[whatmodel.sh](../../README.md) / Granite 4.0 H 1B # Granite 4.0 H 1B IBM · released 2025-10 · 1.46B params · apache-2.0 **score 42.0** ±0.8 · #533 of 732 · from 29 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 4.0 H 1B among every model on that task or test, worst to best. 
task            vs the others          rank  counts  tests
agent tasks     ▁▁▃●█▄▅▅▄▄▂▁   #258 of 313*    1.8%      1
coding          █··········●        #1 of 4     25%      7
chat & writing  ▁▁▁▃▆▄●█▆▇▇▃    #244 of 402    6.3%      3
math            ▁▁▃▅●▆▄█▄▂▁▁    #332 of 431     30%      7
agent tasks · #258 of 313 · 1.8% of its score Beats [DeepSeek R1 0528 Qwen3 8B](deepseek-r1-0528-qwen3-8b.md), [Magistral Small 1.0 (2506)](magistral-small-2506.md), [Llama 3.3 70B Instruct](llama-3-3-70b.md) here, though they rank higher overall. 
test     result  vs the others in its run  difficulty        counts  why
BFCL v3   50.7%  ▄▄·······▄●█ beats 3/6    medium · avg 62%    1.8%  blunt · at its level · several runs
coding · #1 of 4 · 25% of its score Beats [Granite 4.0 1B](granite-4-0-1b.md) here, though they rank higher overall. 
test                  result  vs the others in its run    difficulty           counts  why
HumanEval (maker)      73.0%  ▁▁▁▁▂▂▆▆●▅▂▁ beats 148/205  very easy · avg 89%    6.6%  sharp · at its level
HumanEval+ (maker)     68.0%  ▁·▁▁▂▃▄▄●▅▄▃ beats 73/145   very easy · avg 86%    6.0%  sharp · at its level
BigCodeBench (maker)   29.1%  ▅····▃·●▃▅█▃ beats 4/12     medium · avg 40%       3.0%  at its level
MultiPL-E (maker)      36.1%  ▃··●·▅▅▇▄█▃▄ beats 4/31     medium · avg 51%       2.9%  at its level
MBPP (maker)           69.0%  ▁▁▁▄▅█▆●▄▁▂▁ beats 66/86    easy · avg 81%         2.8%  at its level
MBPP+ (maker)          60.0%  ▁··▁▁▃▄●▆▄▁▁ beats 42/120   easy · avg 72%         2.1%  at its level
CRUXEval-O (maker)     36.0%  ▂▂●█▅▂▁·▁·▁▁ beats 17/51    medium · avg 48%       1.5%  blunt · at its level
chat & writing · #244 of 402 · 6.3% of its score Beats [Mistral Small 4 (119B-A6.5B)](mistral-small-4.md), [Qwen2.5 Coder 32B Instruct](qwen2-5-coder-32b.md), [Ministral 3 14B Reasoning 2512](ministral-3-14b-reasoning.md) here, though they rank higher overall. Trails [Flan-T5 XXL](flan-t5-xxl.md) here, though they rank lower overall. 
test                                result  vs the others in its run  difficulty           counts  why
IFEval mean of four measures         80.1%  █·█··█·█●·██ beats 4/6    very easy · avg 86%    2.7%  typical
IFEval prompt-level strict (maker)   74.7%  ▁▁··▂··●▇█▃▃ beats 5/26   easy · avg 81%         1.8%  at its level
IFEval instruction-level strict (…   82.4%  ▄▄····▄●█▄·█ beats 3/8    very easy · avg 88%    1.7%  typical
math · #332 of 431 · 30% of its score Beats [GPT-4 (0613)](gpt-4-0613.md), [Athene-70B](athene-70b.md), [Llama 3 70B Instruct](llama-3-70b.md) here, though they rank higher overall. Trails [Stable Code 3B](stable-code-3b.md), [Zephyr 7B beta](zephyr-7b-beta.md), [Qwen3 0.6B](qwen3-0-6b.md) here, though they rank lower overall. 
test                                result  vs the others in its run  difficulty           counts  why
GSM8K                                69.6%  █····▄●▄···█ beats 3/6    very easy · avg 87%     15%  sharp · at its level · several runs
DeepMind Mathematics (maker)         35.0%  ▅····●·█▃▃·▅ beats 3/10   medium · avg 50%       4.0%  at its level
Minerva MATH (maker)                 49.4%  ▁·▃▁▁▂▁█●▁▃▃ beats 20/27  medium · avg 59%       3.0%  at its level
GSM-Symbolic (maker)                 65.7%  ▃▃····●·▅▅█▃ beats 2/10   easy · avg 78%         2.7%  at its level
MGSM, 5 languages (en, es, fr, ja…   27.5%  █·●▄██···█·▄ beats 2/10   medium · avg 46%       2.1%  at its level
AIME 2025                             1.0%  ▄●··▄···▄··█ beats 1/6    hard · avg 30%         1.8%  sharp · noisy
MATH-500                             47.2%  ▄··█●·▄···▄▄ beats 3/6    easy · avg 77%         1.6%  at its level · noisy
other tests (8) 
test                               result  vs the others in its run   difficulty        counts  why
BIG-Bench Hard (maker)              59.7%  ▁▁▁▁▂▄▅●█▃▃▁ beats 86/155  easy · avg 75%      7.8%  sharp · at its level
GPQA (subset unspecified) (maker)   29.7%  ▂▁▂▂●▃▆▃▅█▃▃ beats 19/106  hard · avg 37%      7.2%  sharp · too hard for it
MMLU (maker)                        59.7%  ▁▁▁▁▁▂▂▅●█▆▄ beats 85/214  easy · avg 74%      6.6%  at its level
MMLU-Pro                            27.6%  █···█·●█·███ beats 2/6     medium · avg 46%    5.9%  several runs
INCLUDE (maker)                     43.4%  ▂····●▂▄▄▄█▄ beats 4/36    medium · avg 52%    2.9%  typical
MMMLU (maker)                       49.4%  ▂▁▁▂▃●▄▄▅██▃ beats 10/46   medium · avg 56%    2.4%  at its level
GPQA Diamond                        24.3%  ●··········█ beats 0/5     hard · avg 40%      2.1%  sharp · too hard for it · noisy
IFBench (aggregation unspecified)   24.9%  ▄·█·●······█ beats 3/6     medium · avg 42%    2.0%  at its level
## 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-4.0-h-1b model card…       21   79% ▇▇▇▇▇▇▇▇▇
LiquidAI/LFM2.5-1.2B-Thinking model card        8   21% ▇▇▇
prior: pull toward the middle                        0% ▇
weighted average                                   = 42.0
Licences: ibm-granite/granite-4.0-h-1b model card ([Apache-2.0](https://huggingface.co/ibm-granite/granite-4.0-h-1b/blob/d18cca4c121edb87d022116d281ce212c9136f57/README.md)) · LiquidAI/LFM2.5-1.2B-Thinking model card ([LFM Open License v1.0](../../licenses/cards-gap-NOTICE.txt)) ## How to run it ### 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 4.0 H 1B (Q4_K_M, needs ~0.9 GB, from [huggingface.co/ibm-granite](https://huggingface.co/ibm-granite/granite-4.0-h-1b-GGUF/tree/c2cb1972f511add21f3bae244990b8ff3a3ffb23)) ```sh llama cli -hf ibm-granite/granite-4.0-h-1b-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-4.0-h-1b-GGUF:Q4_K_M ``` ### 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 4.0 H 1B (Q4_K_M, needs ~0.9 GB, from [huggingface.co/ibm-granite](https://huggingface.co/ibm-granite/granite-4.0-h-1b-GGUF/tree/c2cb1972f511add21f3bae244990b8ff3a3ffb23)) ```sh lms get https://huggingface.co/ibm-granite/granite-4.0-h-1b-GGUF@Q4_K_M -y ``` 3. Then run lms ls to see its name, and lms chat followed by that name Other versions: 4bit (~0.8 GB, Mac only) `lms get https://huggingface.co/mlx-community/granite-4.0-h-1b-4bit -y` · 8bit (~1.6 GB, Mac only) `lms get https://huggingface.co/mlx-community/granite-4.0-h-1b-8bit -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 4.0 H 1B (Q8_0, needs ~1.6 GB, from [ollama.com](https://ollama.com/library/granite4/tags)) ```sh ollama run granite4:1b-h ``` ### 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 4.0 H 1B (4bit, needs ~0.8 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/granite-4.0-h-1b-4bit/tree/a5a21e23f01a461f501dcd2b7a34c9efc6fba6a6)) ```sh mlx_lm.chat --model mlx-community/granite-4.0-h-1b-4bit ``` 3. Or start an OpenAI-compatible server at http://localhost:8080/v1 ```sh mlx_lm.server --model mlx-community/granite-4.0-h-1b-4bit ``` Other versions: 8bit (~1.6 GB, Mac only) `mlx_lm.chat --model mlx-community/granite-4.0-h-1b-8bit` Model repository and documentation: [huggingface.co/ibm-granite](https://huggingface.co/ibm-granite/granite-4.0-h-1b) [interactive version](https://whatmodel.sh/model/granite-4-0-h-1b) · [JSON](https://whatmodel.sh/api?format=json&all=1)