Comparison
Llama 4 vs GPT-5: Open vs Closed
Comparing Meta's open-weight Llama 4 with OpenAI's GPT-5 for production.
Meta's Llama 4 (April 2025) brought open-weight models to near-frontier performance. GPT-5 (August 2025) remains the closed-source leader. The choice depends on your control, cost, and capability requirements.
Llama 4 Overview
Llama 4 introduced mixture-of-experts and native multimodality:
- Models: Llama 4 Scout, Llama 4 Maverick, Llama 4 Behemoth
- Architecture: Mixture of Experts (MoE), FP8 training
- Multimodal: Native text, image, video, audio
- License: Open-weight (Llama 4 Community License)
GPT-5 Overview
- Context: 400K tokens
- Variants: gpt-5, gpt-5-mini, gpt-5-nano
- Architecture: Unified routing system
- License: Proprietary API access only
When to Choose Llama 4
- Data sovereignty: Self-host for full control
- Cost at scale: No per-token API fees
- Customization: Fine-tune for your domain
- Compliance: Keep data on-premises
When to Choose GPT-5
- Peak capability: Still leads on complex reasoning
- No infrastructure: API-only, no GPUs to manage
- Ecosystem: Broadest third-party integrations
- Rapid iteration: Continuous model updates
Safety Monitoring for Both
Self-hosted or API—both need observability:
- Llama 4 self-hosted still needs hallucination detection
- GPT-5 API outputs still need policy monitoring
- Both can leak PII from context
- Track metrics to compare actual performance
Is Llama 4 as good as GPT-5?
Llama 4 Maverick and Behemoth approach GPT-5 on many benchmarks. For many production use cases, Llama 4 is sufficient. GPT-5 still leads on complex multi-step reasoning. Test both for your specific workload.
Do I need monitoring for self-hosted Llama?
Yes. Self-hosting doesn't eliminate hallucinations, policy violations, or PII leakage. You still need observability to track safety metrics and catch issues before users do.
Monitor any LLM—API or self-hosted
Track hallucinations and safety metrics for Llama, GPT-5, or any provider.
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