StableLM 3B Multimodal vs GPT Enterprise

Comprehensive side-by-side comparison of pricing, performance benchmarks, and capabilities

At a Glance

Best Overall Performance

GPT Enterprise

Higher overall benchmarks

Best for Coding

GPT Enterprise

88% coding score

Best for Reasoning

GPT Enterprise

88.5% reasoning score

Best MMLU Score

GPT Enterprise

89% general knowledge

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Detailed Comparison

Feature StableLM 3B Multimodal GPT Enterprise Winner
Provider Stability AI Microsoft
Context Window 16k 256k
MMLU Score

General knowledge & reasoning

76% 89% GPT Enterprise
Coding Score

Code generation & debugging

74% 88% GPT Enterprise
Reasoning Score

Logic & problem-solving

75% 88.5% GPT Enterprise
Release Date 2025 2026
Vision Support ✓ Yes ✓ Yes
Function Calling ✓ Yes ✓ Yes

Performance Comparison

MMLU (General Knowledge)

Difference: 13.0%
StableLM 3B Multimodal 76%
GPT Enterprise 89%

Coding Performance

Difference: 14.0%
StableLM 3B Multimodal 74%
GPT Enterprise 88%

Reasoning & Logic

Difference: 13.5%
StableLM 3B Multimodal 75%
GPT Enterprise 88.5%

Expert Analysis

Performance Analysis

GPT Enterprise outperforms across 3 of 3 benchmarks, with particularly strong coding abilities (88%).

Final Verdict

Our comprehensive recommendation based on all factors

GPT Enterprise 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 GPT Enterprise's capabilities for routine and specialized tasks.

Our Recommendation

Enterprise teams and applications requiring maximum accuracy should choose GPT Enterprise 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

GPT Enterprise Excels At:

  • Internal chat assistants
  • Knowledge retrieval AI
  • Enterprise document summarization
  • Secure customer support AI
  • Internal research assistants

Strengths & Weaknesses

StableLM 3B Multimodal

Strengths

  • Open weights
  • Multimodal support
  • Lightweight
  • Creative generation

Considerations

  • Limited context
  • Moderate reasoning
  • Smaller model
  • Not enterprise-ready
Full StableLM 3B Multimodal Review →

GPT Enterprise

Strengths

  • Enterprise security
  • High throughput
  • Integration with internal systems
  • Long context reasoning

Considerations

  • High cost
  • Closed-source
  • Complex deployment
  • Requires Azure infrastructure
Full GPT Enterprise Review →

Frequently Asked Questions

Which is better: StableLM 3B Multimodal or GPT Enterprise?

GPT Enterprise 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?

GPT Enterprise 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?

GPT Enterprise leads in coding performance with a score of 88%, making it 14.0 percentage points better than StableLM 3B Multimodal. This makes GPT Enterprise 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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