Altar-1

Altar-1

Aikido Security
MiniMax M3

MiniMax M3

MiniMax
+
+

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About

Aikido Altar is an open-weight security model built to bring frontier-grade defensive security intelligence into infrastructure organizations' control. It is designed for sovereign security environments where sensitive source code, architecture documentation, vulnerability findings, and other internal context cannot be sent to third-party inference services. Altar is based on GLM-5.3 and uses quantization and expert pruning to reduce the model from 1.51 TB at full precision to 328 GB while preserving most of the parent model’s reasoning and security capabilities. It retains 168 of the original 256 routed experts per backbone expert layer and uses a W4A16 representation, making deployment more practical for agentic security workloads with large and growing context windows. Expert selection was calibrated using internal pentesting traces and multilingual data, with no customer data involved, to preserve cybersecurity, coding, and language understanding.

About

MiniMax M3 is an open-weight multimodal AI model designed for coding, agentic workflows, long-context reasoning, and complex automation tasks. The model combines frontier-level coding performance, native multimodal understanding, and a context window of up to 1 million tokens. MiniMax M3 uses MiniMax Sparse Attention to improve long-context efficiency while reducing compute requirements for large-scale inputs. It supports text, image, and video understanding, making it useful for workflows that combine code, documents, visual references, and tool-driven tasks. The model is built for repository-scale reasoning, software engineering, autonomous task execution, tool calling, and multi-step agent workflows. MiniMax M3 helps developers, AI teams, and enterprises build capable agents that can reason across large contexts and work with multimodal information.

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

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

Security teams and regulated organizations needing to run AI-powered vulnerability research, code analysis, remediation, and pentesting entirely within infrastructure they control

Audience

MiniMax M3 is best suited for developers, AI engineers, coding assistant builders, enterprise automation teams, research teams, data teams, agent developers, and organizations that need open-weight AI for coding, long-context reasoning, multimodal understanding, tool use, repository analysis, and autonomous workflows

Support

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

Support

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

API

Offers API Supported

API

Offers API Supported

Screenshots and Videos

Screenshots and Videos

Pricing

$350 per month
Free Version Supported
Free Trial Not Supported

Pricing

$0.30 per million input tokens
$0.30 per million input tokens and $1.20 per 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

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Reviews/Ratings

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

Pros & Cons from Real Users

Pros

  • The multimodal side also makes it more flexible than a code-only model. Being able to work across text, images, video-style understanding, and code gives it more room to support real workflows instead of being boxed into one narrow use case. For agent builders, the best part is that M3 is clearly designed around tool use and multi-step execution. MiniMax specifically calls out autonomous task decomposition and tool invocation, which is exactly what matters when a model is powering coding assistants, workflow agents, or automated dev tools.

Cons

  • The main catch is infrastructure and trust. Even with sparse attention and MoE efficiency, this is still a large model, so deployment is not casual. I would also want to benchmark it on my own repos before depending on it for production work.

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

Aikido Security
Founded: 2022
Belgium
www.aikido.dev/blog/aikido-altar-open-weight-ai-sovereign-security

Company Information

MiniMax
Founded: 2021
Singapore
www.minimax.io

Alternatives

No Alternatives

Alternatives

GPT-5.6 Sol

GPT-5.6 Sol

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MiniMax

MiniMax

MiniMax AI
MiniMax M1

MiniMax M1

MiniMax

Categories

AI Cybersecurity Supported
AI Models Supported

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
Cline Not Supported
ClinePass Not Supported
Factory Droid Not Supported
Fireworks AI Not Supported
Hermes Agent Not Supported
MaxHermes Not Supported
MiniMax Agent Not Supported
MiniMax Code Not Supported
MiniMax Mavis Not Supported
Ollama Not Supported
OpenClaw Not Supported
OpenCode Zen Not Supported
OpenTag Not Supported
Roo Code Not Supported
Tabbit Browser Not Supported
Together AI Not Supported

Integrations

APIFree Supported
Alibaba AI Coding Plan Supported
BLACKBOX AI Supported
Claude Code Supported
Cline Supported
ClinePass Supported
Factory Droid Supported
Fireworks AI Supported
Hermes Agent Supported
MaxHermes Supported
MiniMax Agent Supported
MiniMax Code Supported
MiniMax Mavis Supported
Ollama Supported
OpenClaw Supported
OpenCode Zen Supported
OpenTag Supported
Roo Code Supported
Tabbit Browser Supported
Together AI Supported
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