StableLM 3B Multimodal vs Gopher
Comprehensive side-by-side comparison of pricing, performance benchmarks, and capabilities
At a Glance
Best Overall Performance
Gopher
Higher overall benchmarks
Best for Coding
Gopher
88% coding score
Best for Reasoning
Gopher
90% reasoning score
Best MMLU Score
Gopher
90.5% general knowledge
Compare Different Models
Detailed Comparison
| Feature | StableLM 3B Multimodal | Gopher | Winner |
|---|---|---|---|
| Provider | Stability AI | DeepMind | — |
| Context Window | 16k | 64k | — |
|
MMLU Score
General knowledge & reasoning | 76% | 90.5% | Gopher |
|
Coding Score
Code generation & debugging | 74% | 88% | Gopher |
|
Reasoning Score
Logic & problem-solving | 75% | 90% | Gopher |
| Release Date | 2025 | 2024 | — |
| Vision Support | ✓ Yes | — | — |
| Function Calling | ✓ Yes | ✓ Yes | — |
Performance Comparison
MMLU (General Knowledge)
Difference: 14.5%Coding Performance
Difference: 14.0%Reasoning & Logic
Difference: 15.0%Expert Analysis
Performance Analysis
Gopher outperforms across 3 of 3 benchmarks, with particularly strong coding abilities (88%).
Final Verdict
Our comprehensive recommendation based on all factors
Gopher excels in coding benchmarks, outperforming StableLM 3B Multimodal by 14.0 points—ideal for developers seeking top-tier code generation. Organizations with demanding workloads will benefit from Gopher's capabilities for routine and specialized tasks.
Our Recommendation
Enterprise teams and applications requiring maximum accuracy should choose Gopher for mission-critical deployments where performance is paramount.
Best For These Use Cases
StableLM 3B Multimodal Excels At:
- Multimodal research
- Prototype AI assistants
- Creative content
- Fine-tuning experiments
- Lightweight agents
Gopher Excels At:
- Scientific research assistant
- Academic content generation
- High-level reasoning tasks
- Research document summarization
- Knowledge discovery
Strengths & Weaknesses
StableLM 3B Multimodal
Strengths
- • Open weights
- • Multimodal support
- • Lightweight
- • Creative generation
Considerations
- • Limited context
- • Moderate reasoning
- • Smaller model
- • Not enterprise-ready
Gopher
Strengths
- • High reasoning accuracy
- • Strong academic knowledge
- • Open research integration
- • Scientific domain capabilities
Considerations
- • Limited multimodal support
- • Shorter context
- • Not fully enterprise-ready
- • Closed ecosystem for deployment
Frequently Asked Questions
Which is better: StableLM 3B Multimodal or Gopher?
Gopher 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?
Gopher leads in overall performance with higher benchmark scores, while StableLM 3B Multimodal 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?
Gopher leads in coding performance with a score of 88%, making it 14.0 percentage points better than StableLM 3B Multimodal. This makes Gopher 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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