Who ranks #1 on the Vision leaderboard?
As of August 20, 2026, Claude Opus 5 by Anthropic ranks #1 on MMMU at 89.9%. API pricing is $5.00/M input and $25.00/M output.
As of August 20, 2026, Claude Opus 5 is #1 for multimodal image understanding at 89.9%. Ranked by MMMU: college-level questions on diagrams, charts, and images. Best multimodal LLM for reading images, charts, and video. MMMU and CharXiv ranks, not image generators. API price on every row.
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As of August 20, 2026, Claude Opus 5 by Anthropic is #1 for multimodal image understanding at 89.9%. Ranked by MMMU: college-level questions on diagrams, charts, and images. This board also tracks MMMU, MMMU-Pro, CharXiv-R, MathVista. Next on the same board: Claude Fable 5 and Gemini 3.7 Flash. Related leaders: Gemini 3.5 Flash on MMMU-Pro at 83.6%; Claude Mythos Preview on CharXiv-R at 93.2%. This vision leaderboard ranks models by MMMU. Scores come from public evals. Prices are the live API rates in the table above.
Sources: MMMU (https://mmmu-benchmark.github.io/); OpenAI API pricing (https://developers.openai.com/api/docs/pricing); Anthropic Claude API pricing (https://platform.claude.com/docs/en/about-claude/pricing); Gemini API pricing (https://ai.google.dev/gemini-api/docs/pricing)
| Rank | Model | MMMU | Input /M | Output /M |
|---|---|---|---|---|
| 1 | Claude Opus 5 | 89.9% | $5.00 | $25.00 |
| 2 | Claude Fable 5 | 89.3% | $10.00 | $50.00 |
| 3 | Gemini 3.7 Flash | 89.0% | $0.75 | $3.75 |
| 4 | GPT-5.6 Sol | 88.8% | $5.00 | $30.00 |
| 5 | Gemini 3.6 Flash | 88.4% | $0.75 | $3.75 |
| 6 | Gemini 3.5 Flash | 88.3% | $1.50 | $9.00 |
| 7 | GPT-5.5 | 88.3% | $5.00 | $30.00 |
| 8 | Gemini 3.1 Pro | 88.2% | $2.50 | $15.00 |
Best AI for images is a generation query. This page ranks multimodal LLMs that understand images. If you want pictures out, that is a different tool. If you want a model to read a chart, a screenshot, or a PDF figure, sort MMMU and CharXiv.
MMMU is the college-level multimodal exam most labs quote. MMMU-Pro is harder. VideoMMMU is video. A model can be great at photos and weak at plots. CharXiv is the chart check.
Rank one eval at a time. All LLM benchmarks.
As of August 20, 2026, Claude Opus 5 by Anthropic ranks #1 on MMMU at 89.9%. API pricing is $5.00/M input and $25.00/M output.
As of August 20, 2026, Claude Opus 5 by Anthropic is #1 for multimodal image understanding at 89.9%. Ranked by MMMU: college-level questions on diagrams, charts, and images. This board also tracks MMMU, MMMU-Pro, CharXiv-R, MathVista. Next on the same board: Claude Fable 5 and Gemini 3.7 Flash. Related leaders: Gemini 3.5 Flash on MMMU-Pro at 83.6%; Claude Mythos Preview on CharXiv-R at 93.2%.
The current MMMU ranking as of August 20, 2026 is 1. Claude Opus 5 at 89.9%; 2. Claude Fable 5 at 89.3%; 3. Gemini 3.7 Flash at 89.0%.
Llama 3.2 11B Instruct is the cheapest scored model on this vision leaderboard at $0.05/M input and $0.05/M output ($0.10 blended). Claude Opus 5 still leads MMMU at 89.9%.
Not automatically. Claude Opus 5 leads MMMU, but a cheaper scored model can be the better production choice if the quality gap is small. Use the table to weigh MMMU against input/output price, context window, and related evals.
Scores and API prices on this page are refreshed from published evals and provider rates. The snapshot is labeled August 20, 2026. Treat it as a current index, not a one-off blog post.
MMMU and MMMU-Pro rank college-level multimodal reasoning. CharXiv ranks scientific charts. VideoMMMU ranks video understanding.
MMMU is Massive Multi-discipline Multimodal Understanding. It mixes diagrams, charts, and images across science and engineering.
No. Text-only models are omitted from these columns. Sort by MMMU to see the models that actually publish a vision eval.
Usually no. Best AI for images often means image generators (Midjourney, Flux, Imagen). A vision or multimodal LLM reads images, charts, and screenshots. This board is the reader, not the generator.
ChatGPT vision is OpenAI's image-in-chat feature, backed by a GPT multimodal model. Compare that SKU here on MMMU and related evals, then check Gemini and Claude vision rows on the same table.
They can exist in the index, but vision columns stay empty. Sort by MMMU to hide models that never published a multimodal score.