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Vision AI models
Browse the complete list of Vision AI models. Compare specifications, benchmark scores and provider pricing on GenAIList.
Gemini Robotics-ER 1.5
🇺🇸 Google DeepMind
Granite-Docling
🇺🇸 IBM
MultiverSeg
🇺🇸 Massachusetts Institute of Technology (MIT)
Aeneas
🇺🇸 Google DeepMind
MedSigLIP
V-JEPA 2
🇺🇸 Facebook AI Research
Eagle 2.5
🇺🇸 NVIDIA
Midjourney V7
🇺🇸 Midjourney
Stable Video 4D 2.0 (SV4D 2.0)
🇺🇸 Stability AI
SigLIP 2
🇺🇸 Google DeepMind
PaliGemma 2 3B Mix 224
PaliGemma 2 3B Mix 448
Computer-Using Agent (CUA)
🇺🇸 OpenAI
Eagle 2
🇺🇸 NVIDIA
NVILA 15B
🇺🇸 NVIDIA
NVILA 8B
🇺🇸 NVIDIA
NVLM-D 72B
🇺🇸 NVIDIA
NVLM-H 72B
🇺🇸 NVIDIA
NVLM-X 72B
🇺🇸 NVIDIA
3-ensemble of Self-ensembles on CIFAR-100
🇺🇸 Google DeepMind
CXR Foundation
OmniParser: Icon Description Model
🇺🇸 Microsoft Research
OmniParser: Interactable Region Detection Model
🇺🇸 Microsoft Research
Florence-2-B (base)
🇺🇸 Microsoft
Florence-2-L (large)
🇺🇸 Microsoft
Segment Anything Model 2
🇺🇸 Meta AI
Stable Video 4D (SV4D)
🇺🇸 Stability AI
ScribblePrompt-SAM
🇺🇸 Massachusetts Institute of Technology (MIT)
PaliGemma
🇺🇸 Google DeepMind
ColPali
🇺🇸 Illuin Technology
Flexi-JEST++
🇺🇸 Google DeepMind
JEST++
🇺🇸 Google DeepMind
DigiRL
🇺🇸 University of California (UC) Berkeley
phi-3.5-Vision
🇺🇸 Microsoft
MobileCLIP-B (LT)
🇺🇸 Apple
Stable Video 3D (SV3D)
🇺🇸 Stability AI
Derm Foundational Model
🇺🇸 Google Research
Palmyra Vision
🇺🇸 Writer
Gemini 1.0 Pro Vision
🇺🇸 Google DeepMind
V-JEPA
🇺🇸 Meta AI
YOLOv8x
🇺🇸 Ultralytics
Mobile V-MoEs
🇺🇸 Apple
ELIXR-B
ELIXR-C
Pix2Struct-Large
🇺🇸 Google Research
GELU for CIFAR-10
🇺🇸 University of California (UC) Berkeley
OpenCLIP ViT-H-14-378-quickgelu
🇺🇸 Massachusetts Institute of Technology (MIT)
DINOv2
🇺🇸 Facebook AI Research
Segment Anything Model
🇺🇸 Meta AI
SigLIP 400M
🇺🇸 Google DeepMind
SigLiT
🇺🇸 Google DeepMind
BASIC-L + Lion
ViT-22B
BLIP-2 (Q-Former)
🇺🇸 Salesforce Research
CoCa
🇺🇸 Google Research
ViT-G (model soup)
🇺🇸 University of Washington
Detic
🇺🇸 Meta AI
Florence
🇺🇸 Microsoft
BASIC-L
Swin Transformer V2 (SwinV2-G)
🇺🇸 Microsoft Research Asia
About Vision AI models
This page lists every Vision AI models tracked on GenAIList. When choosing a model, weigh raw capability against practical constraints like context window, latency, licensing and price. Open-weights and open-source models can be self-hosted and fine-tuned, while proprietary models often lead on raw quality. Compare benchmark scores on our benchmarks page and put two candidates head to head with compare.
Frequently asked questions
How do I choose the right model?
Weigh raw capability against practical constraints like context window, latency, licensing and price. Use the benchmarks page to compare rankings and the compare tool to evaluate two candidates side by side.
Where can I see benchmark scores?
Visit the benchmarks page to compare these models on standardised tests, then use the compare tool for a detailed side-by-side of any two models.