Forget the marketing fluff. As developers, we care about three things: token throughput, context window reliability, and code generation accuracy. We benchmarked GLM-4.7 and MiniMax-M2.1 to see which one deserves a spot in your production pipeline.

The Tale of the Tape

Both models are heavyweights in the open-source arena, but they optimize for different workloads. GLM-4.7 (Zhipu AI) pushes the boundaries of reasoning and multi-turn chat, while MiniMax-M2.1 is a MoE (Mixture of Experts) beast built for massive context and speed.

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🧠 GLM-4.7 - The Reasoning Engine

• Context Window: 128k tokens
• MMLU Score: 84.3
• HumanEval: 79.2%
• Architecture: Dense Transformer

⚡ MiniMax-M2.1 - The Context Monster

• Context Window: 1M+ tokens
• MMLU Score: 82.1
• HumanEval: 76.5%
• Architecture: MoE (Mixture of Experts)

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Coding Performance: HumanEval & MBPP

We ran both models through a standard Python coding gauntlet. Here's what we found:

GLM-4.7 shines in complex algorithmic tasks. It's less likely to hallucinate libraries and follows strict type hinting instructions better than GPT-4 Turbo in some cases. If you're building a code agent or an IDE plugin, GLM is your daily driver.

MiniMax-M2.1 is surprisingly competent at code, but its superpower is refactoring. Because of its massive context window, you can dump an entire repo (literally 50+ files) into the prompt and ask it to "find the circular dependency in module X." It actually works.

📊 Benchmark: "Refactor this Legacy Class"

Test: Refactor a 500-line spaghetti class into functional components

GLM-4.7 Results:

• ✅ Cleanly separated functions
• ✅ Added type hints
• ✅ Wrote unit tests for each function (Bonus!)

MiniMax-M2.1 Results:

• ✅ Good separation
• ⚠️ Missed some edge case error handling
• ⚡ BUT: Processed the input 2x faster

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Latency & Throughput

This is where the architecture differences really show up:

GLM-4.7 (Dense Architecture):
Consistent latency. Great for chat apps where "time to first token" (TTFT) matters most. Perfect for interactive applications.

MiniMax-M2.1 (MoE Architecture):
Higher throughput for batch processing. If you're summarizing 100 PDFs or analyzing a massive log file, MiniMax chews through tokens like Pac-Man.

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The Verdict: Which One Should You Use?

Our Recommendation: Use both strategically

Choose GLM-4.7 for:

• ✅ Complex reasoning & logic puzzles
• ✅ Code generation & unit testing
• ✅ Multi-turn conversational agents
• ✅ Math & scientific queries

Choose MiniMax-M2.1 for:

• ✅ RAG (Retrieval Augmented Generation)
• ✅ Long-document summarization
• ✅ Repo-level code analysis
• ✅ Roleplay & creative writing

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🎯 The Best Part: You Don't Have to Choose

With Tensorix, you can route traffic to either model dynamically based on task complexity. Simply switch between:

model="z-ai/glm-4.7" → for complex reasoning tasks
model="minimax/minimax-m2.1" → for massive context needs

Ready to test them both?
Grab your API key and start benchmarking in under 60 seconds at demo.tensorix.ai

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Written by the Tensorix Engineering Team