Gemma 4 vs Llama 4
Gemma 4 vs Llama 4: reasoning quality vs massive context
Google's Gemma 4 and Meta's Llama 4 are two of the most popular open model families. Gemma leads on math reasoning (89.2% vs ~73% AIME), multimodal quality, and edge models with audio. Llama leads on context length (10M tokens) and model scale. Here's the full breakdown.
Quick verdict
When to choose each model
Both are widely adopted. The right choice depends on your primary use case and licensing needs.
Choose Gemma 4 when
Math reasoning, multimodal quality, edge models, or Apache 2.0
Gemma 4 excels at mathematical reasoning (89.2% AIME vs Llama's ~73%), multimodal understanding (76.9% MMMU Pro), and offers edge models with native audio (E2B/E4B). Apache 2.0 license has no MAU restrictions.
Best for: math tutoring, document analysis, on-device AI with audio, multimodal applications, and deployments where Apache 2.0 licensing matters.
Choose Llama 4 when
10M context, larger model scale, or Meta ecosystem
Llama 4 Scout offers a 10M token context window - the largest among open models. Maverick's 400B total parameters with 128 experts provides massive scale. Meta's ecosystem offers extensive tooling and community support.
Best for: very long context tasks, large-scale deployments within Meta's ecosystem, and applications where 10M context is critical.
Google DeepMind
Gemma 4 31B Dense
#3 on Arena AI. 89.2% AIME, 80% LiveCodeBench, 76.9% MMMU Pro. Dense architecture with 256K context.
30.7B parameters, all active. Best for maximum quality across reasoning, coding, and multimodal tasks.
Google DeepMind
Gemma 4 26B A4B MoE
Near-31B quality at 4B inference cost. 88.3% AIME, 77.1% LiveCodeBench. 256K context.
25.2B total, 3.8B active per token. 128 experts, 8 active + 1 shared.
Meta
Llama 4 Scout
109B total, 17B active. 16 experts. 10M token context window - the largest among open models.
MoE architecture optimized for extremely long context. Fits on a single H100 GPU for inference.
Meta
Llama 4 Maverick
400B total, 17B active. 128 experts. Strong general performance across reasoning and coding tasks.
Larger MoE variant with more experts for higher quality. Requires multi-GPU setup for inference.
Head to head
Where each model wins
A category-by-category breakdown of strengths and weaknesses.
Math reasoning: Gemma wins
Gemma 4 31B: 89.2% AIME 2026. Llama 4 Maverick: ~73%. Gemma has a 16-point lead on mathematical reasoning.
Context window: Llama wins
Llama 4 Scout: 10M tokens. Gemma 4: 256K. Llama's context window is nearly 40x larger - a massive advantage for long documents.
Multimodal quality: Gemma wins
Gemma 4: 76.9% MMMU Pro with native vision. Llama 4 has multimodal support but Gemma achieves higher benchmark scores on visual understanding.
Model scale: Llama wins
Llama 4 Maverick: 400B total, 128 experts. Gemma 4: 31B max. Llama offers larger model options for maximum capability.
Edge deployment: Gemma wins
Gemma 4 has E2B (2.3B) and E4B (4.5B) edge models with native audio. Llama 4's smallest model (109B total) is server-focused.
Licensing: Gemma wins
Gemma 4: Apache 2.0 with no restrictions. Llama 4: Llama Community License with MAU restrictions. Apache 2.0 is simpler for commercial use.
Architecture comparison
MoE approaches: efficiency vs scale
Both families use MoE architecture, but with very different design goals.
Gemma 4 26B A4B
- 25.2B total parameters, 3.8B active per token
- 128 experts, 8 active + 1 shared
- 256K context window
- Native multimodal (text + image)
- Apache 2.0 license, no restrictions
Llama 4 Scout
- 109B total parameters, 17B active per token
- 16 experts in MoE architecture
- 10M token context window
- Multimodal support (text + image)
- Llama Community License (MAU restrictions)
Benchmarks
Complete benchmark comparison
Head-to-head benchmark results across reasoning, coding, multimodal, and deployment.
Gemma leads on math reasoning, multimodal quality, and edge deployment. Llama leads on context length and model scale. The choice depends on your primary use case.
Math: Gemma 4 31B (89.2% AIME) vs Llama 4 Maverick (~73%) - Gemma wins by 16 points
Context: Llama 4 Scout (10M tokens) vs Gemma 4 (256K) - Llama has 40x more context
Multimodal: Gemma 4 (76.9% MMMU Pro) - higher quality visual understanding
Licensing: Gemma 4 (Apache 2.0) vs Llama 4 (Community License with MAU limits)
Head to head
Gemma 4 vs Llama 4 on key benchmarks
Direct comparison across the most important evaluation benchmarks.
| Benchmark | Gemma 4 31B Dense 31B | Gemma 4 26B MoE 4B active 26B | Llama 4 Scout MoE 17B active 109B | Llama 4 Maverick MoE 17B active 400B |
|---|---|---|---|---|
MMLU Pro Knowledge & reasoning | 85.2% | 82.6% | 78.5% | 82.0% |
AIME 2026 Mathematics | 89.2% | 88.3% | 68.0% | 73.0% |
LiveCodeBench v6 Code generation | 80.0% | 77.1% | 70.5% | 74.0% |
SWE-Bench Verified Agentic coding | 52.0% | - | - | - |
MMMU Pro Multimodal | 76.9% | 73.8% | 65.0% | 69.5% |
Arena AI ELO Human preference | 1452 | 1441 | - | - |
Context Window Max tokens | 256K | 256K | 10M | 1M |
Total params Model size | 30.7B | 25.2B | 109B | 400B |
Active params Per token | 30.7B | 3.8B | 17B | 17B |
MoE Experts Architecture | Dense | 128 (8+1) | 16 | 128 |
License Commercial use | Apache 2.0 | Apache 2.0 | Llama Community | Llama Community |
Data from official model cards and independent evaluations. Scores may vary by evaluation methodology.
Reasoning
Math reasoning: Gemma 4's decisive advantage
Gemma 4's 89.2% on AIME 2026 vs Llama 4 Maverick's ~73% is a 16-point gap. This is one of the largest reasoning differences between major open model families. For math, science, and logical reasoning, Gemma 4 is the clear winner.
- AIME 2026: Gemma 4 89.2% vs Llama 4 Maverick ~73% - 16 point gap
- MMLU Pro: Gemma 4 85.2% vs Llama 4 Maverick 82.0%
- LiveCodeBench: Gemma 4 80.0% vs Llama 4 Maverick 74.0%
Context & Scale
10M context: Llama 4 Scout's unique advantage
Llama 4 Scout's 10M token context window is nearly 40x larger than Gemma 4's 256K. For processing entire codebases, very long documents, or massive datasets in a single pass, Llama 4 Scout is unmatched.
- Llama 4 Scout: 10M tokens - largest context among open models
- Llama 4 Maverick: 400B total params, 128 experts
- Gemma 4: 256K context - sufficient for most tasks but not extreme length
Licensing & Edge
Apache 2.0 and edge models: Gemma 4's practical advantages
Gemma 4's Apache 2.0 license has no MAU restrictions, unlike Llama's Community License. Combined with edge models (E2B/E4B) that include native audio, Gemma 4 offers more deployment flexibility for commercial products.
- Gemma 4: Apache 2.0 - no MAU restrictions, maximum commercial freedom
- Llama 4: Community License - includes MAU restrictions for large deployments
- Only Gemma 4 has edge models (2.3B-4.5B) with native audio support
Try both
Test the models yourself
The best comparison is hands-on experience.
Gemma 4 resources
Get started with Gemma 4
Everything you need to start building with Gemma 4.
Llama 4 resources
Learn more about Llama 4
Official Llama 4 resources and documentation.
Open model landscape
The best open models of 2026
Gemma 4 and Llama 4 are two of the most popular open model families, but they're not the only options.
Try Gemma 4
Experience Gemma 4's strengths firsthand
Try Gemma 4 for free and see how it performs on your specific tasks. Math reasoning, multimodal understanding, and edge deployment are where it shines brightest.