RETRO XL vs StableLM 14B
Comprehensive side-by-side comparison of pricing, performance benchmarks, and capabilities
At a Glance
Best Overall Performance
RETRO XL
Higher overall benchmarks
Best for Coding
RETRO XL
88% coding score
Best for Reasoning
RETRO XL
88.5% reasoning score
Best MMLU Score
RETRO XL
89% general knowledge
Compare Different Models
Detailed Comparison
| Feature | RETRO XL | StableLM 14B | Winner |
|---|---|---|---|
| Provider | DeepMind | Stability AI | — |
| Context Window | 128k | 64k | — |
|
MMLU Score
General knowledge & reasoning | 89% | 85% | RETRO XL |
|
Coding Score
Code generation & debugging | 88% | 84% | RETRO XL |
|
Reasoning Score
Logic & problem-solving | 88.5% | 84.8% | RETRO XL |
| Release Date | 2025 | 2026 | — |
| Vision Support | — | ✓ Yes | — |
| Function Calling | ✓ Yes | ✓ Yes | — |
Performance Comparison
MMLU (General Knowledge)
Difference: 4.0%Coding Performance
Difference: 4.0%Reasoning & Logic
Difference: 3.7%Expert Analysis
Performance Analysis
RETRO XL achieves superior scores across 3 of 3 key benchmarks, including coding (88%), 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 RETRO XL for demanding workloads and complex tasks.
Our Recommendation
Choose RETRO XL 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
RETRO XL Excels At:
- Academic research AI
- Document retrieval augmentation
- Scientific content generation
- Knowledge discovery
- Research assistants
StableLM 14B Excels At:
- Open research
- Self-hosted assistants
- Content generation
- Fine-tuning experiments
- Creative assistants
Strengths & Weaknesses
RETRO XL
Strengths
- • RAG optimized
- • High-quality reasoning
- • Strong academic benchmarks
- • Open research integration
Considerations
- • Closed deployment options
- • Complex self-hosting
- • Limited enterprise tooling
- • High compute
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
Frequently Asked Questions
Which is better: RETRO XL or StableLM 14B?
RETRO XL 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?
RETRO XL 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?
RETRO XL leads in coding performance with a score of 88%, making it 4.0 percentage points ahead of StableLM 14B. This makes RETRO XL 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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