Gemma vs Laguna by Poolside: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Gemma and Laguna by Poolside — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Gemma
Open-weight family of lightweight, decoder-only LLMs from Google DeepMind, available in pre-trained and instruction-tuned variants for text and multimodal tasks.
Key features
- Open Weights and Variants: Provides publicly released model checkpoints for pre-trained (base) and instruction-tuned (suffix "-it") variants, enabling research, fine-tuning, and local deployment.
- Multiple Model Sizes: Available in a range of sizes across Gemma generations (examples include 1B, 2B, 4B, 7B, 12B, 27B depending on generation) to balance performance and resource requirements.
- Decoder-Only Text and Multimodal Support: Gemma (text-to-text) and Gemma 3 (text+image) support generation, summarization, QA and reasoning; Gemma 3 adds multimodal image understanding capabilities.
- Large Context Windows: Later Gemma versions (Gemma 3) support very large context windows (reportedly up to 128K tokens) for long-document understanding and retrieval-augmented workflows.
- TPU-Optimized Training: Models were trained on modern TPU hardware (TPUv5e) with documentation about implementation and hardware used for reproducibility.
- Native Runtimes and Integrations: Official and community tooling includes a native C++ runtime (gemma.dll), Unity plugin (GemmaManager), and bindings to embed models in games and applications with prewarm and runtime controls.
- Safety Evaluation & Documentation: Published technical report, model cards, and responsible generation tooling with safety testing and benchmark results across multiple tasks and harms categories.
- Platform Availability: Model cards and weights published on Hugging Face and integrated into platforms like Vertex Model Garden for easy access and deployment.
- Open-weight model releases (base and instruction-tuned checkpoints available on model hubs such as Hugging Face)
- Multiple model sizes (examples: 2B, 7B, 9B, 12B, 27B) to trade off quality vs resource needs
- Gemma 3 multimodal support: image + text input to text output
- Very large context windows (Gemma 3 up to 128K tokens; other Gemma versions have large but smaller contexts)
- Multilingual capability covering 140+ languages
- Native runtimes and integration examples: gemma.cpp / gemma.dll for local inference, gemma-unity-plugin with C# bindings and GemmaManager
- Integration / hosting options: model hub hosting (Hugging Face), Vertex Model Garden, local inference via native binary or community runtimes (e.g., llama.cpp ports)
- Instruction-tuned variants for instruction-following and downstream tasks (question answering, summarization, reasoning)
- Developer tooling and documentation: model cards, technical report, repository readmes, and example code
- Supports long-context and prewarm APIs/operations in integration (e.g., Prewarm() in Unity plugin; maxLength parameter in native API)
Best for
- Local or on-premise natural language generation: running pre-trained or instruction-tuned Gemma models locally for summarization, content generation, and assistants where open weights are required.
- Multimodal applications: using Gemma 3 for image-to-text tasks such as captioning, VQA, and document image understanding in apps that combine vision and language.
- Game NPCs and interactive characters: embedding Gemma via the Unity plugin and native runtime to provide prewarmed, low-latency conversational agents or NPC dialogue.
- Long-document analysis: leveraging large context windows for summarizing, question-answering, and reasoning over lengthy documents, logs, or codebases.
- Research and safety evaluation: benchmarking model behavior, performing fine-grained safety testing, and experimenting with instruction tuning and mitigation strategies using published model cards and toolkits.
- Custom instruction tuning and fine-tuning: adapting open weights for domain-specific assistants or workflows by applying instruction-tuning pipelines to the provided base checkpoints.
- Deployment and inference at scale: integrating Gemma models into cloud or edge inference pipelines via Hugging Face, Vertex Model Garden, or self-hosted runtimes for production services.
- Instruction-following text generation: chatbots, assistants, and Q&A
- Text summarization and long-document understanding using large context windows
- Multimodal applications: image-to-text captioning and VQA using Gemma 3
- On-premise or edge inference in games and simulations (Unity integration for NPC dialogue, prewarming workflows)
- Research and fine-tuning experiments using open weights and model cards
- Embedding into custom services via local native runtimes or hosted model gardens for production inference
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.