Hy3

Hy3

Tencent
MiniMax M3

MiniMax M3

MiniMax
+
+

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About

Hy3 preview is Tencent Hy’s most intelligent model in the Hy series to date, built as a 295B-parameter Mixture-of-Experts model with 21B activated parameters, 3.8B MTP layer parameters, and support for up to a 256K token context window. As the first model trained on Tencent Hy’s rebuilt infrastructure, Hy3 preview is designed to improve real-world usability across complex reasoning, instruction following, context learning, coding, agent capabilities, and overall inference performance. It integrates both fast and slow thinking capabilities, allowing direct responses for simpler tasks and deeper reasoning for complex math, coding, and reasoning work. The model is built around well-rounded capabilities across long-context understanding, instruction following, tool use, and agent workflows, with evaluation focused not only on standard benchmarks but also on authentic business and development scenarios.

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 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

Agent-framework developers who need a large reasoning model for coding, tool use, long-context tasks, and real-world agent workflows

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 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 Supported

Screenshots and Videos

Screenshots and Videos

Pricing

Free
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

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 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 Not Supported
In Person Not Supported

Training

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

Company Information

Tencent
Founded: 1998
China
hy.tencent.com/research/hy3-preview

Company Information

MiniMax
Founded: 2021
Singapore
www.minimax.io

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Categories

AI Coding Models Supported
AI Models Supported

Categories

Integrations

APIFree Not Supported
Alibaba AI Coding Plan Not Supported
Clawd.run Not Supported
Cline 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 Go Not Supported
OpenCode Zen Not Supported
OpenTag Not Supported
Pi Agent Not Supported
Tencent Hy Supported
Together AI Not Supported
Vision Agents Not Supported

Integrations

APIFree Supported
Alibaba AI Coding Plan Supported
Clawd.run Supported
Cline 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 Go Supported
OpenCode Zen Supported
OpenTag Supported
Pi Agent Supported
Tencent Hy Not Supported
Together AI Supported
Vision Agents Supported
Claim Hy3 and update features and information
Claim Hy3 and update features and information
Claim MiniMax M3 and update features and information
Claim MiniMax M3 and update features and information