MiniMax M2 vs GLM 4.6 vs Kimi-K2-Thinking: The Next Generation of Open LLMs
Introduction
The AI landscape is evolving at lightning speed, and lately, three new names have captured the community’s attention: MiniMax M2, GLM 4.6, and Kimi-K2-Thinking.
All three claim to push the boundaries of reasoning, tool use, and efficiency — but they come from very different philosophies.
In this article, I’ll walk you through what makes each model unique, where they shine, and what real developers might care about when choosing between them.
MiniMax M2 — Efficient Power in Motion
MiniMax M2 is the latest MoE (Mixture-of-Experts) model from MiniMax AI, featuring a total of 230 billion parameters, but only ~10 billion active during inference.
That’s the secret sauce: massive potential, low compute cost.
The model focuses heavily on coding, agentic workflows, and reasoning efficiency. It’s designed to feel like a top-tier model while keeping inference fast and affordable.
In internal tests, M2 delivers roughly double the speed of comparable models at less than 10% of the cost — a massive deal for startups running large-scale applications.
“It’s like getting GPT-4-level reasoning at half the latency and a fraction of the price.”
🔹 Key Highlights
- Smart activation: only 10 B active params for cost efficiency
- Strong at coding, multi-file edits, and tool orchestration
- Smooth reasoning in long sessions
- Great for practical deployment on mid-range hardware
🔻 Weak Spots
- Still limited in ultra-long context reasoning
- Requires good prompt engineering for creative writing
GLM 4.6 — Balanced and Reliable
Zhipu AI’s GLM 4.6 follows the success of the 4.5 series and represents a balanced step-up in reasoning and writing alignment.
Unlike M2’s efficiency-first focus, GLM 4.6 aims for versatility: strong logical reasoning, clean prose, and solid code generation.
The most noticeable change from previous versions is how naturally it follows multi-step reasoning without losing context.
Writers like it because the text feels more “human,” and developers appreciate that it understands structured tasks, such as function calls or code repair.
🔹 Key Highlights
- Excellent all-rounder for reasoning, writing, and coding
- Improved tool-use and memory handling
- Native support for long context tasks (up to 128K tokens)
- Robust community ecosystem
🔻 Weak Spots
- Inference speed slower than MiniMax M2
- Can “overthink” during step-by-step reasoning
Kimi-K2-Thinking — The Ambitious Giant
If M2 is efficient and GLM is balanced, then Kimi-K2-Thinking is pure ambition.
Developed by Moonshot AI, it’s a 1-trillion-parameter MoE model, with around 32 billion active at inference.
It’s not just big — it’s trained specifically for tool-use, long-context workflows, and autonomous reasoning.
Kimi K2 is designed for complex agentic systems: writing multi-step plans, chaining API calls, or even generating code while running external tools.
Think of it as a model that can “think for itself,” breaking large problems into smaller pieces and solving them logically.
🔹 Key Highlights
- Massive 1T parameters with MoE routing (~32 B active)
- Exceptional performance in agentic reasoning tasks
- Built for long-context and multi-tool workflows
- Open-source and customizable
🔻 Weak Spots
- High system requirements for deployment
- Some creative writing responses still feel mechanical
- Cost can rise quickly in production use
Summary Table
| Model | Total Parameters | Active Parameters | Best For | Key Strength |
|---|---|---|---|---|
| MiniMax M2 | 230 B | ~10 B | Coding, Agents | Ultra-efficient speed and cost |
| GLM 4.6 | ~400 B | Full | General tasks | Balanced reasoning and writing |
| Kimi-K2-Thinking | 1 T | ~32 B | Long-context agents | Massive scale and flexibility |
Which Model Should You Choose?
It depends on your use case:
- For developers or startups: MiniMax M2 is the most practical choice. It’s fast, cheap, and great for coding or automation.
- For researchers and content creators: GLM 4.6 provides balance — it “thinks” well, writes smoothly, and integrates easily into workflows.
- For teams building autonomous agents or large reasoning systems: Kimi-K2-Thinking is in another league, assuming you have the GPU budget.
No clear “winner” — each model hits a different sweet spot. What’s exciting is how close these open models are getting to the top-tier closed ones.
Final Thoughts
The competition among Chinese AI labs is heating up — and that’s great news for users.
MiniMax M2 is shaping up to be the most deployment-friendly, GLM 4.6 is the most balanced, and Kimi-K2-Thinking is the most ambitious.
Each brings something fresh to the table: efficiency, reliability, or scale.
And honestly, we’re just getting started — 2025 is shaping up to be the year open LLMs truly rival the giants.
FAQ
1. Which model is fastest to deploy?
MiniMax M2. Its lightweight activation design means lower GPU load and quicker startup times.
2. Does Kimi-K2-Thinking outperform GPT-4 or Claude?
In some reasoning and agentic tasks, yes. But it still trails in language nuance and global availability.
3. Can I fine-tune GLM 4.6 locally?
Yes, it supports parameter-efficient fine-tuning (PEFT) and LoRA setups. You can run smaller variants on consumer GPUs.
4. What’s the main trade-off between M2 and Kimi K2?
M2 is built for efficiency and cost-saving; Kimi K2 prioritizes reasoning power and scale — but at higher resource demand.
5. Are these models open-source?
GLM 4.6 and Kimi-K2-Thinking are open-weight models. MiniMax M2 currently offers API and partial open access.
6. Which one is best for AI agents or automation?
MiniMax M2 and Kimi-K2-Thinking both perform well — M2 for affordability, K2 for complex tool orchestration.