Mixtral 7B 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
Compare Different Models
Detailed Comparison
| Feature | Mixtral 7B | LLaMA 4 | Winner |
|---|---|---|---|
| Provider | Mistral AI | Meta | — |
| Context Window | 32k | 128k | — |
|
MMLU Score
General knowledge & reasoning | 79% | 85% | LLaMA 4 |
|
Coding Score
Code generation & debugging | 78% | 84% | LLaMA 4 |
|
Reasoning Score
Logic & problem-solving | 78.5% | 85.5% | LLaMA 4 |
| Release Date | 2025 | 2025 | — |
| Vision Support | ✓ Yes | ✓ Yes | — |
| Function Calling | ✓ Yes | ✓ Yes | — |
Performance Comparison
MMLU (General Knowledge)
Difference: 6.0%Coding Performance
Difference: 6.0%Reasoning & Logic
Difference: 7.0%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
LLaMA 4 excels in coding benchmarks, outperforming Mixtral 7B by 6.0 points—ideal for developers seeking top-tier code generation. Organizations with demanding workloads will benefit from LLaMA 4's capabilities for routine and specialized tasks.
Our Recommendation
Enterprise teams and applications requiring maximum accuracy should choose LLaMA 4 for mission-critical deployments where performance is paramount.
Best For These Use Cases
Mixtral 7B Excels At:
- Research experiments
- Custom chatbots
- Prototype AI agents
- Educational AI
- Open-source development
LLaMA 4 Excels At:
- Self-hosted agents
- Research experiments
- Custom AI assistants
- Offline inference
- Fine-tuning for niche tasks
Strengths & Weaknesses
Mixtral 7B
Strengths
- • Open weights
- • Efficient inference
- • Fine-tuning friendly
- • Multimodal support
Considerations
- • Small context
- • Moderate reasoning
- • Not enterprise-optimized
- • Limited benchmarks
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 7B 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 7B 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 6.0 percentage points better than Mixtral 7B. 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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