[whatmodel.sh](../../README.md) / North Micro Vision Instruct (2.4B) # North Micro Vision Instruct (2.4B) Cohere · released 2026-08 · 2.4B params · apache-2.0 **score 41.6** ±1.8 · #539 of 732 · from 13 benchmark results across 1 source · † no independently run results verified Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at ● marks North Micro Vision Instruct (2.4B) among every model on that task or test, worst to best. 
task                vs the others          rank  counts  tests
chat & writing      ▁▁▁▃▆▄●█▆▇▇▃   #261 of 402*    6.7%      1
vision & documents  ▁▁●▂▁▃▄▂▆▇█▄    #142 of 153     55%      9
chat & writing · #261 of 402 · 6.7% 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 (aggregation unspecified) …   74.9%  ▄···▄█●····▄ beats 4/5    easy · avg 78%    6.7%  at its level
vision & documents · #142 of 153 · 55% of its score Beats [Gemma 3 4B](gemma-3-4b.md), [LLaVA-OneVision Qwen2 72B (ov-sft)](llava-onevision-qwen2-72b-ov.md), [CogVLM2 Llama3 Chat 19B](cogvlm2-llama3-chat-19b.md) here, though they rank higher overall. Trails [Qwen3.5 0.8B](qwen3-5-0-8b.md) here, though they rank lower overall. 
test                                result  vs the others in its run  difficulty        counts  why
CharXiv descriptive questions (ma…   60.0%  ●·▄·······▄█ beats 1/5    easy · avg 78%       19%  at its level
MMStar (maker)                       51.8%  ▄▄●█·······▄ beats 2/5    medium · avg 61%     12%  at its level
MMBench-EN dev v1.1 (maker)          68.7%  ●·····▃··▃·▃ beats 0/5    easy · avg 79%      7.3%  blunt · at its level
HallusionBench (maker)               61.5%  ▄▄·●·····█·▄ beats 2/5    easy · avg 66%      5.9%  blunt · at its level
CountBench (maker)                   72.5%  ▄··▄··●··▄·▄ beats 2/5    easy · avg 84%      3.4%  noisy
RealWorldQA (maker)                  62.2%  ▄···█··●▄··▄ beats 3/5    easy · avg 68%      3.1%  blunt · at its level
OCRBench (maker)                     79.2%  █··███·●···█ beats 4/5    easy · avg 81%      2.3%  blunt
AI2D test (maker)                    77.5%  █···▄▄···●·▄ beats 4/5    easy · avg 81%      1.6%  blunt · at its level
ChartQA (maker)                      80.8%  ▄······▄··█● beats 4/5    easy · avg 79%      1.0%  blunt · at its level · noisy
other tests (3) 
test                                result  vs the others in its run  difficulty        counts  why
MMLU (maker)                         50.4%  ▄·····●▄··▄█ beats 1/5    medium · avg 69%     16%  at its level
MMLU-Pro (maker)                     30.7%  █●·······▄▄▄ beats 2/5    medium · avg 49%     14%  at its level
Multi-IF (aggregation unspecified…   37.3%  ▄·●··█▄····▄ beats 1/5    medium · avg 57%    5.9%  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
CohereLabs/North-Micro-Vision-Instruct …       13   99% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 41.6
Licences: CohereLabs/North-Micro-Vision-Instruct model card ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) ## How to run it ### Other ways to run it 1. Run it as published (4bit, needs ~2.17 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/North-Micro-Vision-Instruct-4bit/blob/87466363e6c5f57adf91c18c3a62c3c74765f8df/README.md)) ```sh pip install -U "mlx-vlm @ git+https://github.com/Blaizzy/mlx-vlm.git" && mlx_vlm.generate --model mlx-community/North-Micro-Vision-Instruct-4bit --image /path/to/image.jpg --prompt "Describe this image." --max-tokens 512 --temperature 0.0 ``` Model repository and documentation: [huggingface.co/CohereLabs](https://huggingface.co/CohereLabs/North-Micro-Vision-Instruct) [interactive version](https://whatmodel.sh/model/north-micro-vision-instruct) · [JSON](https://whatmodel.sh/api?format=json&all=1)