North Micro Vision Instruct
Cohere开源权重Apache 2.0 · 商用许可
描述
Compact open-weight vision-language model (2.4B) with native-resolution image support for VQA, captioning, grounding, OCR, charts, and documents. Custom 400M SigLIP 2-based vision encoder + 2B North Micro LLM backbone (Command A+ style). Multilingual and multi-image. LM context 128K; multimodal training validated to 8K. Not a reasoning/tool-calling model. Intended for prototyping and fine-tuning.
发布日期
2026-08-12
参数规模
2.4B
上下文长度
—
支持模态
—
能力雷达图
60
general
0
coding
40
reasoning
34
science估算
28
agents
70
multimodal
缺少专门科学评测时,Science 由 LLM Stats 科学得分或推理能力估算。
排行榜排名
| 领域 | #排名 | 分数 | 来源 |
|---|---|---|---|
| 多模态榜 | 148 | 12.0 | LS |
基准测试分数 (LLM Stats)
(LLM Stats (zeroeval))Chat
IFEvalGoogle Research (2023)
74.9%自报
Multi-IF
37.3%自报
General
MMLU
50.4%自报
Language
MMLU-Pro
30.7%自报
Reasoning
ChartQAMasry et al. (2022)
80.8%自报
CharXiv-D
60.0%自报
Vision
DocVQADocVQA (2020)
92.1%自报
OCRBench
79.2%自报
AI2D
77.5%自报
RefCOCO-avg
0.73 / 100自报
CountBench
0.72 / 100自报
MMBench-V1.1
68.7%自报
InfoVQA
65.2%自报
RealWorldQA
62.2%自报
Hallusion Bench
61.5%自报
BLINK
52.7%自报
MMStar
51.8%自报
OCRBench-V2 (en)
36.7%自报
MMMU (val)
32.9%自报
AA 评测指数
(Artificial Analysis)暂无 AA 评测数据
LLM Stats 分类评分
(LLM Stats (zeroeval))Image To Text70
Spatial Reasoning70
Grounding70
Chat60
Instruction Following60
Multimodal60
Reasoning60
Structured Output60
General60
Vision60
3d50
Language40
Legal40
Math40
Finance40
Healthcare40
Communication40
定价
暂无定价数据
速度
暂无速度数据
供应商价格排行
暂无提供商数据