PaLM 2 vs METRO LM
Comprehensive side-by-side comparison of pricing, performance benchmarks, and capabilities
At a Glance
Best Overall Performance
PaLM 2
Higher overall benchmarks
Best for Coding
PaLM 2
85% coding score
Best for Reasoning
PaLM 2
86.5% reasoning score
Best MMLU Score
PaLM 2
86% general knowledge
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Detailed Comparison
| Feature | PaLM 2 | METRO LM | Winner |
|---|---|---|---|
| Provider | Meta | — | |
| Context Window | 128k | 64k | — |
|
MMLU Score
General knowledge & reasoning | 86% | 82% | PaLM 2 |
|
Coding Score
Code generation & debugging | 85% | 81% | PaLM 2 |
|
Reasoning Score
Logic & problem-solving | 86.5% | 82.5% | PaLM 2 |
| Release Date | 2023 | 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
PaLM 2 achieves superior scores across 3 of 3 key benchmarks, including coding (85%), demonstrating stronger general capabilities.
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. Enterprise teams requiring maximum accuracy should invest in PaLM 2 for demanding workloads and complex tasks.
Our Recommendation
Choose PaLM 2 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
METRO LM Excels At:
- Content moderation AI
- Social media insights
- Multimodal research
- Prototype AI agents
- Research publications
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
METRO LM
Strengths
- • Multimodal understanding
- • Research-ready
- • Scalable
- • Social media AI integration
Considerations
- • Moderate reasoning
- • Smaller community
- • Closed enterprise integrations
- • Limited benchmarks
Frequently Asked Questions
Which is better: PaLM 2 or METRO LM?
PaLM 2 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?
PaLM 2 leads in overall performance with higher benchmark scores, while METRO LM 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?
PaLM 2 leads in coding performance with a score of 85%, making it 4.0 percentage points ahead of METRO LM. This makes PaLM 2 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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