AI Tool Comparison
Gemma 4 vs Llama 4
A side-by-side breakdown to help you pick the right tool for your workflow.
Gemma 4
Run text, image, and supported audio workloads on your own infrastructure with Gemma 4. Choose an edge, dense, or mixture-of-experts variant to match your hardware and task.
Llama 4
Llama 4 Scout and Maverick remain Meta's last open-weight frontier models (April 2025) with up to 10M-token context. Meta paused the open Llama line in 2026 in favor of a new proprietary flagship.
Bottom Line
Last reviewed: August 2026
Gemma 4 and Llama 4 both sit in Models, but they're built around different use cases within it. Llama 4 carries the higher rating (4.6 vs 4.5), but a gap that size rarely overrides a real workflow fit on its own.
Choose Gemma 4 if…
Best for developers building local or self-hosted assistants who can manage deployment, evaluation, and data handling, and its edge is five deployment options: E2B, E4B, 12B Unified, 26B A4B MoE, and 31B Dense, with audio input on the three smaller dense variants. Choose Gemma 4 when control over model deployment matters and you can support the infrastructure; it is not a managed assistant subscription. Lean toward Llama 4 instead if open weights with a long context window and multimodal input, competitive with closed frontier models on most benchmarks matters more for your use case.
Choose Llama 4 if…
Best for developers and companies who want to self-host a capable model instead of calling a closed API, and its edge is open weights with a long context window and multimodal input, competitive with closed frontier models on most benchmarks. The default open-weight choice until something newer ships, but running the larger variants requires real hardware. Lean toward Gemma 4 instead if five deployment options: E2B, E4B, 12B Unified, 26B A4B MoE, and 31B Dense, with audio input on the three smaller dense variants matters more for your use case.
| Attribute | Gemma 4 | Llama 4 |
|---|---|---|
| Category | Models | Models |
| Pricing | free | free |
| Pricing Detail | Free Apache 2.0 model weights. Hardware, cloud compute, managed hosting, and serving costs are separate. | Free and open-weight, no Llama 5 has shipped |
| Rating |
Key Features
Gemma 4
- Apache 2.0 downloadable model weights
- E2B, E4B, 12B Unified, 26B A4B MoE, and 31B Dense variants
- Text and image input with text output across the family
- Audio input on E2B, E4B, and 12B Unified only
- 128K context on E2B/E4B; 256K on 12B/26B A4B/31B
- Native function calling for tool-connected applications
- Pretraining in 140+ languages and 35+ languages supported out of the box
- Pre-trained and instruction-tuned weights, with documented fine-tuning options
- Official quantized formats and deployment guidance for local and cloud environments
Llama 4
- Open weights
- Long context window
- Multimodal variants
- Huge fine-tuning ecosystem
Pros
Gemma 4
- •Permissive licensing and downloadable weights give developers deployment flexibility
- •Multiple architectures and sizes support different hardware budgets
- •Text, vision, and selected audio input can support several tasks in one deployment
- •Official model cards and deployment documentation explain variant-specific trade-offs
Llama 4
- •Industry-standard open model
- •Massive community support
- •Free to use
Cons
Gemma 4
- Serving, updates, evaluation, and access controls remain your responsibility
- Larger variants and long contexts can require substantial memory and compute
- Generated facts, interpretations, and tool calls still need validation
- Audio input is not available on every variant, and output is text only
Llama 4
- Large variants need serious hardware
- License restrictions at scale