Document and image AI
Compare multimodal models for document extraction, screenshots, charts, photographs and visual question answering. Image count and resolution create memory and latency costs that a text-only sizing figure cannot capture.
Selection approach
Use OCR-specialist models for high-volume extraction and multimodal language models when interpretation and reasoning matter. Test the exact scans, layouts, handwriting and languages found in production.
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12 current models
Showing 12 models
Moonshot AI
A 2.8-trillion-parameter, 104-billion-active multimodal mixture-of-experts model for long-context reasoning, coding, visual understanding and agentic work.
Qwen
A 36-billion-parameter multimodal mixture-of-experts model with about 3 billion active parameters, a 262,144-token default context and a strong emphasis on agentic coding.
Mistral AI
A 119B-total, 6B-active hybrid model that combines instruction following, reasoning, coding-agent and multimodal capabilities.
Meta
Meta's 109B-total, 17B-active multimodal mixture-of-experts checkpoint with text and image input and a very long documented context.
Google's 27B instruction-tuned multimodal model for text and image input, with a 128K context and broad language coverage.
DeepSeek
A 3B-class image-to-text model for document optical character recognition and layout-aware text extraction.
Meta
A 12B multimodal safeguard model for classifying text and image prompts and responses against Meta's hazard taxonomy.
Qwen
A compact 4-billion-parameter multimodal model with native vision, reasoning, coding and broad multilingual support.
Qwen
A 27-billion-parameter dense multimodal model for reasoning, coding, agents and visual understanding across 201 languages and dialects.
Google DeepMind
Google DeepMind's 12-billion-parameter instruction-tuned Gemma 4 model, supporting text, images, video and audio input with text output.
Google DeepMind
Google DeepMind's 30.7-billion-parameter instruction-tuned multimodal model for text and image understanding with a 256K context window.
StepFun
A 198-billion-parameter sparse vision-language model with about 11 billion active parameters, designed for reasoning, coding, agents and visual intelligence.
No model matches that search and workload combination.
Model-to-hardware fit
Use the precise model version, numerical format and complete software pipeline intended for production.
Check whether it can load on one GPU or requires supported multi-GPU loading.
Allow for context, cache, batch, media encoders and concurrent users.
Measure quality, latency and stability on representative work.
Model weights are only one part of the system. Data access, runtime software, storage, monitoring and administrator handover also affect a reliable deployment.
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