Multimodal AI Models Compared 2026: Features, Pricing, and Performance

Proprietary Models

GPT-4o (OpenAI)

GPT-4o is the most widely deployed multimodal model. It processes text, images, and audio natively in a single architecture, and supports real-time voice interaction through the Realtime API. The "omni" architecture means all modalities share the same model weights rather than using separate encoders bolted together.

Context window: 128K tokens. Image handling: accepts multiple images per request, processes at variable resolution with automatic tiling for high-resolution images. Audio: native voice input and output with the Realtime API, Whisper-based transcription for file uploads. Video: frame-based processing (extract frames as images), no native video stream input.

Pricing: $2.50 per million input tokens, $10 per million output tokens. Image tokens are charged at the same rate as text tokens, with a typical high-resolution image consuming 1,000 to 2,500 tokens depending on size and detail level.

Strengths: consistent quality across a wide range of visual tasks, strong OCR, reliable chart interpretation, broad tool calling support, largest ecosystem of integrations. Weaknesses: occasionally misses fine visual details compared to Claude, image token costs add up quickly for high-resolution processing, the Realtime API for voice is priced separately and significantly more expensive.

Claude Opus 4 and Sonnet 4 (Anthropic)

Claude models accept images alongside text and have developed a reputation for careful, thorough visual analysis. Claude tends to describe what it sees with more precision than competing models, often catching details that GPT-4o and Gemini miss on first inspection.

Context window: 200K tokens for both Opus and Sonnet. Image handling: accepts multiple images, supports base64 and URL-based image input, processes at the image's native resolution up to a maximum. Audio: not natively supported; use Whisper or another transcription service to convert audio to text before sending to Claude. Video: not natively supported; extract frames as images.

Pricing: Claude Sonnet at $3/$15 per million input/output tokens. Claude Opus at $15/$75 per million tokens. Claude Haiku at $0.25/$1.25 for lightweight tasks.

Strengths: strongest document understanding of any model, excellent at multi-page PDF analysis, thorough visual descriptions with fewer hallucinated details, 200K context window supports processing many images in one request, computer use capability for UI automation. Weaknesses: no native audio or video support (must transcribe first), Opus pricing is high for visual workloads with many image tokens, slightly slower inference than GPT-4o on visual tasks.

Gemini 2.0 (Google)

Gemini models offer the largest context windows in the industry and native support for the broadest range of modalities, including text, images, audio, and video.

Context window: up to 2 million tokens (Gemini 2.0 Pro). Image handling: accepts images with efficient tokenization that keeps image token costs lower than competing models. Audio: native audio processing, can analyze audio files directly without transcription. Video: native video understanding, can process video files and reason about temporal sequences, not just individual frames.

Pricing: Gemini 2.0 Flash at $0.10/$0.40 per million tokens (very competitive for high-volume workloads). Gemini 2.0 Pro at higher pricing tiers with better quality. Free tier available with rate limits through Google AI Studio.

Strengths: largest context window (2M tokens) enables processing entire books or hour-long videos in a single call, native video understanding without frame extraction, cheapest per-token pricing with Flash, strong multilingual support including for visual content. Weaknesses: visual analysis quality is slightly below Claude for document understanding, grounding and factual accuracy on visual content has more variance than GPT-4o, the API has historically had more reliability issues than OpenAI or Anthropic.

Open-Source Models

Qwen2.5-VL (Alibaba)

Qwen2.5-VL is the current leader among open-source vision language models. Available in 7B, 32B, and 72B parameter sizes, it provides options for every hardware budget from consumer GPUs to multi-GPU servers.

Context window: 32K tokens (extendable with rope scaling). Image handling: dynamic resolution processing at native aspect ratios, supports 4K input images, efficient patch tokenization. Audio: not natively supported. Video: can process video as frame sequences.

Hardware requirements: 7B runs on a single RTX 4090 (24GB VRAM). 32B requires an A100 80GB or equivalent. 72B requires 2x A100 80GB or 4x A6000 48GB. Self-hosted inference cost on cloud GPUs ranges from $1.50 to $5 per hour depending on the variant and provider. Services like Vast.ai offer competitive GPU rental rates.

Strengths: 72B variant matches GPT-4V quality on most benchmarks, 32B provides excellent quality-to-hardware ratio, strong performance on Asian language documents and multilingual OCR, fully open weights with commercial license. Weaknesses: smaller context window than proprietary models, no native audio support, requires GPU infrastructure knowledge to deploy, community and tooling ecosystem is smaller than for proprietary APIs.

GLM-4.5V (Zhipu AI)

GLM-4.5V distinguishes itself with a 128K context window, the largest among open-source VLMs. This makes it practical for processing long documents, multi-page PDFs, and batches of images that would exceed other open-source models' context limits.

Context window: 128K tokens. Image handling: supports high-resolution images with adaptive tiling. Hardware requirements: similar to Qwen2.5-VL at comparable parameter counts.

Strengths: 128K context window enables multi-page document processing in a single call, strong performance on information-dense inputs like financial reports, competitive benchmark scores. Weaknesses: smaller community than Qwen, fewer deployment guides and examples available, less tested in Western-language production deployments.

InternVL 2.5 (Shanghai AI Lab)

InternVL 2.5 offers competitive benchmark performance with architecture choices optimized for inference efficiency. Available in multiple sizes from 2B to 78B parameters.

Strengths: efficient architecture keeps inference costs manageable, strong bilingual (Chinese/English) performance, good benchmark scores across vision tasks. Weaknesses: less community adoption than Qwen, documentation primarily in Chinese.

Molmo (Allen AI)

Molmo's distinguishing feature is its "pointing" capability. When asked about visual elements, Molmo returns pixel coordinates rather than just text descriptions. Ask "where is the login button?" and Molmo returns the (x, y) position of that button in the image.

Strengths: spatial grounding with pixel-level pointing, ideal for UI automation and agent-based screen interaction, strong visual question answering. Weaknesses: smaller model sizes than Qwen or InternVL, pointing capability is not useful for many common VLM tasks like document processing.

Multimodal Embedding Models

Separate from the generation models above, multimodal embedding models create the vectors used for multimodal search and multimodal RAG.

Cohere Embed 4: Interleaved text and image input, 128K context window, Matryoshka dimensions (256/512/1024/1536), processes raw PDF pages without parsing. The most capable multimodal embedding model available.

Voyage Multimodal 3: Strong on screenshot and slide retrieval, single-vector output per input, handles complex tables and figures well. Good option if you are already using Voyage for text embeddings.

Gemini Embedding 2: Google's multimodal embedding model, integrates with Vertex AI infrastructure. Good for organizations in the Google Cloud ecosystem.

Jina CLIP v2: Open-source option for local multimodal embeddings. Lower quality than proprietary options but runs without API costs.

Choosing the Right Model for Your Use Case

Document understanding and extraction: Claude Sonnet offers the best accuracy for document processing. For budget-sensitive document workloads, Gemini Flash provides acceptable quality at a fraction of the cost. Self-hosted Qwen2.5-VL-72B is the best option when documents cannot leave your infrastructure.

General-purpose visual Q&A: GPT-4o is the safest default. Consistent quality, broad tool calling support, and the largest developer ecosystem. Claude Sonnet is equally capable if you are already in the Anthropic ecosystem.

Video understanding: Gemini 2.0 Pro is the only model with native video processing. All other models require frame extraction, which loses temporal context.

Real-time voice interaction: GPT-4o via the Realtime API is the production-ready option for voice-enabled multimodal applications.

UI automation and screen agents: Molmo for spatial grounding with coordinates, Claude for computer use with desktop control, GPT-4o for general screen understanding.

High volume at lowest cost: Gemini Flash for API-based processing, self-hosted Qwen2.5-VL-32B for infrastructure-controlled workloads. Both offer strong quality at dramatically lower per-image costs than premium models.

Maximum context (many images in one call): Gemini 2.0 Pro with 2M tokens, followed by Claude with 200K tokens, then GPT-4o with 128K tokens.

What to Watch

The multimodal model landscape is evolving rapidly. Several trends are worth tracking for production planning.

Open-source models are closing the quality gap with proprietary models faster in vision than in any other capability. The Qwen2.5-VL-72B already matches 2024-era GPT-4V. By late 2026, open-source VLMs may match current proprietary models, making self-hosted multimodal inference viable for quality-sensitive applications.

Native video understanding is still nascent. Gemini is the only production model with genuine temporal video reasoning. As other providers add video support, expect rapid improvement in video-based applications.

Multimodal embedding models are evolving toward processing raw documents without any parsing pipeline. Cohere Embed 4 already does this for PDFs. As this capability matures, the traditional document parsing stack (OCR, layout analysis, table extraction) may become unnecessary for most retrieval applications.

Pricing continues to drop. Gemini Flash pricing is already 25x cheaper than GPT-4o for input tokens. Competition is driving costs down across all providers, making multimodal processing financially viable for use cases that were too expensive a year ago.