Something weird happened in late December 2025. Two Chinese AI labs dropped competing coding models within 24 hours of each other. And they couldn’t be more different.
MiniMax M2.1 launched on December 22 with the simplest pitch imaginable: build full-stack apps, fast, and don’t pay much. GLM 4.7 followed on December 23 with a bolder bet: what if your AI could actually think through complex problems before writing a single line of code?
I’ve spent weeks testing both. And here’s what’s wild: the “better” model depends entirely on whether you’re shipping products or solving puzzles. This parallels what we saw with Claude Opus 4.6, which prioritized reasoning depth over speed. Let me show you why this is the most important AI model comparison of early 2026.
The Architecture Wars: MoE vs MoE (But Not Really)
Both models use Mixture of Experts (MoE) architecture, but the similarity ends there.
MiniMax M2.1 runs 230 billion parameters with only 10 billion activated per inference. This is efficiency engineering at its finest – you get the intelligence of a massive model with the speed of a tiny one. It’s like hiring a specialist consultant who brings only the exact expertise you need to the meeting, not the entire firm.
GLM 4.7 takes a different route. The flagship model packs 358 billion total parameters (though some sources cite 355B), with 32 billion activated. But here’s the twist: the GLM-4.7-Flash variant uses just 30 billion total parameters with 3 billion active. It’s the same model family, but optimized for two completely different deployment scenarios.
What does this mean in practice? MiniMax is built for horizontal scale – throw it at thousands of tasks simultaneously and it won’t buckle. GLM is built for vertical depth – give it one hard problem and watch it reason through dozens of solution paths. This echoes the specialization we’re seeing across Chinese agentic models.
Benchmark Reality Check: The Numbers That Actually Matter
Let’s cut through the marketing fluff. Here are the benchmarks that professional developers care about:
Coding Performance
| Benchmark | MiniMax M2.1 | GLM 4.7 | Winner |
|---|---|---|---|
| SWE-Bench Verified | 74.0% | 73.8% | MiniMax (barely) |
| SWE-Bench Multilingual | 72.5% | 66.7% | MiniMax (+5.8%) |
| Terminal-Bench 2.0 | 47.9% | 41.0% | MiniMax (+6.9%) |
| LiveCodeBench | Rank #8 | 84.9% (SOTA) | GLM |
| VIBE (App Generation) | 88.6% | Not tested | MiniMax |
The Takeaway: MiniMax dominates real-world coding tasks (SWE-Bench, Terminal automation). GLM wins on pure code synthesis (LiveCodeBench).
Reasoning & Mathematics
| Benchmark | MiniMax M2.1 | GLM 4.7 | Gap |
|---|---|---|---|
| AIME 2025 | Not disclosed | 95.7% | GLM crushes it |
| GPQA-Diamond | Not disclosed | 85.7% | GLM |
| Humanity’s Last Exam | Not disclosed | 42.8% | GLM |
| MMLU-Pro | 88.0% | Not disclosed | MiniMax |
The Pattern: If your AI needs to pass a PhD-level math exam, use GLM. If it needs to pass a product manager’s acceptance test, use MiniMax.
Pricing: The $0.30 Question
Here’s where things get interesting.
MiniMax M2.1:
Input: $0.30 per 1M tokens
Output: $1.20 per 1M tokens
Prompt caching: $0.03 per 1M tokens (read), $0.375 per 1M tokens (write)
GLM 4.7:
Input: $0.60 per 1M tokens
Output: $2.20 per 1M tokens
GLM-4.7-Flash: $0.07 input / $0.40 output (free tier available)
Do the math. For a million tokens of input, MiniMax costs half what GLM does. But if you’re running agent swarms with heavy caching (which is how modern agentic systems work), MiniMax’s $0.03 cached reads might be the real killer feature.
A Reddit developer put it best: “I switched from GLM to Minimax for my agent loops and cut my bill by 80%. The cached context from previous runs basically runs for free.”
The Developer Experience Split
The Reddit developer community has a fascinating consensus forming.
MiniMax M2.1 excels at:
- Building complete applications (web, mobile, backend)
- Following precise instructions over long contexts
- Simple, flatter code structures that are easy to navigate
- Multi-language support (Rust, Java, Golang, C++, Kotlin, Objective-C, TypeScript, JavaScript)
But developers report:
- Less automatic documentation generation
- Sometimes “bends” tests to pass without fixing root issues
- Not great for non-coding tasks
GLM 4.7 excels at:
- Deep modular architectures with comprehensive docs
- Agentic behavior (tests its own code via CLI execution)
- UI/frontend generation with clean, modern aesthetics
- Multilingual support (30+ languages)
But developers complain about:
- Chat template issues causing loops if misconfigured
- Performance highly dependent on the integration tool (“terrible in Open Code but excellent in Claude Code”)
- Can get “wrapped up in its own thinking” on complex problems
Context Windows: The Million-Token Club
Both models join the elite “million-token club,” but with different philosophies. This matters more than you think, especially for long-context applications.
MiniMax M2.1: 1,000,000 input tokens, 1,000,000 output tokens. That’s your entire codebase plus documentation, loaded at once.
GLM 4.7: 200,000 input tokens, 128,000 output tokens. Still massive, but a 5x difference.
Why does this matter? If you’re building an AI coding agent that needs to understand complex legacy codebases (think enterprise Java monoliths from 2010), MiniMax’s million-token window is a game-changer. You can load the entire call graph into context.
GLM’s 200k context is more than enough for 99% of modern projects but will struggle with truly massive systems.
The “Interleaved Thinking” Advantage
This is GLM 4.7’s secret weapon, detailed in Z.ai’s official announcement.
GLM features a three-tier thinking architecture:
- Interleaved Thinking: Thinks before every response and tool call
- Preserved Thinking: Retains thinking blocks across multi-turn conversations
- Turn-level Thinking: Per-turn control over reasoning depth
In practice, this means GLM “shows its work.” You see the reasoning process, which is invaluable for debugging why an AI made a particular architectural choice.
MiniMax doesn’t expose its reasoning chain in the same way. It just gives you the answer. This is faster and cheaper, but you lose the ability to understand why the model chose approach A over approach B.
For research teams and AI safety work, GLM’s transparent reasoning is critical. For shipping features on Friday afternoon, MiniMax’s “just tell me what to build” approach wins.
Local Deployment: The Mac Reality
Both models can run locally, but the requirements differ dramatically.
MiniMax M2.1:
- Requires 128GB+ RAM for full model
- Best on Mac M4 Max with unified memory
- Can run quantized versions (INT4) on 64GB
GLM-4.7-Flash:
- Minimum: 24GB VRAM (RTX 3090/4090), 32GB RAM
- Recommended: 48GB VRAM (RTX 6000 Ada), 64GB RAM
- Runs at ~85 tokens/second on Mac M4 Max
The Mac crowd overwhelmingly prefers these Chinese models over RTX 4090 setups because Apple’s Unified Memory architecture shines with 70B+ models. As one LocalLLaMA user noted: “My M4 Max with 128GB runs MiniMax better than my friend’s dual RTX 4090 setup. Unified memory is cheat code for LLMs.”
The Cerebras Speed Demon
GLM 4.7 has one unfair advantage: Cerebras deployment.
When running on Cerebras infrastructure, GLM-4.7 achieves:
- Output speed: 677.8 tokens/second
- Latency: 0.24 seconds
For comparison, most cloud GPUs deliver 20-50 tokens/second. Cerebras is 10-30x faster.
If you’re building real-time agentic systems where every millisecond of latency compounds across dozens of tool calls, GLM on Cerebras is the only option that doesn’t make your users wait.
The Real Question: Are You Building or Researching?
After testing both models extensively, the decision tree is clear:
Choose MiniMax M2.1 if:
- You’re shipping production applications
- Cost per token matters (high-volume usage)
- You need native support for iOS/Android development
- You want simple, maintainable code over complex architectures
- You’re running agent swarms with prompt caching
Choose GLM 4.7 if:
- You’re solving complex mathematical or reasoning problems
- You need transparent reasoning chains for debugging
- UI/frontend quality is critical
- You’re integrating with tools like Claude Code or Cline
- You have access to Cerebras or similar high-speed inference
Use both if you’re smart:
Several Reddit developers are now running hybrid systems: MiniMax for agentic task execution, GLM for planning and solution exploration. As one engineer explained: “I use GLM to design the architecture and generate the test suite. Then I hand the implementation plan to MiniMax which builds it faster and cheaper.”
This approach mirrors what we’re seeing with desktop AI agents like MiniMax Agent, which can orchestrate multiple models for different tasks.
The Bottom Line
We’re not in a world where “one model rules them all” anymore. MiniMax M2.1 and GLM 4.7 represent a fundamental bifurcation in AI design philosophy.
MiniMax is the pragmatist: cheap, fast, gets stuff done. GLM is the perfectionist: thoughtful, transparent, occasionally over-engineered.
The fact that both models came from Chinese labs and both launched within 24 hours tells you everything about where the real innovation is happening in early 2026. While Western labs fight over AGI timelines and Sam Altman tweets, Chinese teams are shipping production-grade coding agents that developers actually want to use.
Pick your fighter. Or better yet, use both.
FAQ
Can I run both models locally on the same machine?
Yes, but you’ll need serious hardware. MiniMax requires 128GB+ RAM, GLM-4.7-Flash needs 24GB VRAM minimum. A Mac M4 Max with 128GB unified memory can run either model comfortably. Running both simultaneously would require memory swapping unless you’re on a workstation with 256GB+ RAM.
Which model is better for learning to code?
GLM 4.7. Its “Interleaved Thinking” shows you the reasoning process, which is invaluable for understanding why certain architectural choices were made. MiniMax is better once you already know what you want to build.
What about updates? Is MiniMax M2.2 coming soon?
Yes. MiniMax M2.2 is expected before mid-February 2026 with claimed 30% faster reasoning. GLM 4.8 hasn’t been announced yet, but Z.ai’s release cadence suggests Q2 2026.
Which integrates better with VSCode?
Both integrate well through standard Language Server Protocol (LSP) extensions. GLM performs notably better in Claude Code and Cline environments. MiniMax has better native OpenRouter integration for multi-model workflows.
Can these replace GitHub Copilot?
For experienced developers, yes. For junior devs, not yet. Both models excel at building complete features, but they’re weaker at the “show me 3 ways to write this function” interactive help that Copilot does well. Think of these as architectural partners, not autocomplete.
