15 Underrated Multimodal AI Tools & Models

It’s another caffeine-heavy night here in Delhi. If you’re reading this, chances are you’re also tired of seeing the same ChatGPT, Gemini, Claude, and Grok lists everywhere.

Honestly? We’ve already written 6–7 posts on those at EverydayAITool.com. They’re great, sure — but the real action in 2026 is happening quietly, outside the mainstream hype cycle.

📑 Quick Navigation

🎥 Video Understanding Tools

• Qwen3-VL
• MiniCPM-V 4.5

💻 Screenshot-to-Code Tools

• GLM-4.6V
• InternVL 3.0

📄 Document & OCR Tools

• Pixtral Large
• DeepSeek OCR

📱 On-Device & Privacy Tools

• Phi-4-Multimodal
• Gemma 3

💰 Budget-Friendly Options

• DeepSeek-V3.2
• Qwen2.5-VL-32B

🤖 Agentic & Automation Tools

• Kimi K2.5
• CrewAI/LangGraph

🎬 Video Generation Tools

• Runway Gen-3
• Kling AI

🎙️ Voice & Audio Tools

• Moshi

---

❓ Jump to FAQ

This article is about underrated multimodal AI tools and models that are already powering real workflows today:

• Screenshot → clean production code
• 30–40 minute video → structured insights
• Invoices → auto-extracted data + logic

Visual agents that actually click, navigate, and finish tasks

Most of these come from open research labs and Chinese teams, and many outperform “big-name” models in specific multimodal + agentic tasks — without insane costs.

Everything listed here is hands-on tested in real projects. No fluffy summaries. No affiliate hype.

1. Qwen3-VL (Alibaba Qwen Team)

If video understanding is your bottleneck, Qwen3-VL is the strongest open model I’ve tested so far.

It comfortably handles 30+ minute videos with frame-level reasoning, spatial awareness, and object tracking. I used it to break down long demo videos into summaries and extract embed-ready insights for blogs.

What surprised me most was its visual agent capability — it can reason over phone and desktop screens in a way that actually feels usable, not experimental.

Best for:

Long video analysis, visual agents, multilingual workflows

Why it matters:

Open weights, massive context (1M+), and no hype tax

2. GLM-4.6V (Zhipu AI / Z.ai)

GLM-4.6V shines where many multimodal models still struggle: GUIs and real interfaces.

I tested it by feeding a single dashboard screenshot. It generated clean, readable HTML, CSS, and JS with logical structure. One Tailwind-heavy layout needed a small manual tweak, but overall? Shockingly usable.

The agentic flow is smooth — tool calls, function execution, and multi-step planning work without hacks.

Best for:

Screenshot → code, UI replication, autonomous agents

Context window:

~128K–200K (depending on setup)

3. InternVL 3.0 (OpenGVLab / Shanghai AI Lab)

This is the model I trust when documents are messy.

Research papers, invoices, scanned PDFs with charts — InternVL 3.0 extracts structured data extremely well. I’ve used it to convert graphs into plotting code and tables into machine-readable formats.

It also handles long documents surprisingly well for a research-focused model.

Best for:

OCR, charts, invoices, academic and data-heavy documents

Strength:

Accuracy over flashiness

4. Pixtral Large (Mistral AI)

Pixtral Large is underrated because it doesn’t shout — it just works.

It handles high-resolution images without aggressive downscaling, which makes it excellent for enterprise documents and RAG pipelines. Layout understanding is solid, hallucinations are low, and output formatting is clean.

Best for:

Document OCR, catalogs, layout-aware RAG

Vibe:

Quietly reliable

5. Phi-4-Multimodal (Microsoft)

This one is about privacy and latency.

Phi-4-Multimodal runs well on local machines and supports text, images, and audio. I tested it for offline video summaries and speech-to-text tasks — zero cloud dependency.

It’s not the biggest model here, but for on-device agents, it’s incredibly practical.

Best for:

Local workflows, privacy-first setups, low latency

6. MiniCPM-V 4.5 (Tsinghua/OpenBMB)

MiniCPM-V 4.5 surprised me with its real-time performance on mobile hardware.

It’s ideal for quick video analysis, captioning, or robotic/edge use cases. Token compression keeps things efficient, which matters a lot on constrained devices.

Best for:

Edge AI, mobile apps, robotics, live video input

7. DeepSeek-V3.2

DeepSeek continues to be the best value-for-money model family.

V3.2 handles vision, reasoning, and code generation with impressive efficiency. Screenshot-to-code tasks were fast and accurate, especially considering the low inference cost.

Best for:

Budget workflows, scalable deployments

Why people sleep on it:

No flashy marketing

8. Gemma 3 (Google DeepMind)

Gemma 3 is lightweight, multilingual, and fast.

It’s not meant to replace frontier models, but for quick image captioning, classification, and multilingual tasks, it’s extremely practical — especially on-device.

Best for:

Fast visual tasks, multilingual apps

9. Kimi K2.5 (Moonshot AI)

Kimi K2.5 feels like a visual agent engineer’s model.

It handles documents, images, and code generation in a single flow and works well in agent swarms. I used it for document-heavy research tasks and production-ready code drafts.

Best for:

Agentic research, document → code workflows

10. Qwen2.5-VL-32B-Instruct

This is one of the most human-like visual reasoning models I’ve tested.

Tables, invoices, and screenshots are converted into clean, editable code with logical structure. It’s especially good when tasks involve both reasoning and tool orchestration.

Best for:

Visual reasoning + automation

Strength:

Consistency and structure

11. DeepSeek OCR Variants

If you process high volumes of documents, this is gold.

DeepSeek’s OCR models are fast, multilingual, and box-free. Token usage is efficient, which makes a massive difference at scale.

Best for:

Invoices, receipts, bulk document pipelines.

12. Runway Gen-3 (Latest)

Runway Gen-3 is no longer just “cool video gen.”

The agentic editing loop — text, image, clip input → iterative refinement — makes it useful for short promos and marketing videos, not just experiments.

Best for:

AI video generation + guided editing

13. Kling AI (Latest)

Kling stands out for physical realism.

Animating still images feels coherent rather than floaty. Motion obeys physics better than most competitors, especially in character and object movement.

Best for:

Image → video with realistic motion

14. Moshi (Kyutai)

Moshi is fascinating because it feels alive.

It’s an audio-first multimodal model with full-duplex voice, emotional tone, and image understanding. Conversations feel natural and low-latency — closer to human dialogue than most voice agents.

Best for:

Voice agents, emotional interaction, real-time UX.

15. CrewAI + LangGraph Stacks

These aren’t models — they’re force multipliers.

I’ve combined:

• Qwen3-VL for video understanding
• GLM-4.6V for GUI agents
• LangGraph for orchestration

Result? An autopilot blog workflow that watches videos, extracts insights, formats content, and drafts posts with minimal intervention.

Best for:

Multi-agent, multi-tool automation

The 2026 Reality Check

While the spotlight stays on mainstream tools, open and Chinese research labs are quietly leading in:

• Multimodal reasoning
• Agentic execution
• Cost-efficient scaling

If you stack smart — video model + visual agent + orchestration layer — you can build workflows that outperform single “do-everything” models.

FAQ

What are multimodal AI tools in 2026?

They process text, images, video, audio, and code together — often with agentic abilities like tool use, screen navigation, and multi-step execution.

Why choose underrated tools over ChatGPT or Gemini?

Lower cost, open weights, and better performance in niche tasks like video understanding or GUI automation.

Best multimodal AI for video understanding?

Qwen3-VL — long-context, spatial reasoning, and visual agents.

Best for code generation from screenshots?

• GLM-4.6V and InternVL 3.0.
• Which models run locally?
• Phi-4-Multimodal, MiniCPM-V 4.5, Gemma 3.

Are agentic workflows production-ready?

Yes — especially with GLM-4.6V, Qwen models, and orchestration tools like LangGraph.

“Just tested Qwen3-VL on a 40-minute video — the spatial tracking blew my mind. What underrated multimodal tool are you betting on in 2026?”

About the Author

Hi, I’m Nandani, the person behind EverydayAITool.com.

I spend late nights testing AI models in real workflows — not benchmarks — and share what actually works for creators, developers, and everyday users. If it saves time, money, or mental energy, I’m interested. Otherwise, I skip it.