Two of the most significant open-weight AI releases of April 2026 — GLM-5.1 from Zhipu AI and Gemma 4 from Google DeepMind — take fundamentally different approaches to the same goal: making frontier AI accessible. GLM-5.1 is a large MoE model built for long-horizon agentic coding. Gemma 4 is a family of efficient models that run on a single GPU. Here's how they compare across every dimension that matters.
📋 Table of Contents
- 1.Architecture & Model Sizes
- 2.Coding Performance Comparison
- 3.Reasoning & Math
- 4.Licensing: MIT vs Apache 2.0
- 5.Hardware Requirements
- 6.Edge & Mobile Deployment
- 7.Agentic Capabilities
- 8.Which Should You Choose?
- 9.Lushbinary Integration Services
1Architecture & Model Sizes
| Feature | GLM-5.1 | Gemma 4 |
|---|---|---|
| Architecture | MoE (large) | Dense + MoE variants |
| Model sizes | Single flagship | 2.3B, 9B, 26B MoE, 31B Dense |
| Context window | 200K | 256K |
| Multimodal | Text | Text, images, video, audio |
| Function calling | Yes | Yes (native) |
Gemma 4 offers four model sizes for different deployment scenarios, from edge devices (2.3B) to server-grade (31B Dense). GLM-5.1 is a single large model designed for maximum capability. Gemma 4 adds native multimodal support across text, images, video, and audio — GLM-5.1 is text-focused.
2Coding Performance Comparison
| Benchmark | GLM-5.1 | Gemma 4 31B |
|---|---|---|
| SWE-Bench Pro | 58.4% | — |
| NL2Repo | 42.7% | — |
| Codeforces ELO | — | 2150 |
| Arena AI (text) | — | #3 open models |
Direct benchmark comparison is limited since the models target different evaluation suites. GLM-5.1 dominates on agentic coding benchmarks (SWE-Bench Pro, NL2Repo). Gemma 4's 31B Dense model excels on competitive programming (Codeforces ELO jumped from 110 to 2150) and ranks #3 among open models on Arena AI.
3Reasoning & Math
GLM-5.1 scores 95.3% on AIME 2026 and 86.2% on GPQA-Diamond. Gemma 4's 31B Dense model outperforms models up to 20× its size on Arena AI benchmarks. Both are strong reasoners, but GLM-5.1 has the edge on absolute performance while Gemma 4 wins on performance-per-parameter.
4Licensing: MIT vs Apache 2.0
Both licenses are highly permissive and allow unrestricted commercial use. The MIT License (GLM-5.1) is slightly simpler — it requires only copyright notice inclusion. Apache 2.0 (Gemma 4) adds explicit patent grants and contribution terms. For most practical purposes, both are equally enterprise-friendly.
5Hardware Requirements
This is where the models diverge most sharply:
| Model | Min GPUs | Target Hardware |
|---|---|---|
| GLM-5.1 (full) | 8× H100 | Data center |
| Gemma 4 31B | 1× H100 | Single GPU server |
| Gemma 4 9B | Consumer GPU | Workstation |
| Gemma 4 2.3B | Mobile/Edge | Phone, IoT |
6Edge & Mobile Deployment
Gemma 4 is explicitly designed for edge deployment — the 2.3B model runs on mobile devices and IoT hardware. GLM-5.1 is a data center model with no edge deployment path. If you need on-device AI, Gemma 4 is the clear choice.
7Agentic Capabilities
GLM-5.1's long-horizon agentic capabilities are its defining feature — sustained optimization over 600+ iterations, 6,000+ tool calls, and 8-hour development sessions. Gemma 4 supports function calling and agentic workflows but hasn't been demonstrated at the same extended horizons.
8Which Should You Choose?
- Choose GLM-5.1 for maximum coding capability, long-horizon agentic tasks, and complex software engineering workflows where you have data center infrastructure.
- Choose Gemma 4 for efficient deployment on limited hardware, edge/mobile use cases, multimodal applications, or when you need multiple model sizes for different tiers.
9Lushbinary Integration Services
At Lushbinary, we help teams choose and deploy the right open-weight models for their specific requirements — whether that's GLM-5.1 for agentic coding or Gemma 4 for efficient edge deployment.
🚀 Free Consultation
Choosing between open-weight models for your project? We help teams evaluate GLM-5.1, Gemma 4, and other frontier models for their specific requirements.
❓ Frequently Asked Questions
How does GLM-5.1 compare to Gemma 4?
GLM-5.1 and Gemma 4 serve different niches. GLM-5.1 is a large MoE model optimized for long-horizon agentic coding (SWE-Bench Pro 58.4%). Gemma 4 is a family of smaller models (2.3B–31B) optimized for efficiency and edge deployment under Apache 2.0. GLM-5.1 wins on raw coding performance; Gemma 4 wins on accessibility and hardware requirements.
Which is better for coding: GLM-5.1 or Gemma 4?
GLM-5.1 significantly outperforms Gemma 4 on coding benchmarks like SWE-Bench Pro and NL2Repo. However, Gemma 4's 31B Dense model runs on a single H100 GPU while GLM-5.1 requires multi-GPU clusters. For resource-constrained environments, Gemma 4 offers better performance per dollar.
📚 Sources
- Z.ai — GLM-5.1: Towards Long-Horizon Tasks (April 7, 2026)
- HuggingFace — GLM-5.1 Model Weights
- GitHub — GLM-5.1 Repository
Content was rephrased for compliance with licensing restrictions. Benchmark data sourced from official Zhipu AI publications as of April 8, 2026. Pricing and availability may change — always verify on the vendor's website.
Choosing the Right Open-Weight Model?
Lushbinary helps teams choose and deploy the right open-weight models — whether that's GLM-5.1 for agentic coding or Gemma 4 for efficient edge deployment.
Build Smarter, Launch Faster.
Book a free strategy call and explore how LushBinary can turn your vision into reality.
