StableLM 14B vs PaLM 2
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 | StableLM 14B | PaLM 2 | Winner |
|---|---|---|---|
| Provider | Stability AI | — | |
| Context Window | 64k | 128k | — |
|
MMLU Score
General knowledge & reasoning | 85% | 86% | PaLM 2 |
|
Coding Score
Code generation & debugging | 84% | 85% | PaLM 2 |
|
Reasoning Score
Logic & problem-solving | 84.8% | 86.5% | PaLM 2 |
| Release Date | 2026 | 2023 | — |
| Vision Support | ✓ Yes | ✓ Yes | — |
| Function Calling | ✓ Yes | ✓ Yes | — |
Performance Comparison
MMLU (General Knowledge)
Difference: 1.0%Coding Performance
Difference: 1.0%Reasoning & Logic
Difference: 1.7%Expert Analysis
Performance Analysis
PaLM 2 outperforms across 1 of 3 benchmarks, with particularly strong reasoning skills (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 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
StableLM 14B Excels At:
- Open research
- Self-hosted assistants
- Content generation
- Fine-tuning experiments
- Creative assistants
PaLM 2 Excels At:
- Code assistants
- Document summarization
- Multilingual translation
- Enterprise chatbots
- Research assistance
Strengths & Weaknesses
StableLM 14B
Strengths
- • Open weights
- • Good reasoning for size
- • Strong community ecosystem
- • Creative output quality
Considerations
- • Moderate vs top hyperscaler models
- • Moderate hallucination control
- • Requires tuning for enterprise safety
- • Less multimodal tooling
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
Frequently Asked Questions
Which is better: StableLM 14B or PaLM 2?
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 StableLM 14B 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 1.0 percentage points better than StableLM 14B. 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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