LLM·Dex
Newsletter
Use caseTop 5 picks

Best LLM for Vision in 2026

Image understanding, chart analysis, OCR, screenshot reasoning, photo description.

Updated

How we ranked

  • MMMU and MathVista scores
  • Chart and diagram comprehension
  • OCR quality on dense or low-resolution text
  • Multi-image reasoning
  • Fine-grained spatial understanding

Read the full methodology for our sourcing and ranking standards.

Vision capability is now table-stakes in every flagship release. The interesting question is which model best handles dense-text screenshots, schematics, and charts, the things developers actually paste into their assistants.

GPT-5.5 has the most consistent screenshot-debugging UX. Claude Opus is its closest peer and slightly stronger on hand-drawn diagrams. Gemini-3 Pro leads on document-AI tasks. Qwen-2-VL is the best open-weight option and surprisingly close on many benchmarks.

For OCR, none of the chat models will beat a dedicated OCR pipeline. They're for understanding, not extraction.

The ranking

  1. #1OpenAI

    GPT-5.5

    OpenAI's mid-cycle GPT-5 refresh, improved reasoning, tool use, and multimodal grounding over the 2025 launch.

    Full specsTry GPT-5.5
    Context
    400K tokens
    Output · 1M
    Pricing not published
    Modalities
    text, vision, audio

    Why it ranks here. Industry-leading tool-use and function-calling reliability. Strong end-to-end agent performance across SWE-bench and GAIA. Tracked weakness: Pricing premium vs. open-weight alternatives.

  2. #2Anthropic

    Claude Opus 4.7

    Anthropic's mid-2026 flagship, ahead on SWE-bench, agent reliability, and writing quality.

    Full specsTry Claude Opus 4.7
    Context
    500K tokens
    Output · 1M
    Pricing not published
    Modalities
    text, vision

    Why it ranks here. Strongest published SWE-bench Verified scores in agent settings. Best-in-class writing quality and voice control. Tracked weakness: Premium pricing relative to GPT-5 line.

  3. #3Google

    Gemini 3 Pro

    Google's late-2025 flagship, set new benchmarks on long-context, vision, and reasoning at competitive pricing.

    Full specsTry Gemini 3 Pro
    Context
    1.0M tokens
    Output · 1M
    Pricing not published
    Modalities
    text, vision, audio, video

    Why it ranks here. Massive 1M-token context window. State-of-the-art vision and document understanding. Tracked weakness: Tool-use ergonomics still lag OpenAI / Anthropic in some setups.

  4. #4AlibabaOpen weights

    Qwen2-VL-72B

    Top open-weight vision-language model, strong on document understanding and chart analysis.

    Full specs
    Context
    128K tokens
    Output · 1M
    Pricing not published
    Modalities
    text, vision

    Why it ranks here. Top open vision-language model. Apache-2.0. Tracked weakness: Heavy at 72B.

  5. #5Anthropic

    Claude Sonnet 4.6

    Anthropic's mid-tier 4.6 release, the workhorse model behind most production Anthropic deployments.

    Full specsTry Claude Sonnet 4.6
    Context
    200K tokens
    Output · 1M
    Pricing not published
    Modalities
    text, vision

    Why it ranks here. Excellent quality-cost ratio. Strong for code review and writing. Tracked weakness: Tier below Opus on hardest agent tasks.

How to choose

Don't pick on the headline ranking alone. Run your top two picks on a representative sample of your own workload and compare. The numbers in this list are sound, but task-specific quality varies in ways no benchmark fully captures. The criteria above are the right axes to evaluate on, but the weighting depends on your stack.

  • Cost-sensitive workloads, start with the cheapest of the top three; escalate only if quality is the bottleneck.
  • Privacy-sensitive workloads, filter to open-weight picks above. They're labeled with a green badge.
  • Latency-sensitive workloads, see the Fastest LLMs list, which can override task-specific picks.

Frequently asked

  • What is the best model for vision?
    Our #1 pick is GPT-5.5 from OpenAI. OpenAI's mid-cycle GPT-5 refresh, improved reasoning, tool use, and multimodal grounding over the 2025 launch.
  • How are these rankings determined?
    We rank by the criteria listed at the top of this page: MMMU and MathVista scores; Chart and diagram comprehension; OCR quality on dense or low-resolution text. Where two models are close, we prefer the one with stronger production deployment evidence at the time of writing. Read the full methodology for our standards.
  • GPT-5.5 or Claude Opus 4.7?
    Both are top-tier picks. GPT-5.5 edges ahead on the criteria most relevant to this task. Claude Opus 4.7 is the strongest alternative, see the head-to-head comparison page for full deltas.
  • Are open-source models on this list?
    Yes where they're competitive. Each entry below shows whether the model ships open weights and under what license.
  • How often is this list updated?
    Weekly. New launches that affect the ranking get reflected within seven days. The "last updated" stamp at the top of the page reflects the most recent dataset commit.

Related guides

Friday digest

The week's AI launches, in your inbox.

One short email every Friday, new models, leaks, and quietly-shipped APIs you missed.