GPT Enterprise vs LLaMA 4
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
Compare Different Models
Detailed Comparison
| Feature | GPT Enterprise | LLaMA 4 | Winner |
|---|---|---|---|
| Provider | Microsoft | Meta | — |
| Context Window | 256k | 128k | — |
|
MMLU Score
General knowledge & reasoning | 89% | 85% | GPT Enterprise |
|
Coding Score
Code generation & debugging | 88% | 84% | GPT Enterprise |
|
Reasoning Score
Logic & problem-solving | 88.5% | 85.5% | GPT Enterprise |
| Release Date | 2026 | 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: 3.0%Expert Analysis
Performance Analysis
GPT Enterprise 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 GPT Enterprise for demanding workloads and complex tasks.
Our Recommendation
Choose GPT Enterprise 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
GPT Enterprise Excels At:
- Internal chat assistants
- Knowledge retrieval AI
- Enterprise document summarization
- Secure customer support AI
- Internal research assistants
LLaMA 4 Excels At:
- Self-hosted agents
- Research experiments
- Custom AI assistants
- Offline inference
- Fine-tuning for niche tasks
Strengths & Weaknesses
GPT Enterprise
Strengths
- • Enterprise security
- • High throughput
- • Integration with internal systems
- • Long context reasoning
Considerations
- • High cost
- • Closed-source
- • Complex deployment
- • Requires Azure infrastructure
LLaMA 4
Strengths
- • Open weights
- • High reasoning for size
- • Multimodal support
- • Community-driven
Considerations
- • Shorter context vs GPT-5
- • Resource-intensive
- • Moderate hallucination rate
- • Limited enterprise support
Frequently Asked Questions
Which is better: GPT Enterprise or LLaMA 4?
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 LLaMA 4 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 4.0 percentage points ahead of LLaMA 4. 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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