Multimodal Models

Models that read images, documents or video alongside text.

Available: 38of 69
Providers: 6
Price range: $0.00030 – $0.1021per default run

Quick picks

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38 models

29 models currently unavailable

Their pages stay online, but they can’t be run at the moment.

Multimodal models for vision, OCR, and document understanding

Multimodal models accept text plus images (sometimes plus audio or video) and produce text output. Reach for one when your input contains images and your output is structured information: extract an invoice, describe a chart, transcribe a handwritten note, answer questions about a UI screenshot.

Pricing, trade-offs and pitfalls

Pricing is per-token like regular LLMs, with one twist: every image is counted as a fixed number of tokens — typically 250-1,500 tokens depending on resolution and detail mode. A standard 1024×1024 image costs roughly the same as a 1,000-word text input. High-detail mode (preserving fine text and small UI elements) costs 2-4× more. Plan budgets accordingly — a workload that processes thousands of receipt scans per day adds up quickly at flagship rates; multiply the per-token price on the model page by your image token count.

The trade-off is OCR accuracy, reasoning quality, and cost. Flagships (GPT-5 Vision, Claude 4.6, Gemini 2.5) read complex layouts and reason over chart contents very reliably. Specialized OCR-first models (Qwen 2.5 VL, Pixtral, InternVL) sometimes outperform on pure text extraction at a fraction of the cost. For pure document-to-JSON pipelines, a specialized model with a strict JSON schema usually wins. For document-and-reasoning workloads ('extract this invoice AND tell me if the tax math is right'), flagships win.

Watch out for resolution limits: most models downscale very large images before processing, which can destroy fine text in screenshots and dense documents. Pre-process — slice tall documents into single-page images, upscale low-resolution scans before sending — to preserve readability. Also watch out for hallucinations on hard-to-read regions; multimodal models tend to confidently invent text where the source is illegible.

Top picks above cover the most accurate flagship, the cheapest workhorse, the highest-resolution supporter, and the fastest streaming option.

Typical tasks

Invoice and receipt extraction
Document OCR and structuring
UI screenshot analysis
Chart and graph reading
Medical and scientific image annotation
Visual QA for accessibility

Frequently asked questions

Which multimodal model reads documents best?

GPT-5 Vision and Claude 4.6 lead on complex layouts, charts, and handwriting. For pure OCR on clean documents, Qwen 2.5 VL and Pixtral often match flagship quality at one-tenth the cost. Run a sample of your own documents on each before committing.

How is pricing calculated for images?

Every image is converted to a fixed token count — typically 250-1,500 tokens depending on resolution and detail mode. So a 1024×1024 image costs the same as ~1,000 tokens of text. Multi-page documents multiply linearly; check each model card for exact image-token rates.

What image formats are accepted?

JPEG, PNG, WebP, and GIF (first frame) are universal. Some models also accept PDF directly (one image per page). Max file size is typically 20 MB; max dimensions 8K on a side. Pre-resize larger files to avoid silent downscaling.

Can it read handwriting?

Flagships handle clear handwriting reliably and most cursive scripts in major languages. Quality drops on heavily stylized writing, faded ink, and complex shorthand. For high-volume handwriting workloads, fine-tune on a sample of your domain or pair with a specialized OCR pre-processor.

Can it understand charts and graphs?

Yes — chart reading is one of the strongest use-cases. Flagships extract data points from bar charts, line graphs, and pie charts; they reason about axis scales and identify trends. For dense scientific figures, ask for structured output (CSV or JSON) rather than narrative description.

How does it handle low-quality scans?

Quality degrades sharply below 150 DPI or above 30% noise. Pre-process with deskewing, contrast normalization, and lightweight denoising. For very poor scans, run an upscaler first (from /models/image) to restore readability before sending.

Can I send multiple images in one request?

Yes — most flagships accept 10-20 images in a single prompt. Useful for comparing screenshots, processing multi-page documents in one pass, or analyzing sequences of frames. Total token count (including all image tokens) must fit inside the context window.

Is video frame-by-frame supported?

Some multimodal models (Gemini 2.5 Pro, Qwen 2.5 VL) accept video directly and sample frames internally. For others, extract frames yourself (every 1-2 seconds is typical) and send them as a multi-image prompt. For real-time video analysis, look at dedicated video-understanding models.

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