gemini-3-1-flash-lite.md) here, though they rank higher overall. Trails [Ministral 3 8B Reasoning 2512](ministral-3-8b-reasoning.md), [Phi-4-reasoning 14B](phi-4-reasoning.md), [DeepSeek V3 (Dec '24)](deepseek-v3.md) here, though they rank lower overall.

test                                result  vs the others in its run   difficulty            counts  why
SWE-bench Pro (split unspecified)…   33.3%  ▂·▁▁▁▁▂●█▇▂▁ beats 16/58   very hard · avg 7.8%    7.3%  sharp · at its level · small run
SWE-bench Verified (maker)           57.0%  ▁▁▁▁▁▁▂●█▆▁▁ beats 49/138  hard · avg 22%          6.9%  sharp · at its level · small run
LiveCodeBench v6 (window unspecif…   75.8%  ▂▃▄▄▅██●▄▇▄▆ beats 42/67   medium · avg 49%        6.3%  small run
SWE-bench Multilingual (maker)       41.9%  ▁··▁▃●▅██▂▂▁ beats 9/65    hard · avg 19%          4.4%  at its level · small run
SciCode (level unspecified) (make…   38.8%  ▁····▃▃▄▅●▆▂ beats 26/40   hard · avg 18%          3.8%  at its level · small run

chat & writing · #176 of 402 · 18% of its score

Beats [MiniCPM5 2B](minicpm5-2b.md) here, though they rank higher overall. Trails [LFM2.5 1.2B Instruct](lfm2-5-1-2b-instruct.md), [LFM2.5 1.2B Thinking](lfm2-5-1-2b-thinking.md), [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md) here, though they rank lower overall.

test                             result  vs the others in its run    difficulty      counts  why
LMArena (text)                   1340.1  ▁▁▁▂▃▃▃▆●▇█▃ beats 177/385  avg 1260           12%  blunt
Arena-Hard v2 aggregate (maker)   67.9%  ▁▁··▄▃▁▃●▁▁▇ beats 15/22    hard · avg 28%    6.1%  sharp · at its level · small run

math · #72 of 431 · 14% of its score

Beats [Claude Sonnet 4.6](claude-sonnet-4-6.md), [Claude Opus 4.5](claude-opus-4-5.md), [Qwen3.7 Plus](qwen3-7-plus.md) here, though they rank higher overall.

test                        result  vs the others in its run    difficulty        counts  why
AIME 2025 (maker)            89.2%  ▁·▁▁▁▂▅▆█▇●▁ beats 112/133  medium · avg 59%    9.1%  sharp · small run
HMMT February 2025 (maker)   89.2%  ▁▁▁▃▁▅▅▃●█▁▄ beats 17/31    medium · avg 58%    4.4%  small run

other tests (8)

test                               result  vs the others in its run    difficulty        counts  why
GPQA (subset unspecified) (maker)   66.4%  ▂▁▂▂▄▃▆▃▅●▃▃ beats 71/106   hard · avg 41%       10%  sharp · at its level · small run
MMLU-Pro (maker)                    77.6%  ▁▁·▁▁▁▃▂▄●█▁ beats 112/188  medium · avg 54%    5.6%  small run
ProfBench (maker)                   42.9%  █·········█● beats 2/2      hard · avg 25%      2.8%  at its level · small run
RULER 128k (maker)                  81.4%  ▃··▅▄█▅█●·▃▃ beats 26/33    medium · avg 57%    2.5%  small run
IFBench prompt-level (maker)        77.2%  █▄▄··●·▄···▄ beats 4/6      medium · avg 63%    2.4%  small run
RULER 64k (maker)                   90.0%  ▁▁·▂·▂▃▄█▂●▁ beats 25/26    easy · avg 69%      2.1%  small run
MMLU-ProX-Lite (maker)              66.6%  █·········█● beats 2/2      hard · avg 33%      1.8%  at its level · small run
BIRD (text-to-SQL) (maker)          41.9%  █··········● beats 1/1                             —  not counted yet: no other model in the …

## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its *counts* column above; here it is added up by who ran the tests. [Method](../method.md)

source                                    results  share of the score
ibm-granite/granite-4.2-30b model card …       18   87% ▇▇▇▇▇▇▇▇▇▇
LMArena leaderboard dataset                     1   12% ▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 62.6

_{Licences: 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/))}

Not counted (1)

source                  test                score  why
ibm-granite/granite-4…  BIRD (text-to-SQL)  41.9%  too few models have it yet

## How to run it ### Through an API api (DeepInfra) · $0.28/M blended · $0.16 in / $0.65 out per 1M tokens · [deepinfra.com](https://deepinfra.com/ibm-granite/granite-4.2-30b) _{[price source](https://bertrande.com/en/ai/deepinfra/ibm-granite--granite-4.2-30b): USD per 1M text tokens. DeepInfra standard synchronous API, model ibm-granite/granite-4.2-30b; 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-4.2-30b; 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-4.2-30b): 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 30B (Q4_K_M, needs ~17.7 GB, from [huggingface.co/ibm-granite](https://huggingface.co/ibm-granite/granite-4.2-30b-GGUF/tree/27b350a791e81d9a4d1ddca1c49282e9ec533768)) ```sh llama cli -hf ibm-granite/granite-4.2-30b-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-4.2-30b-GGUF:Q4_K_M ``` Other versions: Q8_0 (~31.1 GB) `llama cli -hf ibm-granite/granite-4.2-30b-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 30B (Q4_K_M, needs ~17.7 GB, from [huggingface.co/ibm-granite](https://huggingface.co/ibm-granite/granite-4.2-30b-GGUF/tree/27b350a791e81d9a4d1ddca1c49282e9ec533768)) ```sh lms get https://huggingface.co/ibm-granite/granite-4.2-30b-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 (~31.1 GB) `lms get https://huggingface.co/ibm-granite/granite-4.2-30b-GGUF@Q8_0 -y` · 4bit (~16.5 GB, Mac only) `lms get https://huggingface.co/ibm-granite/granite-4.2-30b-q4-mlx -y` · 8bit (~31.1 GB, Mac only) `lms get https://huggingface.co/ibm-granite/granite-4.2-30b-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 30B (Q4_K_M, needs ~18.08 GB, from [ollama.com](https://ollama.com/library/granite4.2/tags)) ```sh ollama run granite4.2:30b ``` Other versions: Q8_0 (~35.66 GB) `ollama run granite4.2:30b-q8_0` ### 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 30B (4bit, needs ~16.5 GB, Mac only, from [huggingface.co/ibm-granite](https://huggingface.co/ibm-granite/granite-4.2-30b-q4-mlx/tree/d3f143b440062060eb05f7b8677ced5b4d9a8946)) ```sh mlx_lm.chat --model ibm-granite/granite-4.2-30b-q4-mlx ``` 3. Or start an OpenAI-compatible server at http://localhost:8080/v1 ```sh mlx_lm.server --model ibm-granite/granite-4.2-30b-q4-mlx ``` Other versions: 8bit (~31.1 GB, Mac only) `mlx_lm.chat --model ibm-granite/granite-4.2-30b-q8-mlx` Model repository and documentation: [huggingface.co/ibm-granite](https://huggingface.co/ibm-granite/granite-4.2-30b) [interactive version](https://whatmodel.sh/model/granite-4-2-30b) · [JSON](https://whatmodel.sh/api?format=json&all=1)