peterindia.netMultimodal Language Models
Language models that understand and sometimes produce text, images, audio and video together: proprietary multimodal APIs, open omni models, unified research models and the tools to evaluate them.
OpenAI's guide to models that take audio input and produce spoken or text output, including voice conversations.
Best for: Voice-first assistants through the OpenAI API.
Visit OpenAI audio and speech →Gemini API documentation for describing, summarising and transcribing audio with a multimodal model.
Best for: Audio analysis in the same prompt as text and images.
Visit Google Gemini audio understanding →Gemini API documentation for analysing video, answering questions about it and referring to moments in time.
Best for: Video question answering and summarisation.
Visit Google Gemini video understanding →Amazon's family of foundation models on AWS, including models that handle images and video as well as text.
Best for: Multimodal models inside AWS.
Visit Amazon Nova →Mistral AI's documentation for sending images to its multimodal models alongside text.
Best for: Image understanding with a European provider.
Visit Mistral vision →Alibaba's open omni-modal models that take text, images, audio and video and can respond in text or natural speech.
Best for: Open end-to-end voice and multimodal assistants.
Visit Qwen3-Omni →Qwen's earlier omni model that perceives text, images, audio and video and streams text and speech responses.
Best for: Understanding the design of real-time omni models.
Visit Qwen2.5-Omni →OpenBMB's compact multimodal models that run on edge devices and handle images, video and speech.
Best for: Multimodal models on phones and laptops.
Visit MiniCPM-o →Open omni-modal model from Baichuan that understands text, image, audio and video and can output text and speech.
Best for: Chinese and English omni-modal research.
Visit Baichuan-Omni-1.5 →Open-source interactive multimodal model project aiming at real-time video and audio conversation.
Best for: Real-time multimodal interaction research.
Visit VITA →Meituan's open omni-modal model for text, image, audio and video understanding with speech output.
Best for: Large open omni-modal model.
Visit LongCat-Flash-Omni →Microsoft's compact open model that processes text, images and speech in a single network.
Best for: Small multimodal model for applications.
Visit Phi-4-multimodal →Google's open Gemma model designed for on-device use, with support for image, audio and video input alongside text.
Best for: Efficient multimodal models for devices.
Visit Gemma 3n →Hugging Face's overview of Google's Gemma 3, open models with image understanding and long context.
Best for: Open multimodal models with broad tooling.
Visit Gemma 3 →Meta's research on early-fusion models that treat images and text as one token stream and can generate both.
Best for: Mixed text-and-image generation research.
Visit Chameleon →Research model that handles speech, text, images and music by converting each into discrete tokens.
Best for: Any-to-any modelling with one language model.
Visit AnyGPT →Research system that connects a language model to encoders and decoders for any-to-any multimodal input and output.
Best for: Studying end-to-end any-to-any pipelines.
Visit NExT-GPT →Research model that combines multimodal comprehension with image generation and editing.
Best for: Unified understanding and generation.
Visit SEED-X →Autoregressive multimodal model family for flexible photorealistic text-to-image generation and related vision tasks.
Best for: Image generation inside a language-model framework.
Visit Lumina-mGPT →Documentation for running models that accept and produce several modalities with the Transformers library.
Best for: Prototyping omni models in Python.
Visit Hugging Face Transformers: any-to-any →Open-source evaluation framework for large multimodal models across many benchmarks.
Best for: Repeatable multimodal benchmarking.
Visit lmms-eval →OpenCompass toolkit for evaluating vision-language and multimodal models on many benchmarks.
Best for: Comparing open and proprietary models.
Visit VLMEvalKit →Benchmark for evaluating multimodal models on video understanding across short and long videos.
Best for: Testing video comprehension.
Visit Video-MME →Benchmark and leaderboard for the multimodal abilities of vision-language models.
Best for: General multimodal capability checks.
Visit MMBench →Match your requirement to a starting point, then compare vendors.
| Voice conversation with an assistant | A proprietary audio-capable model, or an open omni model such as Qwen3-Omni |
|---|---|
| Analyse video | Gemini video understanding or an open model with video input |
| Keep data on your own hardware | Open models such as Qwen3-Omni, MiniCPM-o or Gemma 3n |
| Run on a phone or laptop | Compact models such as MiniCPM-o, Phi-4-multimodal or Gemma 3n |
| Generate images as well as understand them | Unified research models such as Chameleon or SEED-X |
| Choose between models | lmms-eval, VLMEvalKit and the benchmarks, then test on your own data |
Neighbouring directories and hubs.
Quick answers.
A multimodal language model extends a language model to handle more than text, such as images, audio and video, so one system can read, see, listen and respond. Some can also generate images or speech.
A VLM handles images (and sometimes video) plus text. Multimodal is the broader term and also covers audio and speech. Every VLM is multimodal, but not every multimodal model is only a VLM.
It describes models that accept several input types and can produce several output types, for example listening to speech and answering in speech, from one model.
They can misread small visual details, mishear audio or invent content. Verify important results and test on your own data.
APIs are quick to start and managed for you. Open models give more control over data and cost but you host and tune them. Evaluate on your own tasks.
Descriptions are summaries of each vendor's own positioning, checked against its website on 4 October 2026. Product names and offerings change often, so confirm details on the vendor site before deciding.