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AI Multimodal Model Comparison — which AI models support vision, audio & video

Tick the modalities your app needs — image, audio, video or PDF input, image or audio output — and instantly see which frontier models (GPT, Claude, Gemini, Llama, Qwen) actually support them, with each model's per-request media limits and a link to the provider doc. No signup, no API keys, sources cited.

By Induwara AshinsanaUpdated Jul 3, 2026
Multimodal capability matrix
Required modalities (matches support ALL you pick)
Providers (empty = all)

14 of 14 models match your filters

ModelProviderImageAudioVideoPDFImage outAudio outContextDetails
GPT-4oOpenAI128,000
GPT-4.1OpenAI1,047,576
o3 (reasoning)OpenAI200,000
GPT-5OpenAI400,000
GPT-4o RealtimeOpenAI128,000
Claude Opus 4.5Anthropic200,000
Claude Haiku 4.5Anthropic200,000
Gemini 2.5 ProGoogle1,048,576
Gemini 2.5 FlashGoogle1,048,576
Gemini 2.0 FlashGoogle1,048,576
Llama 4 MaverickMeta1,000,000
Llama 3.3 70BMeta128,000
Qwen2.5-VLAlibaba128,000
Qwen2.5-OmniAlibaba32,768

“Support” means the documented API of that model accepts or produces the modality. Unknown or unpublished limits show “—” and are never guessed. Each model links its provider doc — expand a row to check the source. Compiled 2026-07-03.

How it works

This is a lookup-and-filter tool, not a calculator. Each of the 14model rows stores a set of boolean flags — does the documented API accept text, image, audio, video or PDF input, and does it produce image or audio output — copied verbatim from that provider's own API guide and cited per row. There is no arithmetic; the “methodology” is careful data compilation plus transparent filter logic.

  1. Modality flags.“Support” means the model's documented chat or generate API accepts or produces that modality — not a research demo and not a separate product. A provider's standalone text-to-speech endpoint, for instance, does not make its chat model “audio-out.” Unknown or unpublished support defaults to false, so a required-modality filter never over-promises.
  2. Hard limits. Where a doc publishes a numeric cap — images per request, image size, audio minutes, video minutes, context tokens — it is copied exactly with its unit. Where the provider publishes no fixed cap, the value is recorded as unknown and rendered , never a guessed number.
  3. Filter logic. A model survives only if it supports every modality you tick (logical AND): visible = models.filter(m => required.every(k => m[k] === true)). Provider and name filters narrow the set further. Everything runs client-side and instantly — no network calls, no keys.
  4. Cross-check. Every filter is evaluated by two independent implementations (a filter pass and a reduce pass) that must return the same model set, and all rows are validated to cite a source and carry no undefined limits. When both agree the card shows a “Cross-checked” badge — the same dual-method credibility idea used in our tax calculator.

The v1 list covers the major public-API models plus flagship open-weight models (Llama 4, Qwen2.5). Niche and long-tail models are deliberately omitted rather than implying exhaustive coverage. For token pricing, benchmark quality or free-tier quotas, see the related tools below — capability, cost and quality are three separate questions.

Worked examples

Case 1 — I need video understanding

Video input = on
  1. Keep only models whose videoIn flag is true.
  2. Gemini 2.5 Pro, Gemini 2.5 Flash and Gemini 2.0 Flash qualify (native video, per Google's video guide).
  3. Qwen2.5-VL and Qwen2.5-Omni also qualify (open-weight video input).
  4. GPT-4o and every Claude row are hidden — no documented native video input.
  5. Result: 5 models match.

Case 2 — Voice assistant: audio in AND audio out

Audio input = on · Audio output = on
  1. Require BOTH audioIn = true AND audioOut = true (logical AND).
  2. GPT-4o Realtime qualifies — speech in, speech out over a realtime session.
  3. Qwen2.5-Omni qualifies — omni input plus native spoken output.
  4. Gemini is excluded: its audio output is Live-API-only, so audioOut is marked false under the conservative rule (unknown ≠ supported).
  5. Result: 2 models match.

Case 3 — Speaks AND draws (edge case → empty)

Image output = on · Audio output = on
  1. Require a single model that both generates images and speaks.
  2. Image output lives on Gemini 2.0 Flash; audio output lives on GPT-4o Realtime and Qwen2.5-Omni.
  3. No documented API model does both at once, so the intersection is empty.
  4. Result: 0 models — the tool shows its empty state suggesting you chain two models.

Frequently asked questions

Sources & references

Related tools

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Comments & feedback

Spotted a bug or want an improvement? Tell us — our team reviews every comment, and good ideas get built. Comments are public and anonymous.

Spotted a model whose modality support has changed, or want one added?

Email me at [email protected] — I re-verify against the provider docs and update within a day.