Laguna by Poolside vs Llama 4: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Laguna by Poolside and Llama 4 — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Laguna by Poolside

Poolside

Free

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.

View Laguna by Poolside details

Llama 4

Key features

Mixture-of-Experts Architecture: Uses an MoE design (e.g., Scout with 16 experts, Maverick with 128 experts) to deliver high effective capacity while reducing inference compute compared to equivalently capable dense models.
Native Multimodality with Early Fusion: Accepts and jointly processes text and images using early fusion, enabling integrated image understanding, captioning, visual question answering, and multimodal reasoning.
Instruction-Tuned and Pretrained Variants: Provides instruction-tuned checkpoints for assistant-like chat and visual reasoning plus pretrained weights for custom natural language generation and fine-tuning.
High Effective Capacity: Although base parameter counts are ~17B, the expert routing design produces effective model capacities (reported comparators up to the 100s of billions) for stronger performance on understanding tasks.
Steerability and System Prompting: Improved steerability enables developers to shape outputs via system prompts to reduce refusals, control tone, and improve formatting for application-specific behavior.
End-to-End Distribution: Meta distributes model weights along with inference and training scripts, example code, and utilities to enable fine-tuning, deployment, and research experimentation.
Production Deployment Guidance: Documented hardware expectations and community tooling notes (e.g., multi-GPU requirements, Llama Stack and other ecosystem integrations) to run inference and fine-tuning at scale.
Native multimodality with early-fusion design for combined text and image inputs
Mixture-of-Experts (MoE) architecture (e.g., Scout 17B/16E, Maverick 17B/128E) for parameter-efficient performance
Auto-regressive language modeling with instruction-tuned variants for assistant/chat behavior
Optimized for vision tasks: image recognition, image reasoning, captioning, and visual Q&A
Supports multiple numeric precisions and variants (bf16, FP8 variants referenced)
Open-source distribution of model code, checkpoints, inference and fine-tuning scripts (subject to license and access approval)
Example PyTorch integrations and torchrun multi-GPU inference scripts provided in official repos
Available via model hubs (Hugging Face) and ecosystem integrations (Llama Stack, fine-tuning toolchains)
Scalable inference across multiple GPUs (examples require 4+ GPUs for full bf16; some stacks recommend 8x H100 for large deployments)
Steerability via system prompts and instruction-tuning to reduce refusals and control style/formatting

Best for

Multimodal Virtual Assistants: Build chat assistants that answer questions about images, generate captions, and provide context-aware responses by combining text and visual inputs.
Visual Question Answering and Image Reasoning: Deploy models to perform image understanding tasks such as scene interpretation, object-based QA, and context-aware image summarization.
Instruction-Following Conversational Agents: Use instruction-tuned variants for customer support bots, interactive tutors, or domain assistants that require conversational, formatted outputs.
Domain Adaptation and Fine-Tuning: Fine-tune pretrained weights on industry-specific text and image datasets for tasks like legal summarization, medical imaging captioning, or product catalog enrichment.
Multilingual Content Generation: Generate or translate content across multiple languages for marketing, documentation, or localized conversational interfaces.
Research and Model Analysis: Conduct research into MoE architectures, multimodal early-fusion strategies, and steerability techniques using provided training and inference code.
Assistant-like chatbots and conversational agents with multimodal (text+image) inputs
Visual reasoning and image question-answering
Image captioning and content understanding for multimedia applications
Natural language generation and instruction-following in multiple languages
Research and commercial fine-tuning for specialized domains
Embedding into inference stacks and services via Hugging Face, Llama Stack, or custom PyTorch deployments

View Llama 4 details