Mixtral 16x7B vs LLaMA 4
Comprehensive side-by-side comparison of pricing, performance benchmarks, and capabilities
At a Glance
Best Overall Performance
LLaMA 4
Higher overall benchmarks
Best for Coding
LLaMA 4
84% coding score
Best for Reasoning
LLaMA 4
85.5% reasoning score
Best MMLU Score
LLaMA 4
85% general knowledge
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Detailed Comparison
| Feature | Mixtral 16x7B | LLaMA 4 | Winner |
|---|---|---|---|
| Provider | Mistral AI | Meta | — |
| Context Window | 64k | 128k | — |
|
MMLU Score
General knowledge & reasoning | 83.5% | 85% | LLaMA 4 |
|
Coding Score
Code generation & debugging | 82.5% | 84% | LLaMA 4 |
|
Reasoning Score
Logic & problem-solving | 83% | 85.5% | LLaMA 4 |
| Release Date | 2025 | 2025 | — |
| Vision Support | ✓ Yes | ✓ Yes | — |
| Function Calling | ✓ Yes | ✓ Yes | — |
Performance Comparison
MMLU (General Knowledge)
Difference: 1.5%Coding Performance
Difference: 1.5%Reasoning & Logic
Difference: 2.5%Expert Analysis
Performance Analysis
LLaMA 4 outperforms across 3 of 3 benchmarks, with particularly strong coding abilities (84%).
Final Verdict
Our comprehensive recommendation based on all factors
Both models show comparable coding performance, with less than 5 points separating them on benchmark tests. Organizations with demanding workloads will benefit from LLaMA 4's capabilities for routine and specialized tasks.
Our Recommendation
Choose LLaMA 4 for applications where response quality directly impacts business outcomes, or evaluate both models based on your specific use case requirements.
Best For These Use Cases
Mixtral 16x7B Excels At:
- Self-hosted AI agents
- High-throughput inference
- Research experiments
- Domain-specific fine-tuning
- Cost-efficient production
LLaMA 4 Excels At:
- Self-hosted agents
- Research experiments
- Custom AI assistants
- Offline inference
- Fine-tuning for niche tasks
Strengths & Weaknesses
Mixtral 16x7B
Strengths
- • Sparse MoE efficiency
- • Open-weight support
- • High inference throughput
- • Fine-tuning flexibility
Considerations
- • Complex MoE management
- • Limited prebuilt tools
- • Closed multimodal roadmap
- • Requires advanced infra
LLaMA 4
Strengths
- • Open weights
- • High reasoning for size
- • Multimodal support
- • Community-driven
Considerations
- • Shorter context vs GPT-5
- • Resource-intensive
- • Moderate hallucination rate
- • Limited enterprise support
Frequently Asked Questions
Which is better: Mixtral 16x7B or LLaMA 4?
LLaMA 4 offers superior overall performance with higher benchmark scores across MMLU, coding, and reasoning tests. The best choice depends on your specific use case requirements and performance priorities.
What are the key differences?
LLaMA 4 leads in overall performance with higher benchmark scores, while Mixtral 16x7B may offer advantages in specific areas like context window size or specialized capabilities. Both models have their strengths depending on your particular needs.
Which is better for coding?
LLaMA 4 leads in coding performance with a score of 84%, making it 1.5 percentage points better than Mixtral 16x7B. This makes LLaMA 4 the superior choice for software development, code generation, and debugging tasks.
Can I use both models together?
Yes! Many organizations use multiple models strategically: one model for routine tasks where efficiency matters, and another for complex, mission-critical applications requiring maximum accuracy. This hybrid approach optimizes both performance and resource utilization across different use cases.
How often are these benchmarks updated?
We update all benchmark scores and pricing data daily to reflect the latest model versions and API pricing changes. Benchmark scores are sourced from official documentation, independent testing platforms like Artificial Analysis, and peer-reviewed academic evaluations. Last updated: 2/2/2026.
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