Beam

Beam

Reflection
Laguna S 2.1

Laguna S 2.1

Poolside
+
+

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About

Beam is Reflection’s first open-weight model, a sparse Mixture-of-Experts model with 501 billion total parameters and 23 billion active parameters, built for coding, reasoning, and agentic workloads. Its capabilities come from large-scale pretraining and reinforcement learning, including training on 23.8 trillion diverse, curated, high-quality tokens from the web, public sources, and proprietary licensed datasets. Beam was trained with a particular focus on coding and agentic performance and is designed to deliver competitive open-weight capabilities with efficient inference compute. It supports complex software engineering, terminal, STEM, web search, tool-use, and general knowledge tasks, with reinforcement learning designed to improve multi-step reasoning, tool use, and adaptation to environment feedback. Users can control the tradeoff between performance and token usage through a reasoning effort parameter.

About

Laguna S 2.1 is an open weight agentic coding model designed to pursue longer-horizon work and make effective use of reasoning. It uses a 118-billion-parameter Mixture-of-Experts architecture with 8 billion active parameters per token and supports a context window of up to one million tokens in both thinking and no-thinking modes. Its compact active size makes it suitable for complex work on local machines while remaining competitive with models many times larger on terminal, software-engineering, codebase-question-answering, and tool-use benchmarks. Laguna S 2.1 is built to keep working through difficult tasks with greater persistence, verification, and willingness to backtrack instead of declaring success too early. In demonstrated runs, it built and validated a browser rendering engine from an empty folder, optimized an agent harness for faster execution and substantially lower memory allocation, and completed extended mathematical research using the tools in its environment.

Platforms Supported

Windows Supported
Mac Supported
Linux Supported
Cloud Supported
On-Premises Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Platforms Supported

Windows Not Supported
Mac Not Supported
Linux Not Supported
Cloud Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Audience

Developers, AI researchers, and engineering teams requiring a tool to run coding, reasoning, tool-use, and agentic workloads with an efficient open-weight language model

Audience

Developers and engineering teams seeking an efficient reasoning model for long-horizon coding, software engineering, and agentic tasks

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

API

Offers API Not Supported

API

Offers API Not Supported

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version Not Supported
Free Trial Not Supported

Pricing

No information available.
Free Version Not Supported
Free Trial Not Supported

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

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Reviews/Ratings

Overall 5.0 / 5
ease 5.0 / 5
features 4.0 / 5
design 5.0 / 5

Pros & Cons from Real Users

Pros

  • Laguna S 2.1 looks awesome from a developer’s point of view because it is built specifically for agentic coding, not just general chatbot tasks. I like that it is open-weight, relatively compact for its capability, and designed for the kind of workflows where an AI needs to inspect a repo, reason through changes, edit code, and keep moving across multiple steps. The 1M-token context window is a huge plus. For real engineering work, context is everything: source files, docs, logs, tests, tickets, configs, and previous attempts all matter. Having a coding model that can handle that much context makes it much more useful for serious repo-level work.

Cons

  • It is still new, so I would want to test it heavily before trusting it with production code. Coding benchmarks are useful, but the real test is messy repos, weird dependencies, flaky tests, security-sensitive changes, and long-running agent loops.

Training

Documentation Supported
Webinars Not Supported
Live Online Supported
In Person Not Supported

Training

Documentation Supported
Webinars Supported
Live Online Not Supported
In Person Not Supported

Company Information

Reflection
United States
reflection.ai/blog/introducing-beam

Company Information

Poolside
Founded: 2023
United States
poolside.ai/blog/introducing-laguna-s-2-1

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LongCat-2.0

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Categories

AI Models Supported

Categories

AI Coding Models Supported
AI Models Supported

Integrations

Agent Client Protocol (ACP) Not Supported
Claude Code Not Supported
Cline Not Supported
Hermes Agent Not Supported
Hugging Face Not Supported
IntelliJ IDEA Not Supported
Kilo Code Not Supported
Nous Portal Not Supported
Ollama Not Supported
OpenAI Codex Not Supported
OpenClaw Not Supported
OpenCode Not Supported
OpenRouter Not Supported
Poolside Not Supported
Roo Code Not Supported
Visual Studio Not Supported
Visual Studio Code Not Supported
Zed Not Supported

Integrations

Agent Client Protocol (ACP) Supported
Claude Code Supported
Cline Supported
Hermes Agent Supported
Hugging Face Supported
IntelliJ IDEA Supported
Kilo Code Supported
Nous Portal Supported
Ollama Supported
OpenAI Codex Supported
OpenClaw Supported
OpenCode Supported
OpenRouter Supported
Poolside Supported
Roo Code Supported
Visual Studio Supported
Visual Studio Code Supported
Zed Supported
Claim Beam and update features and information
Claim Beam and update features and information
Claim Laguna S 2.1 and update features and information
Claim Laguna S 2.1 and update features and information