[whatmodel.sh](../../README.md) / Granite 4.2 8B # Granite 4.2 8B IBM · released 2026-08 · 8.79B params · apache-2.0 **score 59.4** ±1.1 · #210 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 8B among every model on that task or test, worst to best. 
task            vs the others          rank  counts  tests
agent tasks     ▁▁▃▆█●▅▅▄▄▂▁    #169 of 313     28%      6
coding          ▁▁▁▃▅▇█●▅▄▃▁    #199 of 469     28%      5
chat & writing  ▁▁▁▃▆▄▆●▆▇▇▃   #232 of 402*     18%      2
math            ▁▁▃▅█▆▄●▄▂▁▁   #120 of 431*     15%      2
agent tasks · #169 of 313 · 28% 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), [Jamba 1.5 Large](jamba-1-5-large.md) here, though they rank lower overall. 
test                                result  vs the others in its run   difficulty            counts  why
SWE-bench Verified (maker)           47.7%  ▁▁▁▁▁▁▂●█▆▁▁ beats 38/138  hard · avg 22%          6.7%  sharp · at its level · small run
SWE-bench Pro (split unspecified)…   19.1%  ▂·▁▁▁▁●▃█▇▂▁ beats 11/58   very hard · avg 7.8%    6.6%  sharp · small run
Terminal-Bench 2.1 (maker)           20.6%  ▁·▁·▁▁●▃▆█▅█ beats 5/63    very hard · avg 10%     5.5%  sharp · at its level · small run
SWE-bench Multilingual (maker)       30.8%  ▁··▁●▁▅██▂▂▁ beats 7/65    hard · avg 19%          4.1%  at its level · small run
BFCL V4 (December 2025 archive) (…   52.4%  ▁▁▁▂▆▆▅▆●▂▄▃ beats 75/114  medium · avg 39%        3.1%  at its level · small run
τ³-bench (maker)                     58.1%  ▁▁▂▁·▁▁·▁●▂█ beats 11/25   medium · avg 38%        2.3%  at its level · small run
coding · #199 of 469 · 28% of its score Beats [Gemini 2.5 Flash](gemini-2-5-flash.md), [Qwen3 30B-A3B Instruct 2507](qwen3-30b-a3b-instruct-2507.md), [Qwen3.5 4B](qwen3-5-4b.md) here, though they rank higher overall. Trails [QwQ-32B-Preview](qwq-32b-preview.md), [DeepSeek-Coder-V2 Instruct (0724)](deepseek-coder-v2-0724.md), [Ministral 3 8B Reasoning 2512](ministral-3-8b-reasoning.md) here, though they rank lower overall. 
test                                result  vs the others in its run   difficulty            counts  why
LiveCodeBench v6 (window unspecif…   73.2%  ▂▃▄▄▅██●▄▇▄▆ beats 39/67   medium · avg 49%        6.8%  at its level · small run
SWE-bench Verified (maker)           47.7%  ▁▁▁▁▁▁▂●█▆▁▁ beats 38/138  hard · avg 22%          6.7%  sharp · at its level · small run
SWE-bench Pro (split unspecified)…   19.1%  ▂·▁▁▁▁●▃█▇▂▁ beats 11/58   very hard · avg 7.8%    6.6%  sharp · small run
SWE-bench Multilingual (maker)       30.8%  ▁··▁●▁▅██▂▂▁ beats 7/65    hard · avg 19%          4.1%  at its level · small run
SciCode (level unspecified) (make…   36.1%  ▁····▃▃▄▅●▆▂ beats 20/40   hard · avg 18%          3.4%  at its level · small run
chat & writing · #232 of 402 · 18% of its score 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)                   1289.7  ▁▁▁▂▃▃▃●▆▇█▃ beats 122/385  avg 1260           12%  blunt
Arena-Hard v2 aggregate (maker)   65.2%  ▁▁··▄▃▁▃●▁▁▇ beats 12/22    hard · avg 28%    6.1%  sharp · at its level · small run
math · #120 of 431 · 15% of its score Beats [Claude Sonnet 4.6](claude-sonnet-4-6.md), [MiniMax-M3](minimax-m3.md), [MiMo-V2.5 (310B-A15B)](mimo-v2-5.md) here, though they rank higher overall. Trails [Olmo 3.1 32B Think](olmo-3-1-32b-think.md) here, though they rank lower overall. 
test                        result  vs the others in its run    difficulty        counts  why
AIME 2025 (maker)            86.7%  ▁·▁▁▁▂▅▆█●▅▁ beats 104/133  medium · avg 59%     11%  sharp · small run
HMMT February 2025 (maker)   78.3%  ▁▁▁▃▁▅●▃██▁▄ beats 13/31    medium · avg 58%    4.8%  small run
other tests (8) 
test                               result  vs the others in its run   difficulty        counts  why
GPQA (subset unspecified) (maker)   64.1%  ▂▁▂▂▄▃▆▃●█▃▃ beats 68/106  hard · avg 41%       10%  sharp · at its level · small run
MMLU-Pro (maker)                    74.0%  ▁▁·▁▁▁▃▂▄●█▁ beats 99/188  medium · avg 54%    5.9%  at its level · small run
ProfBench (maker)                   41.2%  █·········●█ beats 1/2     hard · avg 25%      2.7%  at its level · small run
RULER 128k (maker)                  71.4%  ▃··▅▄█●█▇·▃▃ beats 17/33   medium · avg 57%    2.6%  at its level · small run
IFBench prompt-level (maker)        79.3%  █▄▄··▄·●···▄ beats 5/6     medium · avg 63%    2.5%  small run
RULER 64k (maker)                   81.0%  ▁▁·▂·▂▃●█▂▁▁ beats 10/26   easy · avg 69%      2.2%  small run
MMLU-ProX-Lite (maker)              61.1%  █·········●█ beats 1/2     hard · avg 33%      1.8%  at its level · small run
BIRD (text-to-SQL) (maker)          41.1%  ●··········█ beats 0/1                            —  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   88% ▇▇▇▇▇▇▇▇▇▇▇
LMArena leaderboard dataset                     1   12% ▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 59.4
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)  41.1%  too few models have it yet
## How to run it ### Through an API api (Weights & Biases) · $0.11/M blended · $0.1 in / $0.15 out per 1M tokens · [wandb.ai](https://wandb.ai/site/pricing/tokens/) [price source](https://github.com/BerriAI/litellm/blob/c19ce71bcdad234ca87a7af10cee45375ce811a8/model_prices_and_context_window.json): USD per 1M text tokens, standard uncached input and output. Product: W&B Inference. API model: ibm-granite/granite-4.2-8b. Settings: service tier=standard, batch=no, cache=uncached input. Additional/excluded charges: Tools, cache storage, priority service and other modalities excluded.. Currency: USD. · [context source](https://github.com/BerriAI/litellm/blob/c19ce71bcdad234ca87a7af10cee45375ce811a8/model_prices_and_context_window.json): provider maximum input tokens; not an intrinsic model maximum or legacy max\_tokens ### 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 8B (Q4_K_M, needs ~5.3 GB, from [huggingface.co/ibm-granite](https://huggingface.co/ibm-granite/granite-4.2-8b-GGUF/tree/93f3f6a8938ee922b784cf4e5b4203cd3428df8f)) ```sh llama cli -hf ibm-granite/granite-4.2-8b-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-4.2-8b-GGUF:Q4_K_M ``` Other versions: Q8_0 (~9.3 GB) `llama cli -hf ibm-granite/granite-4.2-8b-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 8B (Q4_K_M, needs ~5.3 GB, from [huggingface.co/ibm-granite](https://huggingface.co/ibm-granite/granite-4.2-8b-GGUF/tree/93f3f6a8938ee922b784cf4e5b4203cd3428df8f)) ```sh lms get https://huggingface.co/ibm-granite/granite-4.2-8b-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 (~9.3 GB) `lms get https://huggingface.co/ibm-granite/granite-4.2-8b-GGUF@Q8_0 -y` · 4bit (~4.9 GB, Mac only) `lms get https://huggingface.co/ibm-granite/granite-4.2-8b-q4-mlx -y` · 8bit (~9.3 GB, Mac only) `lms get https://huggingface.co/ibm-granite/granite-4.2-8b-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 8B (Q4_K_M, needs ~5.77 GB, from [ollama.com](https://ollama.com/library/granite4.2/tags)) ```sh ollama run granite4.2:8b ``` ### 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 8B (4bit, needs ~4.9 GB, Mac only, from [huggingface.co/ibm-granite](https://huggingface.co/ibm-granite/granite-4.2-8b-q4-mlx/tree/9047f073e46c527f70dade07bb55b1700d728e36)) ```sh mlx_lm.chat --model ibm-granite/granite-4.2-8b-q4-mlx ``` 3. Or start an OpenAI-compatible server at http://localhost:8080/v1 ```sh mlx_lm.server --model ibm-granite/granite-4.2-8b-q4-mlx ``` Other versions: 8bit (~9.3 GB, Mac only) `mlx_lm.chat --model ibm-granite/granite-4.2-8b-q8-mlx` Model repository and documentation: [huggingface.co/ibm-granite](https://huggingface.co/ibm-granite/granite-4.2-8b) [interactive version](https://whatmodel.sh/model/granite-4-2-8b) · [JSON](https://whatmodel.sh/api?format=json&all=1)