Beam

Beam

Reflection
+
+

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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

DeepSeek-V4-Pro is a large-scale Mixture-of-Experts (MoE) language model designed for advanced reasoning, coding, and long-context understanding. It features 1.6 trillion total parameters with 49 billion activated parameters, enabling high performance while maintaining efficiency. The model supports an exceptionally large context window of up to one million tokens, allowing it to process extensive documents and workflows. It uses a hybrid attention architecture to optimize long-context performance and reduce computational cost. DeepSeek-V4-Pro is trained on over 32 trillion tokens, improving its knowledge and reasoning capabilities. It also includes advanced optimization techniques for stability and faster convergence during training. The model supports multiple reasoning modes, allowing users to balance speed and accuracy based on their needs. Overall, it provides a powerful open-source solution for complex AI tasks and large-scale applications.

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 Supported
Mac Supported
Linux Supported
Cloud Supported
On-Premises 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

AI researchers, developers, and enterprises seeking a powerful open-source language model for large-scale reasoning, coding, and long-context AI applications

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 Not Supported

API

Offers API Not Supported

API

Offers API Supported

Screenshots and Videos

Screenshots and Videos

Pricing

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

Pricing

$0.435 per 1M tokens (input)
$0.435 per 1 million input tokens (cache miss), $0.003625 per 1 million input tokens (cache hit), and $0.87 per 1 million output tokens
Free Version 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

Pros & Cons from Real Users

Pros

  • DeepSeek-V4-Pro is one of those models I keep coming back to because it handles serious work without feeling ridiculously expensive. For coding, repo analysis, long debugging threads, and agent-style workflows, the 1M-token context window is a huge advantage. I also like that it feels strong across both reasoning and implementation. I can use it to think through architecture, explain a messy bug, generate a fix, write tests, and then sanity-check the tradeoffs without constantly switching models. The Pro-Max reasoning mode is especially useful when I need it to slow down and really work through something. It is not the mode I would use for every quick answer, but for hard technical problems, it gives the model a lot more room to reason. The open-weight angle is a big plus too. As someone who uses it heavily, I like having more flexibility than a purely closed API model gives me.

Cons

  • It is still not something I would run on autopilot. For production code, I always review diffs, run tests, and check edge cases because even strong models can make confident mistakes. It can also be overkill for simple tasks. If I just need a quick explanation, small script, or lightweight edit, DeepSeek-V4-Flash may be the better fit. The size is another consideration. Open weights are great, but self-hosting a 1.6T-parameter MoE model is not casual infrastructure.

Training

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

Training

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

Company Information

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

Company Information

DeepSeek
Founded: 2023
China
deepseek.com

Alternatives

GLM-5.3

GLM-5.3

Z.ai

Alternatives

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Kimi K3

Moonshot AI
Qwen3.8-Max

Qwen3.8-Max

Alibaba
Inkling

Inkling

Thinking Machines Lab
Qwen3.5

Qwen3.5

Alibaba
DeepSeek-V4

DeepSeek-V4

DeepSeek

Categories

AI Models Supported

Categories

AI Coding Models Supported
AI Models Supported

Integrations

Bash Not Supported
C Not Supported
C# Not Supported
ClinePass Not Supported
DeepSeek Harness Not Supported
Java Not Supported
Kubernetes Not Supported
Lua Not Supported
MoClaw Not Supported
OfoxAI Not Supported
OpenClaw Not Supported
Oxlo.ai Not Supported
PHP Not Supported
Reasonix Not Supported
Ruby Not Supported
Rust Not Supported
SQL Not Supported
Scala Not Supported
Vercel AI Gateway Not Supported
XML Not Supported

Integrations

Bash Supported
C Supported
C# Supported
ClinePass Supported
DeepSeek Harness Supported
Java Supported
Kubernetes Supported
Lua Supported
MoClaw Supported
OfoxAI Supported
OpenClaw Supported
Oxlo.ai Supported
PHP Supported
Reasonix Supported
Ruby Supported
Rust Supported
SQL Supported
Scala Supported
Vercel AI Gateway Supported
XML Supported
Claim Beam and update features and information
Claim Beam and update features and information
Claim DeepSeek-V4-Pro and update features and information
Claim DeepSeek-V4-Pro and update features and information