Laguna by Poolside vs UniVideo: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Laguna by Poolside and UniVideo — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Laguna by Poolside
Poolside
Poolside's family of open Mixture-of-Experts foundation models for agentic coding — XS.2 runs locally, M.1 reaches 72.5% on SWE-bench Verified.
Key features
- Two Model Sizes: Laguna XS.2 (33B total / 3B active) and Laguna M.1 (225B total / 23B active) target different latency and capability needs.
- Mixture-of-Experts Architecture: Routes each token through a subset of experts for efficiency at large scale.
- Local Deployment: XS.2 is small enough to run on a Mac with 36 GB of RAM via Ollama under an Apache 2.0 license.
- Strong SWE-bench Results: XS.2 hits 68.2% and M.1 reaches 72.5% on SWE-bench Verified.
- Bundled Coding Agent: Ships 'pool,' a lightweight terminal-based coding agent.
- Agent Client Protocol: Includes a dual ACP client-server used internally for agent RL training and evaluation.
Best for
- Local Agentic Coding: Running XS.2 on a laptop for private, offline code generation and editing.
- High-Capability Code Tasks: Using M.1 for harder, long-horizon software engineering work.
- Self-Hosted Deployments: Building on open weights to avoid third-party API dependencies.
- Research & Fine-Tuning: Adapting permissively licensed weights for custom coding workflows.
- Benchmarking: Evaluating agentic coding performance against SWE-bench Verified and Pro.
UniVideo
Kling Team (Kuaishou Technology)
Unified video model for understanding, high-fidelity generation, and precise free-form editing via a dual-stream architecture.
Key features
- Dual-Stream Architecture: Combines a Multimodal Large Language Model (MLLM) for understanding instructions with a Multimodal DiT (MMDiT) generator to decouple instruction parsing from video synthesis and preserve visual-temporal consistency.
- Unified Instruction Paradigm: Unifies diverse tasks (text/image-to-video generation, in-context generation, and editing) under a single multimodal instruction format so users can compose complex operations in one prompt.
- In-Context Video Generation: Supports generation conditioned on example frames or short video contexts to produce temporally coherent continuations or variant clips that follow provided examples.
- Free-Form Video Editing: Performs precise edits such as changing materials, green-screening characters, and localized modifications by interpreting free-form multimodal instructions, leveraging transfer from large-scale image editing data.
- Task Composition: Enables combining capabilities (e.g., editing + style transfer) within a single instruction, executing multiple editing and generation steps coherently without separate models.
- Visual-Prompt-Based Generation: Accepts visual prompts (images or video frames) alongside text to guide content, composition, and style of produced videos.
- Joint Multi-Task Training & Checkpoint Variants: Trained jointly across multiple video/image/text tasks and released with checkpoint variants and inference scripts to support different input modalities and research use cases.
- Dual-stream architecture: Multimodal Large Language Model (MLLM) for instruction understanding + Multimodal DiT (MMDiT) for video generation
- Unified capabilities: text-to-video, image-to-video, visual-prompt-based generation, in-context video generation and editing, free-form editing
- Task composition: combine editing, style transfer, and other operations via single multimodal instructions
- Cross-modal transfer: editing capability transferred from image editing datasets to video editing without explicit video-edit training for some tasks
- Model variants / checkpoints: two released variants (Variant 1: img/video/text -> MLLM -> last layer hidden -> MMDiT; Variant 2: img/video/text/queries -> MLLM -> text+queries hidden -> MMDiT)
- Open-source release: code, checkpoints, inference scripts on GitHub and model card on Hugging Face
- Inference utilities: provided demo/inference scripts for running tasks and demos
- Technical stack & tested environment: Python 3.11; PyTorch 2.4.1 with CUDA 12.1; diffusers 0.34.0; transformers 4.51.3; recommended conda environment (environment.yml provided)
Best for
- Text-to-Video Content Creation: Generate short, coherent video clips from textual descriptions for prototyping, creative content, or concept footage.
- In-Context Video Synthesis: Produce video continuations or alternate takes conditioned on example frames or short clips for storyboarding and iterative creative workflows.
- Free-Form Video Editing for VFX: Apply complex edits such as green-screening, material replacement, or object modification across frames while preserving temporal consistency for visual effects and post-production.
- Style Transfer and Composition: Combine style transfer with edits (e.g., recoloring plus material change) in a single instruction to create stylized variations of existing footage.
- Research and Benchmarking: Serve as a baseline and toolkit for academic and industrial research into unified multimodal video models, enabling reproducible experiments with provided code and checkpoints.
- Visual-Prompted Prototyping: Use image or frame prompts to guide generation for rapid prototyping of scene variations, product demos, or UX motion concepts.
- Text-to-video and image-to-video generation for creative content
- In-context video generation using example videos as prompts
- Free-form and region-based video editing (green-screening, material/texture changes)
- Style transfer and composition of multiple editing operations in a single instruction
- Research and development: baseline for multimodal video model research and further model fine-tuning
- Prototyping video-based multimodal applications with provided inference scripts and checkpoints