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

MiniMax M3.1 Flash is a hosted large language model for developers building coding assistants, AI agents, and software applications. Available through MiniMax’s API and MiniMax Code, it accepts text, image, and video input and generates text responses. Documented uses include code generation and debugging, multi-step reasoning, tool use, codebase and document analysis, and computer-use workflows. The API offers a context window of up to 1 million tokens, adjustable reasoning effort, and OpenAI-compatible and Anthropic-compatible request formats. Usage is billed by input, output, and cached-input tokens. MiniMax provides documentation for API integration and model use. M3.1 Flash is available through the hosted API, M Plan, and MiniMax Code; model weights are planned for a later release.

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

Software developers and organizations building coding assistants, AI agents, and applications that use a hosted language model.

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

$0.15/1M input tokens
Input: $0.15 per 1 million tokens; output: $0.60 per 1 million tokens; cached input: $0.015 per 1 million tokens. Standard pay-as-you-go billing in USD, based on token usage.
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

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Reviews/Ratings

Overall 5.0 / 5
ease 5.0 / 5

Pros & Cons from Real Users

Pros

  • It feels noticeably faster than M3 while still being strong enough for coding, research, computer-use tasks, and longer agent workflows. The 1M-token context window is a big advantage too. I can keep large repos, long documents, screenshots, video context, and a lot of prior agent history in the same session without constantly trimming things down. The pricing is probably the best part. At $0.15 per million input tokens and $0.60 per million output tokens, I can run it heavily without worrying that every agent loop is getting expensive. I also like how much MiniMax improved the agent side. M3.1 Flash is aimed at real multi-step work across coding, enterprise automation, computer use, and even cybersecurity, rather than just fast chat responses.

Cons

  • The main downside is that reasoning is always enabled, so it is not quite as lightweight as a pure instant-response model for trivial tasks.

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

MiniMax
Founded: 2022
China
www.minimax.io/blog/minimax-m31f

Alternatives

Alternatives

Inkling

Inkling

Thinking Machines Lab
MiniMax M3

MiniMax M3

MiniMax
Qwen3.5

Qwen3.5

Alibaba
MiniMax

MiniMax

MiniMax AI

Categories

Categories

AI Coding Models Supported
AI Models Supported
Foundation Models Supported
Multimodal Models Supported

Integrations

APIFree Not Supported
Alibaba AI Coding Plan Not Supported
BLACKBOX AI Not Supported
Claude Code Not Supported
Clawd.run Not Supported
Cline Not Supported
ClinePass Not Supported
Fireworks AI Not Supported
Kilo Code Not Supported
MiniMax Mavis Not Supported
Model Context Protocol (MCP) Not Supported
Ollama Not Supported
OpenClaw Not Supported
OpenCode Go Not Supported
OpenTag Not Supported
Pi Agent Not Supported
Roo Code Not Supported
Tabbit Browser Not Supported
Together AI Not Supported
Vision Agents Not Supported

Integrations

APIFree Supported
Alibaba AI Coding Plan Supported
BLACKBOX AI Supported
Claude Code Supported
Clawd.run Supported
Cline Supported
ClinePass Supported
Fireworks AI Supported
Kilo Code Supported
MiniMax Mavis Supported
Model Context Protocol (MCP) Supported
Ollama Supported
OpenClaw Supported
OpenCode Go Supported
OpenTag Supported
Pi Agent Supported
Roo Code Supported
Tabbit Browser Supported
Together AI Supported
Vision Agents Supported
Claim Beam and update features and information
Claim Beam and update features and information
Claim MiniMax M3.1 Flash and update features and information
Claim MiniMax M3.1 Flash and update features and information