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Beam: an open model built for AI agents and coding work
Beam is an open-weight model from Reflection built for AI agents, coding, and token-efficient reasoning. It's not a chat app — it's an engine you plug into your own tools and agents, but only on a serious server.
It's a large open-weight AI model that developers download and wire into their own agents for coding and tool use.
When it helps
Useful if you're building an AI agent or want an open alternative to Claude and GPT without a revenue cap; not for a laptop or casual chat — you need a multi-GPU server.
Pros
- Apache 2.0 license — no revenue cap, retraining and distillation allowed
- Reflection's own tests show top-tier results fixing real code bugs
- Reads up to a million tokens of text in one go
- Lighter FP8 and NVFP4 versions exist for cheaper deployment
Cons
- Needs a multi-GPU server — won't run on a laptop
- Benchmark scores are Reflection's own tests, not independently verified
- Real-world agentic coding performance hasn't been checked by outsiders
How to set it up — step by step
- 1Check your hardware: you need a multi-GPU AI server, not a home laptop.
- 2Install Ollama and run the command ollama run beam to launch the model.
- 3Or download the FP8 or NVFP4 version from https://huggingface.co/reflection/Beam-1.0-501B-A23B for a lighter setup.
- 4Open your AI agent (Claude Code, Codex) inside your project folder.
- 5Send the agent the text from the block below.
- 6Check the agent's answer and ask how to wire Beam into your tools via MCP, a standard way to connect AI to software.
Text for your agent
Copy this and send it to your agent — Claude Code, Codex, any of them:
Install the Beam model by Reflection through Ollama (ollama run beam) or download the FP8/NVFP4 build from https://huggingface.co/reflection/Beam-1.0-501B-A23B — keep in mind this is a server-grade model and a regular laptop will not work. If that hardware is not available, explain what this model can do and how it differs from Claude and GPT.
This is a server-grade model — it will not run at home, but it sets a benchmark for open models of this size. Lighter FP8 and NVFP4 builds exist, but they too need a server with several GPUs, not a home computer.
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