Mixtral 16x7B vs Cerebras GPT Large
Comprehensive side-by-side comparison of pricing, performance benchmarks, and capabilities
At a Glance
Best Overall Performance
Cerebras GPT Large
Higher overall benchmarks
Best for Coding
Cerebras GPT Large
86.5% coding score
Best for Reasoning
Cerebras GPT Large
87% reasoning score
Best MMLU Score
Cerebras GPT Large
87.5% general knowledge
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Detailed Comparison
| Feature | Mixtral 16x7B | Cerebras GPT Large | Winner |
|---|---|---|---|
| Provider | Mistral AI | Cerebras | — |
| Context Window | 64k | 128k | — |
|
MMLU Score
General knowledge & reasoning | 83.5% | 87.5% | Cerebras GPT Large |
|
Coding Score
Code generation & debugging | 82.5% | 86.5% | Cerebras GPT Large |
|
Reasoning Score
Logic & problem-solving | 83% | 87% | Cerebras GPT Large |
| Release Date | 2025 | 2025 | — |
| Vision Support | ✓ Yes | ✓ Yes | — |
| Function Calling | ✓ Yes | ✓ Yes | — |
Performance Comparison
MMLU (General Knowledge)
Difference: 4.0%Coding Performance
Difference: 4.0%Reasoning & Logic
Difference: 4.0%Expert Analysis
Performance Analysis
Cerebras GPT Large outperforms across 3 of 3 benchmarks, with particularly strong coding abilities (86.5%).
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 Cerebras GPT Large's capabilities for routine and specialized tasks.
Our Recommendation
Choose Cerebras GPT Large 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
Cerebras GPT Large Excels At:
- High-throughput AI research
- Enterprise AI assistants
- Multimodal processing
- Document summarization
- Large-scale chatbots
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
Cerebras GPT Large
Strengths
- • Cluster deployment optimized
- • High throughput
- • Scalable
- • Multimodal support
Considerations
- • Requires Cerebras hardware
- • High infra costs
- • Limited third-party integration
- • Closed ecosystem
Frequently Asked Questions
Which is better: Mixtral 16x7B or Cerebras GPT Large?
Cerebras GPT Large 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?
Cerebras GPT Large 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?
Cerebras GPT Large leads in coding performance with a score of 86.5%, making it 4.0 percentage points better than Mixtral 16x7B. This makes Cerebras GPT Large 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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