Gemma
Google DeepMind
FreeLlama
Meta
FreeGemma vs Llama: Full Comparison (2026)
Gemma is google's lightweight open models - gemma 3 leads open-source benchmarks. Llama is meta's open-source llm family - llama 4 with multimodal moe architecture. Use the breakdown below to find the right fit for your needs.
This page presents factual information sourced from publicly available vendor documentation and product pages. AIHub does not endorse either product. The right tool depends on your specific use case, team, and requirements — we recommend evaluating both tools directly before making a decision.
Side-by-Side Overview
Pricing Model
Gemma
FreeLlama
FreeAPI Access
Gemma
Not availableLlama
Not availablePlatforms
Gemma
WebLlama
WebIntegrations
Gemma
—Llama
—Vendor
Gemma
Google DeepMindLlama
MetaCategory
Gemma
Language ModelsLlama
Language ModelsLaunch
Gemma
—Llama
—Models
Gemma
Gemma 3 27B, Gemma 3 12BLlama
Llama 5 (Apr 2026, 600B+ params, multimodal), Llama 4 Maverick (17B active / 400B total)| Feature | Gemma | Llama |
|---|---|---|
| Pricing Model | Free | Free |
| API Access | Not available | Not available |
| Platforms | Web | Web |
| Integrations | — | — |
| Vendor | Google DeepMind | Meta |
| Category | Language Models | Language Models |
| Launch | — | — |
| Models | Gemma 3 27B, Gemma 3 12B | Llama 5 (Apr 2026, 600B+ params, multimodal), Llama 4 Maverick (17B active / 400B total) |
About Gemma
Gemma 3 (released Mar 2025) is Google DeepMind's family of open-source models available in 1B, 4B, 12B, and 27B parameter sizes. The 27B model leads open-source benchmarks outperforming Llama 3.3 70B at half the size. Features 128K context and multimodal support.
Designed For
- Local AI deployment
- Fine-tuning
- Research
- Edge devices
About Llama
Llama is Meta's family of open-weight large language models. Llama 4 Scout (17B active params, 16 experts MoE) and Maverick (17B active, 128 experts) delivered top multimodal benchmarks at launch Apr 2025. Llama 3.3 70B remains the best open-source value for text tasks.
Designed For
- Self-hosted AI
- Fine-tuning for custom tasks
- Research
- Edge deployment
Strengths & Limitations
Gemma
Strengths
- Runs on consumer hardware
- Open weights
- Gemma 3 27B beats 70B models
- 128K context
- Multimodal (image input)
Limitations
- Smaller capability ceiling vs frontier models
- No official fine-tuning SLAs
Llama
Strengths
- Fully open weights
- Multiple size options (1B–400B)
- Commercial license
- Multimodal (Llama 4)
- MoE efficiency
Limitations
- Requires hosting infrastructure
- Large models need significant GPU RAM
Frequently Asked Questions
What is the difference between Gemma and Llama?
Gemma is google's lightweight open models - gemma 3 leads open-source benchmarks, while Llama is meta's open-source llm family - llama 4 with multimodal moe architecture. Gemma is designed for Language Models; Llama is designed for Language Models. The right fit depends on your specific requirements.
How do the pricing models compare?
Gemma is available under a Free model. Llama is available under a Free model. Always verify pricing on each vendor's official website as it may change.
How do I choose between Gemma and Llama?
Consider your team's technical requirements, budget, existing tooling, and use case before deciding. We recommend signing up for free trials or demos of both tools where available, and consulting each vendor's documentation. AIHub provides this comparison for informational purposes only.
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Data sourced from public vendor documentation. Pricing, features, and availability may change. Always verify on official vendor websites before making purchasing decisions. AIHub is not affiliated with any of the listed vendors.