Мультимодальні моделі

Anthropic's April 2026 flagship. 87.6% on SWE-bench Verified, 3x higher image resolution, output self-verification, vision + reasoning.

reasoningagenticvision

Anthropic's balanced mid-tier model from February 2026. Best price/performance for production workloads: 5x cheaper than Opus, near-flagship quality.

balancedproductionagentic

Google's April 2026 fast multimodal model. Combines Gemini 3 Pro's reasoning with Flash-tier latency and price. Default model in the Gemini app.

balancedmultimodallow-latency

Google DeepMind's February 2026 flagship. 1M-token (1,048,576) context, native multimodal (text/image/audio/video), Deep Think reasoning.

multimodaldeep-thinklong-context

OpenAI's unified flagship combining GPT and o-series reasoning into one model. 1M context, multimodal, top SWE-Bench Pro and OSWorld scores.

reasoningagenticvision

OpenAI's efficient mid-tier model. 2x faster than its predecessor, 400k context, approaches GPT-5.4 quality on SWE-Bench Pro at a fraction of the cost.

balancedcost-efficientvision

Salesforce BLIP. Vision-language model for image captioning and visual question answering. Given an image it writes a short natural-language caption, or answers a question about the image when one is supplied. A widely used baseline for automatic captioning.

blipcaptioningvqa

pharmapsychotic's CLIP Interrogator. Takes an image and produces a Stable-Diffusion-style text prompt by combining BLIP captioning with CLIP to rank likely subjects, artists, mediums and styles. Commonly used to reverse-engineer a prompt from an existing picture.

clip-interrogatorcaptioningtagging

Monocular depth-estimation model trained on 595k labeled and 62M unlabeled images. Strong zero-shot generalization in indoor and outdoor scenes.

depthvision-understandingopen-weights

Meta Segment Anything 2. Promptable segmentation across images and video with temporal memory. Zero-shot, point/box/mask prompts, fast on a single H100.

segmentationvision-understandingopen-weights

Anthropic's fastest and cheapest 4.x model. Strong vision and tool use at ultra-low latency, ideal for high-concurrency workloads.

cost-efficientlow-latencyvision

OpenAI's smallest and cheapest GPT-5.4 variant. Built for high-volume classification, extraction and coding subagents at edge-grade latency.

cost-efficientlow-latencyvision

Tsinghua CogVLM2 19B with Llama-3 8B base plus 11B vision expert. Strong document understanding and visual reasoning, 8k context.

multimodalvision-understandingtsinghua

DeepSeek-VL 7B chat model. Vision-language model with hybrid vision encoder and strong real-world visual question answering performance.

multimodalvision-understandingopen-weights

Naver CLOVA Donut OCR-free document-understanding transformer. End-to-end JSON extraction from forms, receipts and invoices without explicit OCR.

ocrvision-understandingnaver

Rednote Hilab Dots OCR. End-to-end document parsing model with layout, text and reading-order prediction in one transformer.

ocrvision-understandingopen-source

JaidedAI EasyOCR. Simple Python OCR wrapper supporting 80+ languages with deep-learning text detection and recognition.

ocrvision-understandingmultilingual

Microsoft Florence-2 Large. Unified prompt-based vision foundation model for captioning, detection, segmentation and OCR with a single 770M-param backbone.

multimodalvision-understandingmicrosoft

Global-Local Path Networks depth-estimation model. Combines hierarchical transformer encoder with selective feature fusion for sharp boundaries.

depthvision-understandingopen-source

StepFun GOT-OCR 2.0. Unified end-to-end OCR-2.0 model handling text, formulas, charts, sheet music and geometric shapes in one architecture.

ocrvision-understandingstepfun

Grounding DINO plus SAM. Open-vocabulary text-prompted detection and segmentation in one pipeline for fully-automatic mask generation.

segmentationvision-understandingopen-vocabulary

Hugging Face Idefics3 8B. Llama-3 based open-source vision-language model with strong document QA and chart-understanding performance.

multimodalvision-understandingopen-weights

Meta Llama 3.2 11B Vision served via Ollama on Replicate. Open-weights multimodal model for image captioning, document and chart reading, and visual question answering.

metallamavision-understanding

Meta Llama 3.2 90B Vision. Largest open-weights Llama vision model. Strong visual reasoning, chart, OCR and document understanding.

multimodalvision-understandingmeta

LLaVA 1.6 (LLaVA-NeXT) with a Vicuna-13B language backbone. Open vision-language chat model that describes images, answers questions, reads charts and reasons about scenes. Version 1.6 adds higher input resolution and better OCR and reasoning than LLaVA 1.5.

llavacaptioningvqa

Lotus generative depth model. Treats depth as a generation task using a diffusion model, producing higher-fidelity depth on textured surfaces.

depthvision-understandingdiffusion

ETH Zurich Marigold. Diffusion-based monocular depth-estimation model fine-tuned from Stable Diffusion with strong fine-detail recovery.

depthvision-understandingdiffusion

Meta Mask2Former universal image-segmentation transformer. Single architecture for panoptic, instance and semantic segmentation tasks.

segmentationvision-understandingtransformer

Intel MiDaS v3.1 relative depth-estimation model. Robust zero-shot single-image depth across diverse domains and resolutions.

depthvision-understandingintel

OpenBMB MiniCPM-V 2.6. 8B vision-language model with strong single-image, multi-image and video understanding plus OCR capabilities.

multimodalvision-understandingopen-weights

Allen AI Molmo 7B-D on Replicate. Open vision-language model trained on the PixMo data, notable for pointing at and locating objects in images, not just describing them.

allenaimolmovision-understanding

Moondream2 small vision-language model on Replicate. About 1.9B params, designed to run on edge devices, handles captioning, visual QA and short OCR-style reads at very low cost.

moondreamvision-understandingopen-source

Allen AI olmOCR. Open-source 7B vision-language model fine-tuned for high-fidelity document parsing including math, code and tables.

ocrvision-understandingallenai

CMU OpenPose multi-person 2D pose estimator. Real-time keypoint detection for body, hand, face and foot using Part Affinity Fields.

posevision-understandingcmu

Baidu PaddleOCR v3 PP-OCR pipeline. Lightweight detector plus recognizer optimized for production use with 80+ language support.

ocrvision-understandingbaidu

Alibaba Qwen2-VL 7B served on Replicate. Open-weights vision-language model that chats about images and video, with dynamic resolution and strong OCR and document QA for its size.

qwenalibabavision-understanding

NVIDIA SegFormer-B5 semantic segmentation. Hierarchical transformer encoder with lightweight MLP decoder, strong ADE20k and Cityscapes results.

segmentationvision-understandingnvidia

Intel ZoeDepth metric depth-estimation model. Combines relative-depth pretraining with metric fine-tuning for absolute distance in real units.

depthvision-understandingintel