Mixtral 16x7B vs Falcon 400B
Comprehensive side-by-side comparison of pricing, performance benchmarks, and capabilities
At a Glance
Best Overall Performance
Falcon 400B
Higher overall benchmarks
Best for Coding
Falcon 400B
89.5% coding score
Best for Reasoning
Falcon 400B
89.7% reasoning score
Best MMLU Score
Falcon 400B
90% general knowledge
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Detailed Comparison
| Feature | Mixtral 16x7B | Falcon 400B | Winner |
|---|---|---|---|
| Provider | Mistral AI | Technology Innovation Institute | — |
| Context Window | 64k | 128k | — |
|
MMLU Score
General knowledge & reasoning | 83.5% | 90% | Falcon 400B |
|
Coding Score
Code generation & debugging | 82.5% | 89.5% | Falcon 400B |
|
Reasoning Score
Logic & problem-solving | 83% | 89.7% | Falcon 400B |
| Release Date | 2025 | 2026 | — |
| Vision Support | ✓ Yes | ✓ Yes | — |
| Function Calling | ✓ Yes | ✓ Yes | — |
Performance Comparison
MMLU (General Knowledge)
Difference: 6.5%Coding Performance
Difference: 7.0%Reasoning & Logic
Difference: 6.7%Expert Analysis
Performance Analysis
Falcon 400B outperforms across 3 of 3 benchmarks, with particularly strong coding abilities (89.5%).
Final Verdict
Our comprehensive recommendation based on all factors
Falcon 400B excels in coding benchmarks, outperforming Mixtral 16x7B by 7.0 points—ideal for developers seeking top-tier code generation. Organizations with demanding workloads will benefit from Falcon 400B's capabilities for routine and specialized tasks.
Our Recommendation
Enterprise teams and applications requiring maximum accuracy should choose Falcon 400B for mission-critical deployments where performance is paramount.
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
Falcon 400B Excels At:
- Large research assistants
- Foundation for custom AI stacks
- Instruction-tuned agents
- Benchmark research
- High-capacity RAG
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
Falcon 400B
Strengths
- • Huge model capacity
- • Strong open research benchmarks
- • Support for instruction variants
- • Large-scale agent foundation
Considerations
- • Extremely high infra costs
- • Complex self-hosting
- • Safety tools community-managed
- • Deployment custom tooling needed
Frequently Asked Questions
Which is better: Mixtral 16x7B or Falcon 400B?
Falcon 400B 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?
Falcon 400B 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?
Falcon 400B leads in coding performance with a score of 89.5%, making it 7.0 percentage points better than Mixtral 16x7B. This makes Falcon 400B 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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