Beginner

Open vs Closed LLM Models

Compare closed-source API models with open-weight alternatives - understand the tradeoffs and choose the right approach for your needs.

Closed-Source Models

Closed-source models are accessible only through APIs. You cannot download the weights, inspect the architecture, or modify the model. The provider manages all infrastructure.

ModelProviderKey Strengths
GPT-4 / GPT-4oOpenAIStrong reasoning, multimodal, extensive tool use
Claude 4 / 3.5 SonnetAnthropicLong context (200K), strong coding, careful safety alignment
Gemini Ultra / ProGoogleMultimodal (native), long context (1M+), Google integration

Open-Weight Models

Open-weight models have publicly downloadable weights. You can run them locally, fine-tune them, and deploy them on your own infrastructure.

ModelProviderSizesLicense
LLaMA 3Meta8B, 70B, 405BLlama 3 Community License
Mistral / MixtralMistral AI7B, 8x7B, 8x22BApache 2.0
Qwen 2.5Alibaba0.5B-72BApache 2.0 (most sizes)
Phi-3Microsoft3.8B, 7B, 14BMIT
Gemma 2Google2B, 9B, 27BGemma license
DeepSeek V3DeepSeek671B (MoE)MIT

Open-Weight vs Open-Source

💡
Important distinction: Most "open-source" LLMs are actually open-weight. They release the trained model weights but not the full training data, training code, or data processing pipeline. True open-source (per OSI definition) would include all of these. Notable exceptions like OLMo from AI2 do release everything.

Licensing

LicenseCommercial UseModificationRestrictions
Apache 2.0YesYesNone (most permissive)
MITYesYesNone
Llama 3 LicenseYes (under 700M MAU)YesMust request license above 700M MAU
Gemma LicenseYesYesUse restrictions (no harm)

Comparison: Open vs Closed

FactorClosed-Source (API)Open-Weight (Self-hosted)
QualityFrontier performanceGood to excellent (70B+ competitive)
CostPer-token pricingFixed infrastructure cost
PrivacyData sent to third partyData stays on your servers
CustomizationLimited (prompt/fine-tune via API)Full (fine-tune, modify, distill)
LatencyNetwork + inference timeInference time only
AvailabilityDepends on provider uptimeYou control availability
Setup effortMinutes (API key)Hours to days (infrastructure)
MaintenanceProvider handles everythingYou manage updates, security, scaling
ComplianceMay conflict with data regulationsFull control over data handling

Pros and Cons

Choose Closed-Source When

  • You need frontier performance immediately
  • Your team lacks ML infrastructure expertise
  • Usage volume is low to moderate
  • You don't have strict data residency requirements
  • You want the latest models without managing updates

Choose Open-Weight When

  • Data privacy is critical (healthcare, finance, legal)
  • You need to fine-tune for your specific domain
  • High volume makes per-token pricing expensive
  • You need full control over model behavior
  • You want to avoid vendor lock-in or API deprecation
✅
Hybrid approach: Many organizations use both. Use frontier API models (GPT-4, Claude) for complex tasks and open models (LLaMA 3, Mistral) for high-volume, simpler tasks. This optimizes both quality and cost.

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