MiniMax M2

MiniMax M2

MiniMax
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About

MiniMax M2 is an open source foundation model built specifically for agentic applications and coding workflows, striking a new balance of performance, speed, and cost. It excels in end-to-end development scenarios, handling programming, tool-calling, and complex, long-chain workflows with capabilities such as Python integration, while delivering inference speeds of around 100 tokens per second and offering API pricing at just ~8% of the cost of comparable proprietary models. The model supports “Lightning Mode” for high-speed, lightweight agent tasks, and “Pro Mode” for in-depth full-stack development, report generation, and web-based tool orchestration; its weights are fully open source and available for local deployment with vLLM or SGLang. MiniMax M2 positions itself as a production-ready model that enables agents to complete independent tasks, such as data analysis, programming, tool orchestration, and large-scale multi-step logic at real organizational scale.

About

NVIDIA Nemotron 3.5 Bolt is a family of open artificial intelligence models designed for local AI agents, autonomous task execution, and conversational applications. The family includes Nemotron 3.5 Bolt 74B-A9B, a 74-billion-parameter mixture-of-experts model with approximately 9 billion active parameters, and Nemotron 3.5 Bolt E4B, a compact model optimized for conversational agents. The 74B-A9B variant uses native 2-bit quantization to operate within approximately 24GB of memory on compatible NVIDIA hardware. Its agentic capabilities support web research, software development, code debugging, and multistep task automation without requiring cloud-based inference. The E4B variant targets local voice assistants, interactive characters, and embedded applications with a 4GB memory footprint. Nemotron 3.5 Bolt supports deployment on NVIDIA RTX systems, DGX Spark, and other compatible hardware, giving developers greater control over inference, application data, and local AI workflows.

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 Not Supported
Mac Not Supported
Linux Supported
Cloud Supported
On-Premises Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Audience

Software engineering teams, AI practitioners and developer-led organizations requiring a tool offering a model optimized for agent workflows and full-stack coding tasks

Audience

AI developers, software engineers, machine learning researchers, game developers, robotics engineers, enterprise IT teams, and organizations building local autonomous agents, coding assistants, conversational AI systems, and privacy-conscious applications on NVIDIA hardware

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 Supported

API

Offers API Supported

Screenshots and Videos

Screenshots and Videos

Pricing

$0.30 per million input tokens
Free Version Supported
Free Trial Supported

Pricing

Free
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
features 5.0 / 5

Pros & Cons from Real Users

Pros

  • What I like most is that I can run a genuinely capable agent model locally without needing datacenter hardware. The 74B-A9B version fits in 24GB of memory, which makes it realistic for high-end RTX systems and DGX Spark. It is also surprisingly useful for real agent work. I can use it for research, coding, debugging, writing, and multi-step tasks while keeping everything on my own machine instead of pushing code or sensitive data to a cloud API. The performance-to-memory tradeoff is probably the biggest win. NVIDIA says Bolt 74B-A9B is the #1 open model in its class on PinchBench and #2 on BrowseComp, so it is not just small enough to run locally — it is actually competitive at agentic tasks.

Cons

  • The main downside is that 24GB is still a meaningful hardware requirement, and local inference will not always match the raw capability of the biggest cloud frontier models.

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

MiniMax
Founded: 2021
Singapore
www.minimax.io/news/minimax-m2

Company Information

NVIDIA
Founded: 1993
United States
nvidia.com

Alternatives

Alternatives

Devstral 2

Devstral 2

Mistral AI
Devstral Small 2

Devstral Small 2

Mistral AI
MiniMax M3

MiniMax M3

MiniMax
MiniMax M2.5

MiniMax M2.5

MiniMax

Categories

AI Coding Models Supported
AI Models Supported
Foundation Models Supported

Categories

Integrations

Baseten Not Supported
Claude Code Supported
Cline Supported
DeepSeek Supported
Hugging Face Transformers Not Supported
Kilo Code Supported
NVIDIA DRIVE Supported
NVIDIA NIM Not Supported
Okara Supported
OpenAI Supported
OpenRouter Not Supported
Python Supported
Shiori Supported
vLLM Not Supported

Integrations

Baseten Supported
Claude Code Not Supported
Cline Not Supported
DeepSeek Not Supported
Hugging Face Transformers Supported
Kilo Code Not Supported
NVIDIA DRIVE Not Supported
NVIDIA NIM Supported
Okara Not Supported
OpenAI Not Supported
OpenRouter Supported
Python Not Supported
Shiori Not Supported
vLLM Supported
Claim MiniMax M2 and update features and information
Claim MiniMax M2 and update features and information
Claim Nemotron 3.5 Bolt and update features and information
Claim Nemotron 3.5 Bolt and update features and information