PaLM 2 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
Compare Different Models
Detailed Comparison
| Feature | PaLM 2 | Cerebras GPT Large | Winner |
|---|---|---|---|
| Provider | Cerebras | — | |
| Context Window | 128k | 128k | — |
|
MMLU Score
General knowledge & reasoning | 86% | 87.5% | Cerebras GPT Large |
|
Coding Score
Code generation & debugging | 85% | 86.5% | Cerebras GPT Large |
|
Reasoning Score
Logic & problem-solving | 86.5% | 87% | Cerebras GPT Large |
| Release Date | 2023 | 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: 0.5%Expert Analysis
Performance Analysis
Cerebras GPT Large outperforms across 2 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. The optimal choice between these models depends on your specific use case and performance requirements.
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
PaLM 2 Excels At:
- Code assistants
- Document summarization
- Multilingual translation
- Enterprise chatbots
- Research assistance
Cerebras GPT Large Excels At:
- High-throughput AI research
- Enterprise AI assistants
- Multimodal processing
- Document summarization
- Large-scale chatbots
Strengths & Weaknesses
PaLM 2
Strengths
- • Strong reasoning
- • Excellent multilingual support
- • Coding capabilities
- • Integration with Google Cloud
Considerations
- • Closed weights
- • Costly deployment
- • Limited open-source community
- • Occasional hallucinations
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: PaLM 2 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 PaLM 2 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 1.5 percentage points better than PaLM 2. 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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