Gemma 4 vs Llama 4: Benchmarks, Speed, Context, License

Two of the most capable open AI models launched in early 2026: Google's Gemma 4 and Meta's Llama 4 Maverick. Both are free, both are powerful — but they serve different use cases. Here's how they compare.

Quick Comparison

Where Gemma 4 Wins

1. Edge and Mobile Deployment

Gemma 4's biggest advantage is its range of model sizes. The E2B (2B) model runs on a smartphone, the E4B (4B) on a laptop — no GPU needed. Llama 4 Maverick at 400B parameters requires serious server hardware. Learn more about deploying Gemma 4 on mobile devices.

2. Multimodal Breadth

Gemma 4 natively processes text, images, audio, and video. Llama 4 handles text and images but lacks native audio and video understanding. Check our guide on Gemma 4's multimodal capabilities for practical examples.

3. Language Coverage

With 140+ languages built-in, Gemma 4 is far more globally accessible. Llama 4 supports 12 languages — enough for major markets but limited for global applications.

4. Licensing Freedom

Apache 2.0 means no restrictions whatsoever. Llama 4's license has commercial use limitations for companies with 700M+ monthly active users.

Where Llama 4 Wins

1. Raw Power

At 400B parameters with MoE architecture, Llama 4 Maverick is simply a larger, more capable model for complex reasoning tasks when you have the hardware.

2. Context Length

10M token context window vs Gemma 4's 256K. For processing extremely long documents or codebases, Llama 4 has a clear edge.

3. Ecosystem Maturity

Meta's Llama series has been around since 2023. The ecosystem of tools, fine-tunes, and community resources is more mature.

Benchmark Comparison

Based on published benchmarks (April 2026):

The key insight: Llama 4 scores higher on most benchmarks — but it's a 400B MoE model vs Gemma 4's 31B dense model. Gemma 4 achieves ~90-95% of Llama 4's quality at a fraction of the compute cost. Per-parameter efficiency is where Gemma 4 shines.

Note: Direct head-to-head benchmarks vary by task and quantization level. Numbers above are based on FP16 precision.

Real-World Performance

Benchmarks tell part of the story, but practical performance matters more. Here's what you'll actually experience:

Speed Comparison (Same Hardware: RTX 4090 24GB)

On consumer hardware, this comparison is unfair — Gemma 4 runs, Llama 4 doesn't. That's the whole story for most people.

Speed Comparison (Cloud: 4× A100 80GB)

Even on cloud hardware, Gemma 4 is 4× cheaper per query due to lower GPU requirements.

Quality Comparison (Subjective, Blind Test on 50 Prompts)

For everyday tasks, the quality gap is small enough that most users won't notice. The gap only shows up on complex multi-step reasoning where Llama 4's sheer size gives it an edge.

Quick Decision Tree

What hardware do you have?
├── Consumer laptop/desktop (≤24GB GPU)
│   └── → Gemma 4 (Llama 4 doesn't fit)
├── Phone or Raspberry Pi
│   └── → Gemma 4 E2B/E4B (only option)
├── Single cloud GPU (A100 80GB)
│   └── → Gemma 4 31B FP16 (best cost/quality)
└── Multi-GPU server (4× A100+)
    ├── Need maximum reasoning power?
    │   └── → Llama 4 Maverick
    ├── Need audio/video understanding?
    │   └── → Gemma 4 (Llama 4 can't do it)
    ├── Need 140+ languages?
    │   └── → Gemma 4
    └── Need 10M token context?
        └── → Llama 4 Maverick

Which Should You Choose?

Not sure which Gemma 4 model size to start with? Our detailed comparison guide can help you decide.

Choose Gemma 4 if:

• You need to run AI on phones, laptops, or edge devices
• You need multimodal input (especially audio/video)
• You're building for a global, multilingual audience
• You want zero licensing restrictions (Apache 2.0)
• You want the fastest path from download to running

Choose Llama 4 if:

• You have powerful GPU servers available (see our hardware requirements guide to compare specs)
• You need maximum reasoning capability for complex tasks
• You need extremely long context (10M tokens)
• You're already invested in the Llama ecosystem

Can You Run Both?

Yes! Many developers use both:

• Gemma 4 E4B for local development and testing (fast, low resources)
• Llama 4 Maverick on cloud servers for production heavy-lifting

Both models are available through Ollama, making it easy to switch between them. New to Ollama? Our complete guide covers installation and usage.

For advanced use cases, Gemma 4 also supports function calling, which is essential for building AI agents and tool-using applications.

Bottom Line

Gemma 4 is the best open model you can run on your own hardware. Its range of model sizes, multimodal capabilities, and Apache 2.0 license make it the most versatile choice for most developers.

Llama 4 is the most powerful open model period — but you need the hardware to match.

For most individual developers and small teams, Gemma 4 is the practical choice. For organizations with GPU clusters, Llama 4 unlocks higher ceilings.

Want to see how both compare to other options? Check our comprehensive ranking of the best local AI models in 2026.

FAQ

Is Gemma 4 better than Llama 4?

It depends on your use case and hardware. Gemma 4 is better for local deployment, multilingual applications, and multimodal tasks (audio/video). Llama 4 is better for maximum reasoning power when you have multi-GPU servers. For most developers, Gemma 4 is the more practical choice.

Can I run Llama 4 Maverick on my laptop?

No. Llama 4 Maverick has 400B parameters (MoE architecture) and requires 128+ GB of GPU VRAM even when quantized. It's a server-only model. Gemma 4 31B in 4-bit quantization runs on a single consumer GPU with 16 GB VRAM.

Which model is better for coding?

Both are strong at code generation. Llama 4 scores slightly higher on HumanEval benchmarks (74.8 vs 72.1), but Gemma 4 produces more consistent output formatting and follows instructions more reliably. For local coding assistants, Gemma 4 is the only practical option since Llama 4 can't run locally.

Which model supports more languages?

Gemma 4 supports 140+ languages compared to Llama 4's 12 languages. If you need support for Japanese, Korean, Indonesian, Thai, Arabic, or any language outside the top 12, Gemma 4 is the clear choice.

Can I use both models together?

Yes. A common pattern is using Gemma 4 E4B for local development and quick prototyping, then routing complex queries to Llama 4 Maverick via a cloud API. Both support Ollama and OpenAI-compatible endpoints.

Is Gemma 4 or Llama 4 cheaper to run?

Gemma 4 is approximately 4x cheaper per query on cloud hardware because it only needs 1 GPU vs Llama 4's 4+ GPUs. For local inference, Gemma 4 costs nothing (runs on your own hardware) while Llama 4 requires expensive cloud servers.

Related Comparisons

Looking for more model comparisons? Check out these detailed analyses:

• Gemma 4 vs Qwen 3.5 - Compare with Alibaba's latest multilingual model
• Gemma 4 vs ChatGPT - Local vs cloud: when to use each
• Gemma 4 vs Gemini - Google's open source vs proprietary models
• Gemma 4 vs Gemma 3 - What's new in the latest generation
• Gemma 4 26B vs 31B - Choosing between Gemma 4's larger models
• Gemma 4 E2B vs E4B - Comparing Gemma 4's efficient edge models

Both models are freely available. Try Gemma 4 with one command: ollama run gemma4