Mixtral 16x7B vs Bard Pro
Comprehensive side-by-side comparison of pricing, performance benchmarks, and capabilities
At a Glance
Best Overall Performance
Bard Pro
Higher overall benchmarks
Best for Coding
Bard Pro
85.5% coding score
Best for Reasoning
Bard Pro
87.2% reasoning score
Best MMLU Score
Bard Pro
87% general knowledge
Compare Different Models
Detailed Comparison
| Feature | Mixtral 16x7B | Bard Pro | Winner |
|---|---|---|---|
| Provider | Mistral AI | — | |
| Context Window | 64k | 128k | — |
|
MMLU Score
General knowledge & reasoning | 83.5% | 87% | Bard Pro |
|
Coding Score
Code generation & debugging | 82.5% | 85.5% | Bard Pro |
|
Reasoning Score
Logic & problem-solving | 83% | 87.2% | Bard Pro |
| Release Date | 2025 | 2026 | — |
| Vision Support | ✓ Yes | ✓ Yes | — |
| Function Calling | ✓ Yes | ✓ Yes | — |
Performance Comparison
MMLU (General Knowledge)
Difference: 3.5%Coding Performance
Difference: 3.0%Reasoning & Logic
Difference: 4.2%Expert Analysis
Performance Analysis
Bard Pro outperforms across 3 of 3 benchmarks, with particularly strong coding abilities (85.5%).
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 Bard Pro's capabilities for routine and specialized tasks.
Our Recommendation
Choose Bard Pro 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
Bard Pro Excels At:
- Enterprise chat assistants
- Document Q&A
- Search augmentation
- Research assistance
- Customer support AI
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
Bard Pro
Strengths
- • Dialogue optimization
- • Long-context handling
- • Search integration
- • Enterprise knowledge retrieval
Considerations
- • Premium tier
- • Closed-source
- • Some hallucinations in rare domains
- • Limited third-party plugin ecosystem
Frequently Asked Questions
Which is better: Mixtral 16x7B or Bard Pro?
Bard Pro 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?
Bard Pro 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?
Bard Pro leads in coding performance with a score of 85.5%, making it 3.0 percentage points better than Mixtral 16x7B. This makes Bard Pro 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.
Related Comparisons
Ready to Get Started?
Choose the AI model that best fits your needs and budget
Or compare other models to find your perfect match