Gemma 4 vs Llama 4 vs Mistral Small 4: Full Comparison
Three open-source AI model families launched in quick succession: Google's Gemma 4 under Apache 2.0, Meta's Llama 4 with a 10M context window, and Mistral Small 4 with 128 experts. This head-to-head comparison covers architecture, benchmarks, licensing, and deployment costs to help you choose the right model for your production workloads.
Llama 4 Scout context
Mistral Small 4 experts
Gemma 4 license
Parameter range
Key Takeaways
April 2026 marks the most competitive moment in open-source AI history. Gemma 4, Llama 4, and Mistral Small 4 each push the boundaries of what open models can achieve, but they take fundamentally different architectural approaches. Whether you need on-device inference, million-token context, or cost-efficient MoE routing, this comparison will help you make the right call.
Model Overview & Architecture
Each model family represents a distinct approach to building capable open models. Gemma 4 prioritizes parameter efficiency and on-device deployment. Llama 4 pushes the scale of context and expert count. Mistral Small 4 maximizes output quality per active parameter.
Released: April 2, 2026
Company: Google DeepMind
Architecture: Dense + MoE variants
Sizes: E2B, E4B, 26B A4B, 31B
License: Apache 2.0
Best for: On-device AI, edge deployment
Released: April 2026
Company: Meta AI
Architecture: MoE (16 experts)
Parameters: 17B active / 109B total
License: Meta Llama License
Best for: Long-context processing, RAG
Released: March 26, 2026
Company: Mistral AI
Architecture: MoE (128 experts)
Parameters: 6B active / 119B total
License: Apache 2.0
Best for: Coding, reasoning, multilingual
Gemma 4 Variant Breakdown
Gemma 4 offers four sizes designed for different deployment targets, from smartphones to cloud servers:
| Variant | Parameters | Architecture | Context | Modality |
|---|---|---|---|---|
| E2B | 2.3B effective | Dense + PLE | 128K | Text, image, video, audio |
| E4B | 4.5B effective | Dense + PLE | 128K | Text, image, video, audio |
| 26B A4B | 3.8B active / 26B total | MoE | 256K | Text, image |
| 31B | 31B dense | Dense | 256K | Text, image |
The “E” prefix stands for “effective parameters” - these models use Per-Layer Embeddings (PLE) to maximize parameter efficiency for on-device deployment.
MoE Architecture Comparison
Both Llama 4 and Mistral Small 4 use Mixture of Experts, but their approaches differ significantly:
| MoE Feature | Llama 4 Scout | Llama 4 Maverick | Mistral Small 4 |
|---|---|---|---|
| Total experts | 16 | 128 | 128 |
| Active per token | 1 | 1 | 4 |
| Active parameters | 17B | 17B | 6B |
| Total parameters | 109B | 400B | 119B |
| Training tokens | 40T | 22T | Not disclosed |
Benchmark Performance Comparison
Performance data from official model cards, Arena AI leaderboard, and independent testing. All scores reflect the largest variant from each family unless noted otherwise.
| Benchmark | Gemma 4 31B | Llama 4 Scout | Mistral Small 4 |
|---|---|---|---|
| Arena AI Text Ranking | #3 open model | Top 10 open | Top 15 open |
| Chatbot Arena ELO | ~1380 | ~1400 | ~1370 |
| LiveCodeBench* | Competitive | Strong | Beats GPT-OSS 120B |
| Instruction Following | Excellent | Good | Very Good |
| Math Reasoning | Strong | Good | Strong |
| Multimodal Vision | Text + Image + Video | Text + Image (native) | Text + Image |
| Configurable Thinking | Yes | No | Yes |
* LiveCodeBench and Arena scores from official reports and community benchmarks (March-April 2026). Rankings shift as new models release.
Efficiency: Output Quality per Active Parameter
When measured by quality per compute dollar, the picture changes dramatically:
| Efficiency Metric | Gemma 4 26B A4B | Llama 4 Scout | Mistral Small 4 |
|---|---|---|---|
| Active params | 3.8B | 17B | 6B |
| AA LCR characters | N/A | N/A | 1.6K (most concise) |
| Latency vs predecessor | New model | N/A | 40% lower |
| Throughput vs predecessor | New model | N/A | 3x higher |
Licensing Deep Dive
Licensing determines what you can build, how you can distribute, and what legal risks you carry. The differences between these three models are substantial:
| License Feature | Gemma 4 | Llama 4 | Mistral Small 4 |
|---|---|---|---|
| License type | Apache 2.0 | Meta Llama License | Apache 2.0 |
| Commercial use | Unrestricted | <700M MAU free | Unrestricted |
| Derivative distribution | Any license | Must keep Meta license | Any license |
| Attribution required | Yes (minimal) | Yes + “Built with Llama” | Yes (minimal) |
| Use in training other models | Allowed | Prohibited | Allowed |
| OSI “open source” compliant | Yes | No | Yes |
Context Windows & Multimodality
Context window size determines how much information a model can process at once. For a deeper dive into context window strategies, see our dedicated comparison guide.
| Capability | Gemma 4 | Llama 4 Scout | Mistral Small 4 |
|---|---|---|---|
| Max context window | 128K (E2B/E4B) / 256K (26B/31B) | 10M tokens | 256K tokens |
| Equivalent pages | 384 / 768 pages | ~30,000 pages | ~768 pages |
| Text input | All variants | Yes | Yes |
| Image input | All variants (variable resolution) | Native multimodal | Yes |
| Video input | E2B and E4B only | Via frames | No |
| Audio input | E2B and E4B only | No | No |
| Tool calling | Native structured | Function calling | Native tool use |
Llama 4 Scout: 10M Context in Practice
While 10M tokens sounds transformative, real-world deployment has constraints. Full 10M context at BF16 requires 200GB+ VRAM. Most production deployments:
- Use quantized versions limiting practical context to 1-2M tokens
- Combine with RAG for efficient retrieval rather than stuffing the full context
- Run on multi-GPU setups (2-4x H100) for production workloads
Deployment & Hardware Requirements
Hardware requirements vary drastically across these model families. Gemma 4 spans from smartphones to servers, while Llama 4 and Mistral Small 4 target cloud and enterprise deployments.
| Model | Min GPU | Recommended | Inference Frameworks |
|---|---|---|---|
| Gemma 4 E2B | Mobile / browser | Any modern device | llama.cpp, MediaPipe, Ollama |
| Gemma 4 E4B | 8GB VRAM | RTX 3060+ | Ollama, vLLM, llama.cpp |
| Gemma 4 31B | 24GB VRAM (Q4) | RTX 4090 / A100 | vLLM, TGI, Ollama |
| Llama 4 Scout | 80GB (H100) | Single H100 | vLLM, TGI, NIM |
| Llama 4 Maverick | Multi-GPU | 4x H100 | vLLM, TGI, NIM |
| Mistral Small 4 | 48GB (Q4) | A100 / H100 | vLLM, llama.cpp, NIM |
Gemma 4's E2B and E4B models are optimized for Arm processors, running natively on mobile devices with optimized performance via MediaPipe and Google AI Edge.
The Per-Layer Embedding (PLE) architecture maximizes parameter efficiency, making these models ideal for offline AI assistants, smart home devices, and privacy-sensitive edge applications.
All three model families are available through major cloud providers. NVIDIA NIM containers provide day-0 support for Mistral Small 4 and Llama 4.
For teams without GPU infrastructure, hosted API access through providers like OpenRouter offers a zero-setup path to production deployment.
API Pricing & Cost Analysis
API pricing varies by provider. These are representative rates from major hosted endpoints as of April 2026. Self-hosted costs depend on your infrastructure.
| Model | Input / 1M | Output / 1M | Self-Host? | Typical Request* |
|---|---|---|---|---|
| Gemma 4 E4B | Free (self-host) | Free (self-host) | Consumer GPU | $0.00 |
| Gemma 4 31B (API) | ~$0.15 | ~$0.60 | Single A100 | ~$0.011 |
| Llama 4 Scout (API) | ~$0.15 | ~$0.60 | Single H100 | ~$0.011 |
| Llama 4 Maverick (API) | ~$0.25 | ~$1.00 | 4x H100 | ~$0.018 |
| Mistral Small 4 (API) | ~$0.10 | ~$0.30 | Single A100 | ~$0.007 |
* Typical request: 50K input tokens, 5K output tokens. API pricing from OpenRouter and provider endpoints (April 2026). Self-host costs exclude infrastructure.
$0 - $110
Gemma 4 (free self-host to API)
$110 - $180
Llama 4 Scout/Maverick API
~$70
Mistral Small 4 API
Best Use Cases for Each Model
Each model family excels in different deployment scenarios. Here is where each shines brightest:
On-Device AI
E2B and E4B run on smartphones, IoT devices, and edge hardware without cloud connectivity.
Privacy-First Applications
Apache 2.0 + local deployment means data never leaves the device.
Multimodal Edge
Video and audio processing on E2B/E4B enables real-time multimedia analysis at the edge.
Research & Academics
True Apache 2.0 allows unrestricted use in research and training derivative models.
Entire Codebase Analysis
10M context ingests massive repositories for comprehensive code understanding.
Document Processing
Legal, medical, and financial document analysis spanning thousands of pages.
RAG at Scale
Retrieval-augmented generation with massive context reduces hallucination risk.
Conversation Memory
Multi-session chatbots that remember months of interaction history.
Coding & Development
Outperforms GPT-OSS 120B on LiveCodeBench with 20% less output.
Agentic Workflows
Configurable reasoning effort lets agents choose speed vs depth per task.
Multilingual Services
Strong European language performance from Mistral AI's French engineering team.
High-Throughput APIs
3x throughput improvement over Mistral Small 3 serves more requests per GPU.
Which Model to Choose
The right model depends on your specific constraints and requirements. Use this decision framework:
Choose Gemma 4 When:
- You need on-device or edge AI with models running on mobile, IoT, or consumer hardware
- True Apache 2.0 is required for derivative model training or redistribution under custom licenses
- Video and audio multimodal processing is needed at the edge (E2B/E4B)
- Privacy mandates require that data never leaves the device or local network
- You want the #3 ranked open model globally for general reasoning tasks (31B)
Choose Llama 4 Scout When:
- Long-context processing (1M-10M tokens) is a core requirement for document or code analysis
- Your application serves fewer than 700M MAU, making Meta's license terms acceptable
- Enterprise RAG systems need massive retrieval context to reduce hallucination
- Natively multimodal (text + image) from a single model is preferred over pipeline approaches
- You have H100 GPU access and need a single-GPU deployment that punches above its weight
Choose Mistral Small 4 When:
- Coding and development tasks are the primary workload, especially with tight latency requirements
- Apache 2.0 + high-performance MoE is needed for cost-efficient enterprise deployment
- Configurable reasoning effort is important for dynamic agent workflows
- Maximum throughput per GPU matters for high-volume production APIs
- European language support or EU data sovereignty compliance is a consideration
Ready to Deploy Open-Source AI?
Whether you choose Gemma 4 for edge deployment, Llama 4 for long-context processing, or Mistral Small 4 for efficient coding, our team can help you integrate the right open-source models into your production stack.
Frequently Asked Questions
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